From 73d5cacce12b3be97d98405a4c5c0380d3f85493 Mon Sep 17 00:00:00 2001
From: kevinrue
vignettes/iSEEde.Rmd
iSEEde.Rmd
iSEEde
#> To cite package 'iSEEde' in publications use:
#>
#> Rue-Albrecht K (2024). _iSEEde: iSEE extension for panels related to differential
-#> expression analysis_. R package version 1.3.1, <https://github.com/iSEE/iSEEde>.
+#> expression analysis_. R package version 1.5.0, <https://github.com/iSEE/iSEEde>.
#>
#> A BibTeX entry for LaTeX users is
#>
@@ -156,7 +156,7 @@ iSEEde
#> title = {iSEEde: iSEE extension for panels related to differential expression analysis},
#> author = {Kevin Rue-Albrecht},
#> year = {2024},
-#> note = {R package version 1.3.1},
+#> note = {R package version 1.5.0},
#> url = {https://github.com/iSEE/iSEEde},
#> }
@@ -179,7 +179,7 @@ iSEEdelibrary("iSEEde")
library("airway")
library("DESeq2")
-library("iSEE")
+library("iSEE")
# Example data ----
@@ -222,7 +222,7 @@ Quick start to using to iSEEde#> ENSG00000273492 <iSEEDESeq2Results>
#> ENSG00000273493 <iSEEDESeq2Results>
-app <- iSEE(airway, initial = list(
+app <- iSEE(airway, initial = list(
DETable(ContrastName="dex: trt vs untrt", HiddenColumns = c("baseMean",
"lfcSE", "stat"), PanelWidth = 4L),
VolcanoPlot(ContrastName="dex: trt vs untrt", PanelWidth = 4L),
@@ -272,9 +272,9 @@ Reproducibilitylibrary("knitr")
knit("iSEEde.Rmd", tangle = TRUE)
Date the vignette was generated.
-#> [1] "2024-10-16 08:36:07 UTC"
+#> [1] "2024-10-31 10:10:42 UTC"
Wallclock time spent generating the vignette.
-#> Time difference of 22.425 secs
+#> Time difference of 22.597 secs
session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
@@ -287,7 +287,7 @@ Reproducibility#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz UTC
-#> date 2024-10-16
+#> date 2024-10-31
#> pandoc 3.4 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
@@ -296,12 +296,12 @@ Reproducibility#> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0)
#> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0)
#> bibtex 0.5.1 2023-01-26 [1] RSPM (R 4.4.0)
-#> Biobase * 2.65.1 2024-08-28 [1] Bioconductor 3.20 (R 4.4.1)
-#> BiocGenerics * 0.51.3 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1)
+#> Biobase * 2.66.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> BiocGenerics * 0.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> BiocManager 1.30.25 2024-08-28 [2] CRAN (R 4.4.1)
-#> BiocParallel 1.39.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
-#> BiocStyle * 2.33.1 2024-06-12 [1] Bioconductor 3.20 (R 4.4.0)
-#> bookdown 0.40 2024-07-02 [1] RSPM (R 4.4.0)
+#> BiocParallel 1.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> BiocStyle * 2.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> bookdown 0.41 2024-10-16 [1] RSPM (R 4.4.0)
#> bslib 0.8.0 2024-07-29 [2] RSPM (R 4.4.0)
#> cachem 1.1.0 2024-05-16 [2] RSPM (R 4.4.0)
#> circlize 0.4.16 2024-02-20 [1] RSPM (R 4.4.0)
@@ -311,16 +311,16 @@ Reproducibility#> codetools 0.2-20 2024-03-31 [3] CRAN (R 4.4.1)
#> colorspace 2.1-1 2024-07-26 [1] RSPM (R 4.4.0)
#> colourpicker 1.3.0 2023-08-21 [1] RSPM (R 4.4.0)
-#> ComplexHeatmap 2.21.1 2024-09-24 [1] Bioconductor 3.20 (R 4.4.1)
+#> ComplexHeatmap 2.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> crayon 1.5.3 2024-06-20 [2] RSPM (R 4.4.0)
-#> DelayedArray 0.31.14 2024-10-03 [1] Bioconductor 3.20 (R 4.4.1)
+#> DelayedArray 0.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> desc 1.4.3 2023-12-10 [2] RSPM (R 4.4.0)
-#> DESeq2 * 1.45.3 2024-07-24 [1] Bioconductor 3.20 (R 4.4.1)
+#> DESeq2 * 1.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> digest 0.6.37 2024-08-19 [2] RSPM (R 4.4.0)
#> doParallel 1.0.17 2022-02-07 [1] RSPM (R 4.4.0)
#> dplyr 1.1.4 2023-11-17 [1] RSPM (R 4.4.0)
#> DT 0.33 2024-04-04 [1] RSPM (R 4.4.0)
-#> edgeR 4.3.19 2024-10-11 [1] Bioconductor 3.20 (R 4.4.1)
+#> edgeR 4.4.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> evaluate 1.0.1 2024-10-10 [2] RSPM (R 4.4.0)
#> fansi 1.0.6 2023-12-08 [2] RSPM (R 4.4.0)
#> fastmap 1.2.0 2024-05-15 [2] RSPM (R 4.4.0)
@@ -328,24 +328,24 @@ Reproducibility#> foreach 1.5.2 2022-02-02 [1] RSPM (R 4.4.0)
#> fs 1.6.4 2024-04-25 [2] RSPM (R 4.4.0)
#> generics 0.1.3 2022-07-05 [1] RSPM (R 4.4.0)
-#> GenomeInfoDb * 1.41.2 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1)
-#> GenomeInfoDbData 1.2.13 2024-10-15 [1] Bioconductor
-#> GenomicRanges * 1.57.2 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1)
+#> GenomeInfoDb * 1.42.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> GenomeInfoDbData 1.2.13 2024-10-31 [1] Bioconductor
+#> GenomicRanges * 1.58.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> GetoptLong 1.0.5 2020-12-15 [1] RSPM (R 4.4.0)
#> ggplot2 3.5.1 2024-04-23 [1] RSPM (R 4.4.0)
#> ggrepel 0.9.6 2024-09-07 [1] RSPM (R 4.4.0)
#> GlobalOptions 0.1.2 2020-06-10 [1] RSPM (R 4.4.0)
#> glue 1.8.0 2024-09-30 [2] RSPM (R 4.4.0)
-#> gtable 0.3.5 2024-04-22 [1] RSPM (R 4.4.0)
+#> gtable 0.3.6 2024-10-25 [1] RSPM (R 4.4.0)
#> highr 0.11 2024-05-26 [2] RSPM (R 4.4.0)
#> htmltools 0.5.8.1 2024-04-04 [2] RSPM (R 4.4.0)
#> htmlwidgets 1.6.4 2023-12-06 [2] RSPM (R 4.4.0)
#> httpuv 1.6.15 2024-03-26 [2] RSPM (R 4.4.0)
#> httr 1.4.7 2023-08-15 [1] RSPM (R 4.4.0)
-#> igraph 2.0.3 2024-03-13 [1] RSPM (R 4.4.0)
-#> IRanges * 2.39.2 2024-07-17 [1] Bioconductor 3.20 (R 4.4.1)
-#> iSEE * 2.17.4 2024-09-03 [1] Bioconductor 3.20 (R 4.4.1)
-#> iSEEde * 1.3.1 2024-10-16 [1] Bioconductor
+#> igraph 2.1.1 2024-10-19 [1] RSPM (R 4.4.0)
+#> IRanges * 2.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> iSEE * 2.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> iSEEde * 1.5.0 2024-10-31 [1] Bioconductor
#> iterators 1.0.14 2022-02-05 [1] RSPM (R 4.4.0)
#> jquerylib 0.1.4 2021-04-26 [2] RSPM (R 4.4.0)
#> jsonlite 1.8.9 2024-09-20 [2] RSPM (R 4.4.0)
@@ -353,13 +353,13 @@ Reproducibility#> later 1.3.2 2023-12-06 [2] RSPM (R 4.4.0)
#> lattice 0.22-6 2024-03-20 [3] CRAN (R 4.4.1)
#> lifecycle 1.0.4 2023-11-07 [2] RSPM (R 4.4.0)
-#> limma 3.61.12 2024-09-30 [1] Bioconductor 3.20 (R 4.4.1)
+#> limma 3.62.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> listviewer 4.0.0 2023-09-30 [1] RSPM (R 4.4.0)
#> locfit 1.5-9.10 2024-06-24 [1] RSPM (R 4.4.0)
#> lubridate 1.9.3 2023-09-27 [1] RSPM (R 4.4.0)
#> magrittr 2.0.3 2022-03-30 [2] RSPM (R 4.4.0)
-#> Matrix 1.7-0 2024-04-26 [3] CRAN (R 4.4.1)
-#> MatrixGenerics * 1.17.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> Matrix 1.7-1 2024-10-18 [2] RSPM (R 4.4.0)
+#> MatrixGenerics * 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> matrixStats * 1.4.1 2024-09-08 [1] RSPM (R 4.4.0)
#> memoise 2.0.1 2021-11-26 [2] RSPM (R 4.4.0)
#> mgcv 1.9-1 2023-12-21 [3] CRAN (R 4.4.1)
@@ -382,29 +382,29 @@ Reproducibility#> rjson 0.2.23 2024-09-16 [1] RSPM (R 4.4.0)
#> rlang 1.1.4 2024-06-04 [2] RSPM (R 4.4.0)
#> rmarkdown 2.28 2024-08-17 [2] RSPM (R 4.4.0)
-#> S4Arrays 1.5.11 2024-10-14 [1] Bioconductor 3.20 (R 4.4.1)
-#> S4Vectors * 0.43.2 2024-07-17 [1] Bioconductor 3.20 (R 4.4.1)
+#> S4Arrays 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> S4Vectors * 0.44.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> sass 0.4.9 2024-03-15 [2] RSPM (R 4.4.0)
#> scales 1.3.0 2023-11-28 [1] RSPM (R 4.4.0)
#> sessioninfo * 1.2.2 2021-12-06 [2] RSPM (R 4.4.0)
#> shape 1.4.6.1 2024-02-23 [1] RSPM (R 4.4.0)
#> shiny 1.9.1 2024-08-01 [2] RSPM (R 4.4.0)
-#> shinyAce 0.4.2 2022-05-06 [1] RSPM (R 4.4.0)
+#> shinyAce 0.4.3 2024-10-19 [1] RSPM (R 4.4.0)
#> shinydashboard 0.7.2 2021-09-30 [1] RSPM (R 4.4.0)
#> shinyjs 2.1.0 2021-12-23 [1] RSPM (R 4.4.0)
#> shinyWidgets 0.8.7 2024-09-23 [1] RSPM (R 4.4.0)
-#> SingleCellExperiment * 1.27.2 2024-05-24 [1] Bioconductor 3.20 (R 4.4.0)
-#> SparseArray 1.5.44 2024-10-06 [1] Bioconductor 3.20 (R 4.4.1)
+#> SingleCellExperiment * 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> SparseArray 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> statmod 1.5.0 2023-01-06 [1] RSPM (R 4.4.0)
#> stringi 1.8.4 2024-05-06 [2] RSPM (R 4.4.0)
#> stringr 1.5.1 2023-11-14 [2] RSPM (R 4.4.0)
-#> SummarizedExperiment * 1.35.4 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1)
+#> SummarizedExperiment * 1.36.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> systemfonts 1.1.0 2024-05-15 [2] RSPM (R 4.4.0)
#> textshaping 0.4.0 2024-05-24 [2] RSPM (R 4.4.0)
#> tibble 3.2.1 2023-03-20 [2] RSPM (R 4.4.0)
#> tidyselect 1.2.1 2024-03-11 [1] RSPM (R 4.4.0)
#> timechange 0.3.0 2024-01-18 [1] RSPM (R 4.4.0)
-#> UCSC.utils 1.1.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> UCSC.utils 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> utf8 1.2.4 2023-10-22 [2] RSPM (R 4.4.0)
#> vctrs 0.6.5 2023-12-01 [2] RSPM (R 4.4.0)
#> vipor 0.4.7 2023-12-18 [1] RSPM (R 4.4.0)
@@ -412,9 +412,9 @@ Reproducibility#> xfun 0.48 2024-10-03 [2] RSPM (R 4.4.0)
#> xml2 1.3.6 2023-12-04 [2] RSPM (R 4.4.0)
#> xtable 1.8-4 2019-04-21 [2] RSPM (R 4.4.0)
-#> XVector 0.45.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> XVector 0.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> yaml 2.3.10 2024-07-26 [2] RSPM (R 4.4.0)
-#> zlibbioc 1.51.1 2024-06-05 [1] Bioconductor 3.20 (R 4.4.0)
+#> zlibbioc 1.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#>
#> [1] /__w/_temp/Library
#> [2] /usr/local/lib/R/site-library
@@ -447,7 +447,7 @@ Bibliography[3]
A. Oleś. BiocStyle: Standard styles for vignettes and other
-Bioconductor documents. R package version 2.33.1. 2024. DOI:
+Bioconductor documents. R package version 2.34.0. 2024. DOI:
10.18129/B9.bioc.BiocStyle.
URL:
https://bioconductor.org/packages/BiocStyle.
@@ -462,7 +462,7 @@ Bibliography[5]
K. Rue-Albrecht. iSEEde: iSEE extension for panels related to
-differential expression analysis. R package version 1.3.1. 2024.
+differential expression analysis. R package version 1.5.0. 2024.
URL:
https://github.com/iSEE/iSEEde.
diff --git a/articles/index.html b/articles/index.html
index dd55cd6..6e32dfe 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/articles/methods.html b/articles/methods.html
index 49f8ab3..a7f9cd7 100644
--- a/articles/methods.html
+++ b/articles/methods.html
@@ -21,7 +21,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -68,7 +68,7 @@ Kevin
Oxford
kevin.rue-albrecht@imm.ox.ac.uk
- 16 October 2024
+ 31 October 2024
Source: vignettes/methods.Rmd
methods.Rmd
@@ -491,8 +491,8 @@ Live app
-library(iSEE)
-app <- iSEE(airway, initial = list(
+library(iSEE)
+app <- iSEE(airway, initial = list(
DETable(ContrastName="Limma-Voom", HiddenColumns = c("AveExpr",
"t", "B"), PanelWidth = 4L),
VolcanoPlot(ContrastName = "Limma-Voom", PanelWidth = 4L),
@@ -525,8 +525,8 @@ Comparing two contrasts?LogFCLogFCPlot
and highlight the selected features in the
two ?VolcanoPlot
panels.
-library(iSEE)
-app <- iSEE(airway, initial = list(
+library(iSEE)
+app <- iSEE(airway, initial = list(
VolcanoPlot(ContrastName="Limma-Voom",
RowSelectionSource = "LogFCLogFCPlot1", ColorBy = "Row selection",
PanelWidth = 4L),
@@ -585,9 +585,9 @@ Reproducibilitylibrary("knitr")
knit("methods.Rmd", tangle = TRUE)
Date the vignette was generated.
-#> [1] "2024-10-16 08:36:36 UTC"
+#> [1] "2024-10-31 10:11:12 UTC"
Wallclock time spent generating the vignette.
-#> Time difference of 26.619 secs
+#> Time difference of 26.831 secs
R
session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
@@ -599,7 +599,7 @@ Reproducibility#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz UTC
-#> date 2024-10-16
+#> date 2024-10-31
#> pandoc 3.4 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
@@ -608,12 +608,12 @@ Reproducibility#> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0)
#> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0)
#> bibtex 0.5.1 2023-01-26 [1] RSPM (R 4.4.0)
-#> Biobase * 2.65.1 2024-08-28 [1] Bioconductor 3.20 (R 4.4.1)
-#> BiocGenerics * 0.51.3 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1)
+#> Biobase * 2.66.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> BiocGenerics * 0.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> BiocManager 1.30.25 2024-08-28 [2] CRAN (R 4.4.1)
-#> BiocParallel 1.39.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
-#> BiocStyle * 2.33.1 2024-06-12 [1] Bioconductor 3.20 (R 4.4.0)
-#> bookdown 0.40 2024-07-02 [1] RSPM (R 4.4.0)
+#> BiocParallel 1.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> BiocStyle * 2.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> bookdown 0.41 2024-10-16 [1] RSPM (R 4.4.0)
#> bslib 0.8.0 2024-07-29 [2] RSPM (R 4.4.0)
#> cachem 1.1.0 2024-05-16 [2] RSPM (R 4.4.0)
#> circlize 0.4.16 2024-02-20 [1] RSPM (R 4.4.0)
@@ -623,16 +623,16 @@ Reproducibility#> codetools 0.2-20 2024-03-31 [3] CRAN (R 4.4.1)
#> colorspace 2.1-1 2024-07-26 [1] RSPM (R 4.4.0)
#> colourpicker 1.3.0 2023-08-21 [1] RSPM (R 4.4.0)
-#> ComplexHeatmap 2.21.1 2024-09-24 [1] Bioconductor 3.20 (R 4.4.1)
+#> ComplexHeatmap 2.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> crayon 1.5.3 2024-06-20 [2] RSPM (R 4.4.0)
-#> DelayedArray 0.31.14 2024-10-03 [1] Bioconductor 3.20 (R 4.4.1)
+#> DelayedArray 0.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> desc 1.4.3 2023-12-10 [2] RSPM (R 4.4.0)
-#> DESeq2 * 1.45.3 2024-07-24 [1] Bioconductor 3.20 (R 4.4.1)
+#> DESeq2 * 1.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> digest 0.6.37 2024-08-19 [2] RSPM (R 4.4.0)
#> doParallel 1.0.17 2022-02-07 [1] RSPM (R 4.4.0)
#> dplyr 1.1.4 2023-11-17 [1] RSPM (R 4.4.0)
#> DT 0.33 2024-04-04 [1] RSPM (R 4.4.0)
-#> edgeR * 4.3.19 2024-10-11 [1] Bioconductor 3.20 (R 4.4.1)
+#> edgeR * 4.4.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> evaluate 1.0.1 2024-10-10 [2] RSPM (R 4.4.0)
#> fansi 1.0.6 2023-12-08 [2] RSPM (R 4.4.0)
#> fastmap 1.2.0 2024-05-15 [2] RSPM (R 4.4.0)
@@ -640,24 +640,24 @@ Reproducibility#> foreach 1.5.2 2022-02-02 [1] RSPM (R 4.4.0)
#> fs 1.6.4 2024-04-25 [2] RSPM (R 4.4.0)
#> generics 0.1.3 2022-07-05 [1] RSPM (R 4.4.0)
-#> GenomeInfoDb * 1.41.2 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1)
-#> GenomeInfoDbData 1.2.13 2024-10-15 [1] Bioconductor
-#> GenomicRanges * 1.57.2 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1)
+#> GenomeInfoDb * 1.42.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> GenomeInfoDbData 1.2.13 2024-10-31 [1] Bioconductor
+#> GenomicRanges * 1.58.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> GetoptLong 1.0.5 2020-12-15 [1] RSPM (R 4.4.0)
#> ggplot2 3.5.1 2024-04-23 [1] RSPM (R 4.4.0)
#> ggrepel 0.9.6 2024-09-07 [1] RSPM (R 4.4.0)
#> GlobalOptions 0.1.2 2020-06-10 [1] RSPM (R 4.4.0)
#> glue 1.8.0 2024-09-30 [2] RSPM (R 4.4.0)
-#> gtable 0.3.5 2024-04-22 [1] RSPM (R 4.4.0)
+#> gtable 0.3.6 2024-10-25 [1] RSPM (R 4.4.0)
#> highr 0.11 2024-05-26 [2] RSPM (R 4.4.0)
#> htmltools 0.5.8.1 2024-04-04 [2] RSPM (R 4.4.0)
#> htmlwidgets 1.6.4 2023-12-06 [2] RSPM (R 4.4.0)
#> httpuv 1.6.15 2024-03-26 [2] RSPM (R 4.4.0)
#> httr 1.4.7 2023-08-15 [1] RSPM (R 4.4.0)
-#> igraph 2.0.3 2024-03-13 [1] RSPM (R 4.4.0)
-#> IRanges * 2.39.2 2024-07-17 [1] Bioconductor 3.20 (R 4.4.1)
-#> iSEE * 2.17.4 2024-09-03 [1] Bioconductor 3.20 (R 4.4.1)
-#> iSEEde * 1.3.1 2024-10-16 [1] Bioconductor
+#> igraph 2.1.1 2024-10-19 [1] RSPM (R 4.4.0)
+#> IRanges * 2.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> iSEE * 2.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> iSEEde * 1.5.0 2024-10-31 [1] Bioconductor
#> iterators 1.0.14 2022-02-05 [1] RSPM (R 4.4.0)
#> jquerylib 0.1.4 2021-04-26 [2] RSPM (R 4.4.0)
#> jsonlite 1.8.9 2024-09-20 [2] RSPM (R 4.4.0)
@@ -665,13 +665,13 @@ Reproducibility#> later 1.3.2 2023-12-06 [2] RSPM (R 4.4.0)
#> lattice 0.22-6 2024-03-20 [3] CRAN (R 4.4.1)
#> lifecycle 1.0.4 2023-11-07 [2] RSPM (R 4.4.0)
-#> limma * 3.61.12 2024-09-30 [1] Bioconductor 3.20 (R 4.4.1)
+#> limma * 3.62.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> listviewer 4.0.0 2023-09-30 [1] RSPM (R 4.4.0)
#> locfit 1.5-9.10 2024-06-24 [1] RSPM (R 4.4.0)
#> lubridate 1.9.3 2023-09-27 [1] RSPM (R 4.4.0)
#> magrittr 2.0.3 2022-03-30 [2] RSPM (R 4.4.0)
-#> Matrix 1.7-0 2024-04-26 [3] CRAN (R 4.4.1)
-#> MatrixGenerics * 1.17.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> Matrix 1.7-1 2024-10-18 [2] RSPM (R 4.4.0)
+#> MatrixGenerics * 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> matrixStats * 1.4.1 2024-09-08 [1] RSPM (R 4.4.0)
#> memoise 2.0.1 2021-11-26 [2] RSPM (R 4.4.0)
#> mgcv 1.9-1 2023-12-21 [3] CRAN (R 4.4.1)
@@ -694,29 +694,29 @@ Reproducibility#> rjson 0.2.23 2024-09-16 [1] RSPM (R 4.4.0)
#> rlang 1.1.4 2024-06-04 [2] RSPM (R 4.4.0)
#> rmarkdown 2.28 2024-08-17 [2] RSPM (R 4.4.0)
-#> S4Arrays 1.5.11 2024-10-14 [1] Bioconductor 3.20 (R 4.4.1)
-#> S4Vectors * 0.43.2 2024-07-17 [1] Bioconductor 3.20 (R 4.4.1)
+#> S4Arrays 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> S4Vectors * 0.44.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> sass 0.4.9 2024-03-15 [2] RSPM (R 4.4.0)
#> scales 1.3.0 2023-11-28 [1] RSPM (R 4.4.0)
#> sessioninfo * 1.2.2 2021-12-06 [2] RSPM (R 4.4.0)
#> shape 1.4.6.1 2024-02-23 [1] RSPM (R 4.4.0)
#> shiny 1.9.1 2024-08-01 [2] RSPM (R 4.4.0)
-#> shinyAce 0.4.2 2022-05-06 [1] RSPM (R 4.4.0)
+#> shinyAce 0.4.3 2024-10-19 [1] RSPM (R 4.4.0)
#> shinydashboard 0.7.2 2021-09-30 [1] RSPM (R 4.4.0)
#> shinyjs 2.1.0 2021-12-23 [1] RSPM (R 4.4.0)
#> shinyWidgets 0.8.7 2024-09-23 [1] RSPM (R 4.4.0)
-#> SingleCellExperiment * 1.27.2 2024-05-24 [1] Bioconductor 3.20 (R 4.4.0)
-#> SparseArray 1.5.44 2024-10-06 [1] Bioconductor 3.20 (R 4.4.1)
+#> SingleCellExperiment * 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> SparseArray 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> statmod 1.5.0 2023-01-06 [1] RSPM (R 4.4.0)
#> stringi 1.8.4 2024-05-06 [2] RSPM (R 4.4.0)
#> stringr 1.5.1 2023-11-14 [2] RSPM (R 4.4.0)
-#> SummarizedExperiment * 1.35.4 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1)
+#> SummarizedExperiment * 1.36.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> systemfonts 1.1.0 2024-05-15 [2] RSPM (R 4.4.0)
#> textshaping 0.4.0 2024-05-24 [2] RSPM (R 4.4.0)
#> tibble 3.2.1 2023-03-20 [2] RSPM (R 4.4.0)
#> tidyselect 1.2.1 2024-03-11 [1] RSPM (R 4.4.0)
#> timechange 0.3.0 2024-01-18 [1] RSPM (R 4.4.0)
-#> UCSC.utils 1.1.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> UCSC.utils 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> utf8 1.2.4 2023-10-22 [2] RSPM (R 4.4.0)
#> vctrs 0.6.5 2023-12-01 [2] RSPM (R 4.4.0)
#> vipor 0.4.7 2023-12-18 [1] RSPM (R 4.4.0)
@@ -724,9 +724,9 @@ Reproducibility#> xfun 0.48 2024-10-03 [2] RSPM (R 4.4.0)
#> xml2 1.3.6 2023-12-04 [2] RSPM (R 4.4.0)
#> xtable 1.8-4 2019-04-21 [2] RSPM (R 4.4.0)
-#> XVector 0.45.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> XVector 0.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> yaml 2.3.10 2024-07-26 [2] RSPM (R 4.4.0)
-#> zlibbioc 1.51.1 2024-06-05 [1] Bioconductor 3.20 (R 4.4.0)
+#> zlibbioc 1.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#>
#> [1] /__w/_temp/Library
#> [2] /usr/local/lib/R/site-library
@@ -759,7 +759,7 @@ Bibliography[3]
A. Oleś. BiocStyle: Standard styles for vignettes and other
-Bioconductor documents. R package version 2.33.1. 2024. DOI:
+Bioconductor documents. R package version 2.34.0. 2024. DOI:
10.18129/B9.bioc.BiocStyle.
URL:
https://bioconductor.org/packages/BiocStyle.
@@ -774,7 +774,7 @@ Bibliography[5]
K. Rue-Albrecht. iSEEde: iSEE extension for panels related to
-differential expression analysis. R package version 1.3.1. 2024.
+differential expression analysis. R package version 1.5.0. 2024.
URL:
https://github.com/iSEE/iSEEde.
diff --git a/articles/rounding.html b/articles/rounding.html
index e040bc5..446adda 100644
--- a/articles/rounding.html
+++ b/articles/rounding.html
@@ -21,7 +21,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -68,7 +68,7 @@ Kevin
Oxford
kevin.rue-albrecht@imm.ox.ac.uk
- 16 October 2024
+ 31 October 2024
Source: vignettes/rounding.Rmd
rounding.Rmd
@@ -176,16 +176,16 @@ Set a default rounding configurati
other words, numeric values are not rounded, and if users do activate
the rounding functionality, numeric values are rounded to three
significant digits.
-
Those defaults can be changed using the panelDefaults()
+
Those defaults can be changed using the panelDefaults()
function.
-panelDefaults(RoundDigits = TRUE, SignifDigits = 2L)
+panelDefaults(RoundDigits = TRUE, SignifDigits = 2L)
With the default panel settings configured, we use the
DETable()
function to display the contrast results with
rounded numeric values.
-library(iSEE)
-app <- iSEE(airway, initial = list(
+library(iSEE)
+app <- iSEE(airway, initial = list(
DETable(ContrastName="edgeR", HiddenColumns = c("logCPM", "LR"),
PanelWidth = 12L)
))
@@ -207,8 +207,8 @@ Configuring rounding in indiv
to the default value of two significant digits set above, the other
rounding the same values to three significant digits.
-library(iSEE)
-app <- iSEE(airway, initial = list(
+library(iSEE)
+app <- iSEE(airway, initial = list(
DETable(ContrastName="edgeR", HiddenColumns = c("logCPM", "LR"),
PanelWidth = 6L, RoundDigits = TRUE),
DETable(ContrastName="edgeR", HiddenColumns = c("logCPM", "LR"),
@@ -258,9 +258,9 @@ Reproducibilitylibrary("knitr")
knit("rounding.Rmd", tangle = TRUE)
Date the vignette was generated.
-#> [1] "2024-10-16 08:36:50 UTC"
+#> [1] "2024-10-31 10:11:26 UTC"
Wallclock time spent generating the vignette.
-#> Time difference of 11.68 secs
+#> Time difference of 11.579 secs
R
session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
@@ -272,7 +272,7 @@ Reproducibility#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz UTC
-#> date 2024-10-16
+#> date 2024-10-31
#> pandoc 3.4 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
@@ -281,12 +281,12 @@ Reproducibility#> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0)
#> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0)
#> bibtex 0.5.1 2023-01-26 [1] RSPM (R 4.4.0)
-#> Biobase * 2.65.1 2024-08-28 [1] Bioconductor 3.20 (R 4.4.1)
-#> BiocGenerics * 0.51.3 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1)
+#> Biobase * 2.66.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> BiocGenerics * 0.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> BiocManager 1.30.25 2024-08-28 [2] CRAN (R 4.4.1)
-#> BiocParallel 1.39.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
-#> BiocStyle * 2.33.1 2024-06-12 [1] Bioconductor 3.20 (R 4.4.0)
-#> bookdown 0.40 2024-07-02 [1] RSPM (R 4.4.0)
+#> BiocParallel 1.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> BiocStyle * 2.34.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> bookdown 0.41 2024-10-16 [1] RSPM (R 4.4.0)
#> bslib 0.8.0 2024-07-29 [2] RSPM (R 4.4.0)
#> cachem 1.1.0 2024-05-16 [2] RSPM (R 4.4.0)
#> circlize 0.4.16 2024-02-20 [1] RSPM (R 4.4.0)
@@ -296,16 +296,16 @@ Reproducibility#> codetools 0.2-20 2024-03-31 [3] CRAN (R 4.4.1)
#> colorspace 2.1-1 2024-07-26 [1] RSPM (R 4.4.0)
#> colourpicker 1.3.0 2023-08-21 [1] RSPM (R 4.4.0)
-#> ComplexHeatmap 2.21.1 2024-09-24 [1] Bioconductor 3.20 (R 4.4.1)
+#> ComplexHeatmap 2.22.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> crayon 1.5.3 2024-06-20 [2] RSPM (R 4.4.0)
-#> DelayedArray 0.31.14 2024-10-03 [1] Bioconductor 3.20 (R 4.4.1)
+#> DelayedArray 0.32.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> desc 1.4.3 2023-12-10 [2] RSPM (R 4.4.0)
-#> DESeq2 1.45.3 2024-07-24 [1] Bioconductor 3.20 (R 4.4.1)
+#> DESeq2 1.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> digest 0.6.37 2024-08-19 [2] RSPM (R 4.4.0)
#> doParallel 1.0.17 2022-02-07 [1] RSPM (R 4.4.0)
#> dplyr 1.1.4 2023-11-17 [1] RSPM (R 4.4.0)
#> DT 0.33 2024-04-04 [1] RSPM (R 4.4.0)
-#> edgeR * 4.3.19 2024-10-11 [1] Bioconductor 3.20 (R 4.4.1)
+#> edgeR * 4.4.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> evaluate 1.0.1 2024-10-10 [2] RSPM (R 4.4.0)
#> fansi 1.0.6 2023-12-08 [2] RSPM (R 4.4.0)
#> fastmap 1.2.0 2024-05-15 [2] RSPM (R 4.4.0)
@@ -313,24 +313,24 @@ Reproducibility#> foreach 1.5.2 2022-02-02 [1] RSPM (R 4.4.0)
#> fs 1.6.4 2024-04-25 [2] RSPM (R 4.4.0)
#> generics 0.1.3 2022-07-05 [1] RSPM (R 4.4.0)
-#> GenomeInfoDb * 1.41.2 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1)
-#> GenomeInfoDbData 1.2.13 2024-10-15 [1] Bioconductor
-#> GenomicRanges * 1.57.2 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1)
+#> GenomeInfoDb * 1.42.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> GenomeInfoDbData 1.2.13 2024-10-31 [1] Bioconductor
+#> GenomicRanges * 1.58.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> GetoptLong 1.0.5 2020-12-15 [1] RSPM (R 4.4.0)
#> ggplot2 3.5.1 2024-04-23 [1] RSPM (R 4.4.0)
#> ggrepel 0.9.6 2024-09-07 [1] RSPM (R 4.4.0)
#> GlobalOptions 0.1.2 2020-06-10 [1] RSPM (R 4.4.0)
#> glue 1.8.0 2024-09-30 [2] RSPM (R 4.4.0)
-#> gtable 0.3.5 2024-04-22 [1] RSPM (R 4.4.0)
+#> gtable 0.3.6 2024-10-25 [1] RSPM (R 4.4.0)
#> highr 0.11 2024-05-26 [2] RSPM (R 4.4.0)
#> htmltools 0.5.8.1 2024-04-04 [2] RSPM (R 4.4.0)
#> htmlwidgets 1.6.4 2023-12-06 [2] RSPM (R 4.4.0)
#> httpuv 1.6.15 2024-03-26 [2] RSPM (R 4.4.0)
#> httr 1.4.7 2023-08-15 [1] RSPM (R 4.4.0)
-#> igraph 2.0.3 2024-03-13 [1] RSPM (R 4.4.0)
-#> IRanges * 2.39.2 2024-07-17 [1] Bioconductor 3.20 (R 4.4.1)
-#> iSEE * 2.17.4 2024-09-03 [1] Bioconductor 3.20 (R 4.4.1)
-#> iSEEde * 1.3.1 2024-10-16 [1] Bioconductor
+#> igraph 2.1.1 2024-10-19 [1] RSPM (R 4.4.0)
+#> IRanges * 2.40.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> iSEE * 2.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> iSEEde * 1.5.0 2024-10-31 [1] Bioconductor
#> iterators 1.0.14 2022-02-05 [1] RSPM (R 4.4.0)
#> jquerylib 0.1.4 2021-04-26 [2] RSPM (R 4.4.0)
#> jsonlite 1.8.9 2024-09-20 [2] RSPM (R 4.4.0)
@@ -338,13 +338,13 @@ Reproducibility#> later 1.3.2 2023-12-06 [2] RSPM (R 4.4.0)
#> lattice 0.22-6 2024-03-20 [3] CRAN (R 4.4.1)
#> lifecycle 1.0.4 2023-11-07 [2] RSPM (R 4.4.0)
-#> limma * 3.61.12 2024-09-30 [1] Bioconductor 3.20 (R 4.4.1)
+#> limma * 3.62.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> listviewer 4.0.0 2023-09-30 [1] RSPM (R 4.4.0)
#> locfit 1.5-9.10 2024-06-24 [1] RSPM (R 4.4.0)
#> lubridate 1.9.3 2023-09-27 [1] RSPM (R 4.4.0)
#> magrittr 2.0.3 2022-03-30 [2] RSPM (R 4.4.0)
-#> Matrix 1.7-0 2024-04-26 [3] CRAN (R 4.4.1)
-#> MatrixGenerics * 1.17.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> Matrix 1.7-1 2024-10-18 [2] RSPM (R 4.4.0)
+#> MatrixGenerics * 1.18.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> matrixStats * 1.4.1 2024-09-08 [1] RSPM (R 4.4.0)
#> memoise 2.0.1 2021-11-26 [2] RSPM (R 4.4.0)
#> mgcv 1.9-1 2023-12-21 [3] CRAN (R 4.4.1)
@@ -367,29 +367,29 @@ Reproducibility#> rjson 0.2.23 2024-09-16 [1] RSPM (R 4.4.0)
#> rlang 1.1.4 2024-06-04 [2] RSPM (R 4.4.0)
#> rmarkdown 2.28 2024-08-17 [2] RSPM (R 4.4.0)
-#> S4Arrays 1.5.11 2024-10-14 [1] Bioconductor 3.20 (R 4.4.1)
-#> S4Vectors * 0.43.2 2024-07-17 [1] Bioconductor 3.20 (R 4.4.1)
+#> S4Arrays 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> S4Vectors * 0.44.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> sass 0.4.9 2024-03-15 [2] RSPM (R 4.4.0)
#> scales 1.3.0 2023-11-28 [1] RSPM (R 4.4.0)
#> sessioninfo * 1.2.2 2021-12-06 [2] RSPM (R 4.4.0)
#> shape 1.4.6.1 2024-02-23 [1] RSPM (R 4.4.0)
#> shiny 1.9.1 2024-08-01 [2] RSPM (R 4.4.0)
-#> shinyAce 0.4.2 2022-05-06 [1] RSPM (R 4.4.0)
+#> shinyAce 0.4.3 2024-10-19 [1] RSPM (R 4.4.0)
#> shinydashboard 0.7.2 2021-09-30 [1] RSPM (R 4.4.0)
#> shinyjs 2.1.0 2021-12-23 [1] RSPM (R 4.4.0)
#> shinyWidgets 0.8.7 2024-09-23 [1] RSPM (R 4.4.0)
-#> SingleCellExperiment * 1.27.2 2024-05-24 [1] Bioconductor 3.20 (R 4.4.0)
-#> SparseArray 1.5.44 2024-10-06 [1] Bioconductor 3.20 (R 4.4.1)
+#> SingleCellExperiment * 1.28.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
+#> SparseArray 1.6.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> statmod 1.5.0 2023-01-06 [1] RSPM (R 4.4.0)
#> stringi 1.8.4 2024-05-06 [2] RSPM (R 4.4.0)
#> stringr 1.5.1 2023-11-14 [2] RSPM (R 4.4.0)
-#> SummarizedExperiment * 1.35.4 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1)
+#> SummarizedExperiment * 1.36.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> systemfonts 1.1.0 2024-05-15 [2] RSPM (R 4.4.0)
#> textshaping 0.4.0 2024-05-24 [2] RSPM (R 4.4.0)
#> tibble 3.2.1 2023-03-20 [2] RSPM (R 4.4.0)
#> tidyselect 1.2.1 2024-03-11 [1] RSPM (R 4.4.0)
#> timechange 0.3.0 2024-01-18 [1] RSPM (R 4.4.0)
-#> UCSC.utils 1.1.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> UCSC.utils 1.2.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> utf8 1.2.4 2023-10-22 [2] RSPM (R 4.4.0)
#> vctrs 0.6.5 2023-12-01 [2] RSPM (R 4.4.0)
#> vipor 0.4.7 2023-12-18 [1] RSPM (R 4.4.0)
@@ -397,9 +397,9 @@ Reproducibility#> xfun 0.48 2024-10-03 [2] RSPM (R 4.4.0)
#> xml2 1.3.6 2023-12-04 [2] RSPM (R 4.4.0)
#> xtable 1.8-4 2019-04-21 [2] RSPM (R 4.4.0)
-#> XVector 0.45.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0)
+#> XVector 0.46.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#> yaml 2.3.10 2024-07-26 [2] RSPM (R 4.4.0)
-#> zlibbioc 1.51.1 2024-06-05 [1] Bioconductor 3.20 (R 4.4.0)
+#> zlibbioc 1.52.0 2024-10-29 [1] Bioconductor 3.20 (R 4.4.1)
#>
#> [1] /__w/_temp/Library
#> [2] /usr/local/lib/R/site-library
@@ -432,7 +432,7 @@ Bibliography[3]
A. Oleś. BiocStyle: Standard styles for vignettes and other
-Bioconductor documents. R package version 2.33.1. 2024. DOI:
+Bioconductor documents. R package version 2.34.0. 2024. DOI:
10.18129/B9.bioc.BiocStyle.
URL:
https://bioconductor.org/packages/BiocStyle.
@@ -447,7 +447,7 @@ Bibliography[5]
K. Rue-Albrecht. iSEEde: iSEE extension for panels related to
-differential expression analysis. R package version 1.3.1. 2024.
+differential expression analysis. R package version 1.5.0. 2024.
URL:
https://github.com/iSEE/iSEEde.
diff --git a/authors.html b/authors.html
index 0be96a4..6ae713b 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -61,13 +61,13 @@ Citation
Rue-Albrecht K (2024).
iSEEde: iSEE extension for panels related to differential expression analysis.
-R package version 1.3.1, https://github.com/iSEE/iSEEde.
+R package version 1.5.0, https://github.com/iSEE/iSEEde.
@Manual{,
title = {iSEEde: iSEE extension for panels related to differential expression analysis},
author = {Kevin Rue-Albrecht},
year = {2024},
- note = {R package version 1.3.1},
+ note = {R package version 1.5.0},
url = {https://github.com/iSEE/iSEEde},
}
diff --git a/index.html b/index.html
index 69535a0..fcc5814 100644
--- a/index.html
+++ b/index.html
@@ -23,7 +23,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -84,7 +84,7 @@ Example
library("iSEEde")
library("airway")
library("DESeq2")
-library("iSEE")
+library("iSEE")
# Example data ----
@@ -100,7 +100,7 @@ Example
airway <- embedContrastResults(res_deseq2, airway, name = "dex: trt vs untrt")
-app <- iSEE(airway, initial = list(
+app <- iSEE(airway, initial = list(
DETable(ContrastName="dex: trt vs untrt", HiddenColumns = c("baseMean",
"lfcSE", "stat"), PanelWidth = 4L),
VolcanoPlot(ContrastName="dex: trt vs untrt", PanelWidth = 4L),
diff --git a/news/index.html b/news/index.html
index d3c35c8..ec331c0 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/pkgdown.yml b/pkgdown.yml
index 6d5fa01..50115d4 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
iSEEde: iSEEde.html
methods: methods.html
rounding: rounding.html
-last_built: 2024-10-16T08:34Z
+last_built: 2024-10-31T10:09Z
urls:
reference: https://isee.github.io/iSEEde/reference
article: https://isee.github.io/iSEEde/articles
diff --git a/reference/DETable-class.html b/reference/DETable-class.html
index 3c7f678..0481dc6 100644
--- a/reference/DETable-class.html
+++ b/reference/DETable-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -43,7 +43,7 @@ The DETable class
- The DETable class is a RowTable subclass that is dedicated to creating a volcano plot.
+
The DETable class is a RowTable subclass that is dedicated to creating a volcano plot.
It retrieves the table of results for the selected differential expression contrast and creates an interactive table where each row represents a feature.
@@ -55,7 +55,7 @@ Slot overviewRowTable and Table classes.
+
In addition, this class inherits all slots from its parent RowTable and Table classes.
diff --git a/reference/LogFCLogFCPlot-class.html b/reference/LogFCLogFCPlot-class.html
index 12380e0..822dcc8 100644
--- a/reference/LogFCLogFCPlot-class.html
+++ b/reference/LogFCLogFCPlot-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -43,7 +43,7 @@ The LogFCLogFCPlot class
- The LogFCLogFCPlot class is a RowDataPlot subclass that is dedicated to comparing the log-fold-change value of two contrasts.
+
The LogFCLogFCPlot class is a RowDataPlot subclass that is dedicated to comparing the log-fold-change value of two contrasts.
It retrieves the log-fold change of the two selected contrasts and creates a row-based plot where each point represents a feature.
@@ -54,7 +54,7 @@ Slot overviewRowDotPlot, DotPlot, and Panel classes.
+
In addition, this class inherits all slots from its parent RowDotPlot, DotPlot, and Panel classes.
diff --git a/reference/MAPlot-class.html b/reference/MAPlot-class.html
index f6c1487..a90dc91 100644
--- a/reference/MAPlot-class.html
+++ b/reference/MAPlot-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -43,7 +43,7 @@ The MAPlot class
- The MAPlot is a RowDataPlot subclass that is dedicated to creating an MA plot.
+
The MAPlot is a RowDataPlot subclass that is dedicated to creating an MA plot.
It retrieves the log-fold change (M) and mean average (A) values and creates a row-based plot where each point represents a feature.
@@ -53,7 +53,7 @@ Slot overviewRowDotPlot, DotPlot, and Panel classes.
+
In addition, this class inherits all slots from its parent RowDotPlot, DotPlot, and Panel classes.
diff --git a/reference/VolcanoPlot-class.html b/reference/VolcanoPlot-class.html
index 2a09f63..786c2db 100644
--- a/reference/VolcanoPlot-class.html
+++ b/reference/VolcanoPlot-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -43,7 +43,7 @@ The VolcanoPlot class
- The VolcanoPlot is a RowDataPlot subclass that is dedicated to creating a volcano plot.
+
The VolcanoPlot is a RowDataPlot subclass that is dedicated to creating a volcano plot.
It retrieves the log-fold change and p-value from and creates a row-based plot where each point represents a feature.
@@ -53,7 +53,7 @@ Slot overviewRowDotPlot, DotPlot, and Panel classes.
+
In addition, this class inherits all slots from its parent RowDotPlot, DotPlot, and Panel classes.
diff --git a/reference/contrastResults.html b/reference/contrastResults.html
index 8d93007..cfbf0cc 100644
--- a/reference/contrastResults.html
+++ b/reference/contrastResults.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
@@ -80,7 +80,7 @@ Exampleslibrary("iSEEde")
library("airway")
library("DESeq2")
-library("iSEE")
+library("iSEE")
##
# Example data ----
diff --git a/reference/de-generics.html b/reference/de-generics.html
index 32d566b..1786322 100644
--- a/reference/de-generics.html
+++ b/reference/de-generics.html
@@ -7,7 +7,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/reference/iSEEDESeq2Results-class.html b/reference/iSEEDESeq2Results-class.html
index cc326d0..5a5c94f 100644
--- a/reference/iSEEDESeq2Results-class.html
+++ b/reference/iSEEDESeq2Results-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/reference/iSEELimmaResults-class.html b/reference/iSEELimmaResults-class.html
index 59c1973..eb33e8d 100644
--- a/reference/iSEELimmaResults-class.html
+++ b/reference/iSEELimmaResults-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/reference/iSEEde-pkg.html b/reference/iSEEde-pkg.html
index 80413f6..1143d42 100644
--- a/reference/iSEEde-pkg.html
+++ b/reference/iSEEde-pkg.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/reference/iSEEedgeRResults-class.html b/reference/iSEEedgeRResults-class.html
index 69973fe..f309c47 100644
--- a/reference/iSEEedgeRResults-class.html
+++ b/reference/iSEEedgeRResults-class.html
@@ -9,7 +9,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/reference/index.html b/reference/index.html
index fbdd493..47bbf16 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/reference/utils-SummarizedExperiment.html b/reference/utils-SummarizedExperiment.html
index 7038aaf..c32c2c0 100644
--- a/reference/utils-SummarizedExperiment.html
+++ b/reference/utils-SummarizedExperiment.html
@@ -7,7 +7,7 @@
iSEEde
- 1.3.1
+ 1.5.0
diff --git a/search.json b/search.json
index 23a2055..657f489 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://isee.github.io/iSEEde/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to iSEEde","title":"Contributing to iSEEde","text":"outlines propose change iSEEde. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://isee.github.io/iSEEde/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to iSEEde","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://isee.github.io/iSEEde/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to iSEEde","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://isee.github.io/iSEEde/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to iSEEde","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"iSEE/iSEEde\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://isee.github.io/iSEEde/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to iSEEde","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://isee.github.io/iSEEde/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to iSEEde","text":"Please note iSEEde project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://isee.github.io/iSEEde/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with iSEEde","title":"Getting help with iSEEde","text":"Thanks using iSEEde! filing issue, places explore pieces put together make process smooth possible.","code":""},{"path":"https://isee.github.io/iSEEde/SUPPORT.html","id":"make-a-reprex","dir":"","previous_headings":"","what":"Make a reprex","title":"Getting help with iSEEde","text":"Start making minimal reproducible example using reprex package. haven’t heard used reprex , ’re treat! Seriously, reprex make R-question-asking endeavors easier (pretty insane ROI five ten minutes ’ll take learn ’s ). additional reprex pointers, check Get help! section tidyverse site.","code":""},{"path":"https://isee.github.io/iSEEde/SUPPORT.html","id":"where-to-ask","dir":"","previous_headings":"","what":"Where to ask?","title":"Getting help with iSEEde","text":"Armed reprex, next step figure ask. See also Bioconductor help website. ’s question: start community.rstudio.com, /StackOverflow. Bioconductor-related question, please ask Bioconductor Support Website using appropriate package tag (website send automatic email package authors). people answer questions. ’s bug: ’re right place, file issue. ’re sure: let community help figure ! problem bug feature request, can easily return report . opening new issue, sure search issues pull requests make sure bug hasn’t reported /already fixed development version. default, search pre-populated :issue :open. can edit qualifiers (e.g. :pr, :closed) needed. example, ’d simply remove :open search issues repo, open closed.","code":""},{"path":"https://isee.github.io/iSEEde/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with iSEEde","text":"efficient possible, development tidyverse packages tends bursty, shouldn’t worry don’t get immediate response. Typically don’t look repo sufficient quantity issues accumulates, ’s burst intense activity focus efforts. makes development efficient avoids expensive context switching problems, cost taking longer get back . process makes good reprex particularly important might multiple months initial report start working . can’t reproduce bug, can’t fix !","code":""},{"path":"https://isee.github.io/iSEEde/articles/annotations.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Using annotations to facilitate interactive exploration","text":"example, use ?airway data set.","code":"library(\"airway\") data(\"airway\")"},{"path":"https://isee.github.io/iSEEde/articles/annotations.html","id":"annotating-data","dir":"Articles","previous_headings":"","what":"Annotating data","title":"Using annotations to facilitate interactive exploration","text":"section demonstrates one many possible workflows adding annotations data set. annotations meant make easier users identify genes interest, e.g. displaying gene symbols ENSEMBL gene identifiers tooltips interactive browser. First, make copy Ensembl identifiers – currently stored rownames() – column rowData() component. , use org.Hs.eg.db package map Ensembl identifiers gene symbols. store gene symbols additional column rowData() component. Next, use uniquifyFeatureNames() function scuttle package replace rownames() unique identifier generated follows: gene symbol unique. concatenate gene symbol Ensembl gene identifier gene symbol unique. Ensembl identifier gene symbol available.","code":"rowData(airway)[[\"ENSEMBL\"]] <- rownames(airway) library(\"org.Hs.eg.db\") rowData(airway)[[\"SYMBOL\"]] <- mapIds( org.Hs.eg.db, rownames(airway), \"SYMBOL\", \"ENSEMBL\" ) library(\"scuttle\") rownames(airway) <- uniquifyFeatureNames( ID = rowData(airway)[[\"ENSEMBL\"]], names = rowData(airway)[[\"SYMBOL\"]] ) airway #> class: RangedSummarizedExperiment #> dim: 63677 8 #> metadata(1): '' #> assays(1): counts #> rownames(63677): TSPAN6 TNMD ... APP-DT ENSG00000273493 #> rowData names(12): gene_id gene_name ... ENSEMBL SYMBOL #> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521 #> colData names(9): SampleName cell ... Sample BioSample"},{"path":"https://isee.github.io/iSEEde/articles/annotations.html","id":"differential-expression","dir":"Articles","previous_headings":"","what":"Differential expression","title":"Using annotations to facilitate interactive exploration","text":"generate example results, first use edgeR::filterByExpr() remove genes whose counts low support rigorous differential expression analysis. run standard Limma-Voom analysis using edgeR::voomLmFit(), limma::makeContrasts(), limma::eBayes(); alternatively, used limma::treat() instead limma::eBayes(). linear model includes dex cell covariates, indicating treatment conditions cell types, respectively. , interested differences treatments, adjusted cell type, define comparison dextrt - dexuntrt contrast. final differential expression results fetched using limma::topTable(). , embed set differential expression results airway object using embedContrastResults() method use function contrastResults() display contrast results stored airway object.","code":"library(\"edgeR\") design <- model.matrix(~ 0 + dex + cell, data = colData(airway)) keep <- filterByExpr(airway, design) fit <- voomLmFit(airway[keep, ], design, plot = FALSE) contr <- makeContrasts(\"dextrt - dexuntrt\", levels = design) fit <- contrasts.fit(fit, contr) fit <- eBayes(fit) res_limma <- topTable(fit, sort.by = \"P\", n = Inf) head(res_limma) #> gene_id gene_name entrezid gene_biotype gene_seq_start gene_seq_end seq_name #> CACNB2 ENSG00000165995 CACNB2 NA protein_coding 18429606 18830798 10 #> DUSP1 ENSG00000120129 DUSP1 NA protein_coding 172195093 172198198 5 #> PRSS35 ENSG00000146250 PRSS35 NA protein_coding 84222194 84235423 6 #> MAOA ENSG00000189221 MAOA NA protein_coding 43515467 43606068 X #> STEAP2 ENSG00000157214 STEAP2 NA protein_coding 89796904 89867451 7 #> SPARCL1 ENSG00000152583 SPARCL1 NA protein_coding 88394487 88452213 4 #> seq_strand seq_coord_system symbol ENSEMBL SYMBOL logFC AveExpr t #> CACNB2 1 NA CACNB2 ENSG00000165995 CACNB2 3.205606 3.682244 37.68303 #> DUSP1 -1 NA DUSP1 ENSG00000120129 DUSP1 2.864778 6.644455 28.50569 #> PRSS35 1 NA PRSS35 ENSG00000146250 PRSS35 -2.828184 3.224885 -28.10830 #> MAOA 1 NA MAOA ENSG00000189221 MAOA 3.256085 5.950559 27.66135 #> STEAP2 1 NA STEAP2 ENSG00000157214 STEAP2 1.894559 6.790009 27.40834 #> SPARCL1 -1 NA SPARCL1 ENSG00000152583 SPARCL1 4.489009 4.166904 27.34820 #> P.Value adj.P.Val B #> CACNB2 1.115938e-10 1.881472e-06 14.45518 #> DUSP1 1.148539e-09 4.309046e-06 13.01074 #> PRSS35 1.291089e-09 4.309046e-06 12.44376 #> MAOA 1.475547e-09 4.309046e-06 12.75540 #> STEAP2 1.592916e-09 4.309046e-06 12.71210 #> SPARCL1 1.622327e-09 4.309046e-06 12.33402 library(iSEEde) #> Loading required package: iSEE airway <- embedContrastResults(res_limma, airway, name = \"dextrt - dexuntrt\", class = \"limma\" ) contrastResults(airway) #> DataFrame with 63677 rows and 1 column #> dextrt - dexuntrt #> #> TSPAN6 #> TNMD #> DPM1 #> SCYL3 #> FIRRM #> ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> APP-DT #> ENSG00000273493 contrastResults(airway, \"dextrt - dexuntrt\") #> iSEELimmaResults with 63677 rows and 18 columns #> gene_id gene_name entrezid gene_biotype gene_seq_start gene_seq_end #> #> TSPAN6 ENSG00000000003 TSPAN6 NA protein_coding 99883667 99894988 #> TNMD NA NA NA NA NA NA #> DPM1 ENSG00000000419 DPM1 NA protein_coding 49551404 49575092 #> SCYL3 ENSG00000000457 SCYL3 NA protein_coding 169818772 169863408 #> FIRRM ENSG00000000460 C1orf112 NA protein_coding 169631245 169823221 #> ... ... ... ... ... ... ... #> ENSG00000273489 NA NA NA NA NA NA #> ENSG00000273490 NA NA NA NA NA NA #> ENSG00000273491 NA NA NA NA NA NA #> APP-DT NA NA NA NA NA NA #> ENSG00000273493 NA NA NA NA NA NA #> seq_name seq_strand seq_coord_system symbol ENSEMBL SYMBOL #> #> TSPAN6 X -1 NA TSPAN6 ENSG00000000003 TSPAN6 #> TNMD NA NA NA NA NA NA #> DPM1 20 -1 NA DPM1 ENSG00000000419 DPM1 #> SCYL3 1 -1 NA SCYL3 ENSG00000000457 SCYL3 #> FIRRM 1 1 NA C1orf112 ENSG00000000460 FIRRM #> ... ... ... ... ... ... ... #> ENSG00000273489 NA NA NA NA NA NA #> ENSG00000273490 NA NA NA NA NA NA #> ENSG00000273491 NA NA NA NA NA NA #> APP-DT NA NA NA NA NA NA #> ENSG00000273493 NA NA NA NA NA NA #> logFC AveExpr t P.Value adj.P.Val B #> #> TSPAN6 -0.464216 5.02559 -6.589673 0.000136603 0.00171857 0.899306 #> TNMD NA NA NA NA NA NA #> DPM1 0.125077 4.60191 1.632802 0.139297605 0.25279527 -6.274792 #> SCYL3 -0.042107 3.47269 -0.438929 0.671768770 0.77787936 -7.230811 #> FIRRM -0.228517 1.40857 -0.977961 0.355374953 0.49644090 -6.208897 #> ... ... ... ... ... ... ... #> ENSG00000273489 NA NA NA NA NA NA #> ENSG00000273490 NA NA NA NA NA NA #> ENSG00000273491 NA NA NA NA NA NA #> APP-DT NA NA NA NA NA NA #> ENSG00000273493 NA NA NA NA NA NA"},{"path":"https://isee.github.io/iSEEde/articles/annotations.html","id":"live-app","dir":"Articles","previous_headings":"","what":"Live app","title":"Using annotations to facilitate interactive exploration","text":"example, use iSEE::panelDefaults() specify rowData() fields show tooltip displayed hovering data point. application configured display volcano plot MA plot contrast. Finally, configured app launched.","code":"library(iSEE) panelDefaults( TooltipRowData = c(\"SYMBOL\", \"ENSEMBL\") ) app <- iSEE(airway, initial = list( VolcanoPlot(ContrastName = \"dextrt - dexuntrt\", PanelWidth = 6L), MAPlot(ContrastName = \"dextrt - dexuntrt\", PanelWidth = 6L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/articles/annotations.html","id":"reproducibility","dir":"Articles","previous_headings":"","what":"Reproducibility","title":"Using annotations to facilitate interactive exploration","text":"iSEEde package (Rue-Albrecht, 2024) made possible thanks : R (R Core Team, 2024) BiocStyle (Oleś, 2024) knitr (Xie, 2024) RefManageR (McLean, 2017) rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, Iannone, 2024) sessioninfo (Wickham, Chang, Flight, Müller, Hester, 2021) testthat (Wickham, 2011) package developed using biocthis. Code creating vignette Date vignette generated. Wallclock time spent generating vignette. R session information.","code":"## Create the vignette library(\"rmarkdown\") system.time(render(\"annotations.Rmd\", \"BiocStyle::html_document\")) ## Extract the R code library(\"knitr\") knit(\"annotations.Rmd\", tangle = TRUE) #> [1] \"2024-10-16 08:35:39 UTC\" #> Time difference of 14.753 secs #> ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────── #> setting value #> version R version 4.4.1 (2024-06-14) #> os Ubuntu 22.04.5 LTS #> system x86_64, linux-gnu #> ui X11 #> language en #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz UTC #> date 2024-10-16 #> pandoc 3.4 @ /usr/bin/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-8 2024-09-12 [1] RSPM (R 4.4.0) #> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0) #> AnnotationDbi * 1.67.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0) #> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0) #> beachmat 2.21.6 2024-09-05 [1] Bioconductor 3.20 (R 4.4.1) #> bibtex 0.5.1 2023-01-26 [1] RSPM (R 4.4.0) #> Biobase * 2.65.1 2024-08-28 [1] Bioconductor 3.20 (R 4.4.1) #> BiocGenerics * 0.51.3 2024-10-02 [1] Bioconductor 3.20 (R 4.4.1) #> BiocManager 1.30.25 2024-08-28 [2] CRAN (R 4.4.1) #> BiocParallel 1.39.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0) #> BiocStyle * 2.33.1 2024-06-12 [1] Bioconductor 3.20 (R 4.4.0) #> Biostrings 2.73.2 2024-09-26 [1] Bioconductor 3.20 (R 4.4.1) #> bit 4.5.0 2024-09-20 [1] RSPM (R 4.4.0) #> bit64 4.5.2 2024-09-22 [1] RSPM (R 4.4.0) #> blob 1.2.4 2023-03-17 [1] RSPM (R 4.4.0) #> bookdown 0.40 2024-07-02 [1] RSPM (R 4.4.0) #> bslib 0.8.0 2024-07-29 [2] RSPM (R 4.4.0) #> cachem 1.1.0 2024-05-16 [2] RSPM (R 4.4.0) #> circlize 0.4.16 2024-02-20 [1] RSPM (R 4.4.0) #> cli 3.6.3 2024-06-21 [2] RSPM (R 4.4.0) #> clue 0.3-65 2023-09-23 [1] RSPM (R 4.4.0) #> cluster 2.1.6 2023-12-01 [3] CRAN (R 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──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────"},{"path":"https://isee.github.io/iSEEde/articles/annotations.html","id":"bibliography","dir":"Articles","previous_headings":"","what":"Bibliography","title":"Using annotations to facilitate interactive exploration","text":"vignette generated using BiocStyle (Oleś, 2024) knitr (Xie, 2024) rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind scenes. Citations made RefManageR (McLean, 2017). [1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents R. R package version 2.28. 2024. URL: https://github.com/rstudio/rmarkdown. [2] M. W. McLean. “RefManageR: Import Manage BibTeX BibLaTeX References R”. : Journal Open Source Software (2017). DOI: 10.21105/joss.00338. [3] . Oleś. BiocStyle: Standard styles vignettes Bioconductor documents. R package version 2.33.1. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle. [4] R Core Team. R: Language Environment Statistical Computing. R Foundation Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/. [5] K. Rue-Albrecht. iSEEde: iSEE extension panels related differential expression analysis. R package version 1.3.1. 2024. URL: https://github.com/iSEE/iSEEde. [6] H. Wickham. “testthat: Get Started Testing”. : R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf. [7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2, https://r-lib.github.io/sessioninfo/. 2021. URL: https://github.com/r-lib/sessioninfo#readme. [8] Y. Xie. knitr: General-Purpose Package Dynamic Report Generation R. R package version 1.48. 2024. URL: https://yihui.org/knitr/.","code":""},{"path":[]},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"install-iseede","dir":"Articles","previous_headings":"Basics","what":"Install iSEEde","title":"Introduction to iSEEde","text":"open-source statistical environment can easily modified enhance functionality via packages. iSEEde package available via Bioconductor repository packages. can installed operating system CRAN can install iSEEde using following commands session:","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"iSEEde\") ## Check that you have a valid Bioconductor installation BiocManager::valid()"},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"required-knowledge","dir":"Articles","previous_headings":"Basics","what":"Required knowledge","title":"Introduction to iSEEde","text":"iSEEde based many packages particular implemented infrastructure needed dealing omics data interactive visualisation. , packages like SummarizedExperiment, SingleCellExperiment, iSEE shiny. asking question “start using Bioconductor?” might interested blog post.","code":""},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"asking-for-help","dir":"Articles","previous_headings":"Basics","what":"Asking for help","title":"Introduction to iSEEde","text":"package developers, try explain clearly use packages order use functions. Bioconductor steep learning curve critical learn ask help. blog post quoted mentions like highlight Bioconductor support site main resource getting help: remember use iSEEde tag check older posts. alternatives available creating GitHub issues tweeting. However, please note want receive help adhere posting guidelines. particularly critical provide small reproducible example session information package developers can track source error.","code":""},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"citing-iseede","dir":"Articles","previous_headings":"Basics","what":"Citing iSEEde","title":"Introduction to iSEEde","text":"hope iSEEde useful research. Please use following information cite package overall approach. Thank !","code":"## Citation info citation(\"iSEEde\") #> To cite package 'iSEEde' in publications use: #> #> Rue-Albrecht K (2024). _iSEEde: iSEE extension for panels related to differential #> expression analysis_. R package version 1.3.1, . #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {iSEEde: iSEE extension for panels related to differential expression analysis}, #> author = {Kevin Rue-Albrecht}, #> year = {2024}, #> note = {R package version 1.3.1}, #> url = {https://github.com/iSEE/iSEEde}, #> }"},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"quick-start-to-using-to-iseede","dir":"Articles","previous_headings":"","what":"Quick start to using to iSEEde","title":"Introduction to iSEEde","text":"example demonstrates use iSEEde functionality simple differential expression workflow. Specifically, DESeq2 package used perform simple differential expression analysis. , results one contrast – extracted using function DESeq2::results() function – embedded SummarizedExperiment object airway using function iSEEde::embedContrastResults(). ensures differential expression results contrast accessible specialised interactive panels iSEE applications.","code":"library(\"iSEEde\") library(\"airway\") library(\"DESeq2\") library(\"iSEE\") # Example data ---- data(\"airway\") airway$dex <- relevel(airway$dex, \"untrt\") dds <- DESeqDataSet(airway, ~ 0 + dex + cell) dds <- DESeq(dds) res_deseq2 <- results(dds, contrast = list(\"dextrt\", \"dexuntrt\")) head(res_deseq2) #> log2 fold change (MLE): dextrt vs dexuntrt #> Wald test p-value: dextrt vs dexuntrt #> DataFrame with 6 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue padj #> #> ENSG00000000003 708.602170 -0.3812539 0.100654 -3.787752 0.000152016 0.00128292 #> ENSG00000000005 0.000000 NA NA NA NA NA #> ENSG00000000419 520.297901 0.2068127 0.112219 1.842944 0.065337213 0.19646961 #> ENSG00000000457 237.163037 0.0379205 0.143445 0.264356 0.791505314 0.91141884 #> ENSG00000000460 57.932633 -0.0881679 0.287142 -0.307054 0.758802543 0.89500551 #> ENSG00000000938 0.318098 -1.3782416 3.499906 -0.393794 0.693733216 NA # iSEE / iSEEde --- airway <- embedContrastResults(res_deseq2, airway, name = \"dex: trt vs untrt\") contrastResults(airway) #> DataFrame with 63677 rows and 1 column #> dex: trt vs untrt #> #> ENSG00000000003 #> ENSG00000000005 #> ENSG00000000419 #> ENSG00000000457 #> ENSG00000000460 #> ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> ENSG00000273492 #> ENSG00000273493 app <- iSEE(airway, initial = list( DETable(ContrastName=\"dex: trt vs untrt\", HiddenColumns = c(\"baseMean\", \"lfcSE\", \"stat\"), PanelWidth = 4L), VolcanoPlot(ContrastName=\"dex: trt vs untrt\", PanelWidth = 4L), MAPlot(ContrastName=\"dex: trt vs untrt\", PanelWidth = 4L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"reproducibility","dir":"Articles","previous_headings":"","what":"Reproducibility","title":"Introduction to iSEEde","text":"iSEEde package (Rue-Albrecht, 2024) made possible thanks : R (R Core Team, 2024) BiocStyle (Oleś, 2024) knitr (Xie, 2024) RefManageR (McLean, 2017) rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, Iannone, 2024) sessioninfo (Wickham, Chang, Flight, Müller, Hester, 2021) testthat (Wickham, 2011) package developed using biocthis. Code creating vignette Date vignette generated. Wallclock time spent generating vignette. session information.","code":"## Create the vignette library(\"rmarkdown\") system.time(render(\"iSEEde.Rmd\", \"BiocStyle::html_document\")) ## Extract the R code library(\"knitr\") knit(\"iSEEde.Rmd\", tangle = TRUE) #> [1] \"2024-10-16 08:36:07 UTC\" #> Time difference of 22.425 secs #> ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────── #> setting value #> version R version 4.4.1 (2024-06-14) #> os Ubuntu 22.04.5 LTS #> system x86_64, linux-gnu #> ui X11 #> language en #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz UTC #> date 2024-10-16 #> pandoc 3.4 @ /usr/bin/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-8 2024-09-12 [1] RSPM (R 4.4.0) #> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0) #> backports 1.5.0 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2024-05-24 [1] Bioconductor 3.20 (R 4.4.0) #> SparseArray 1.5.44 2024-10-06 [1] Bioconductor 3.20 (R 4.4.1) #> statmod 1.5.0 2023-01-06 [1] RSPM (R 4.4.0) #> stringi 1.8.4 2024-05-06 [2] RSPM (R 4.4.0) #> stringr 1.5.1 2023-11-14 [2] RSPM (R 4.4.0) #> SummarizedExperiment * 1.35.4 2024-10-09 [1] Bioconductor 3.20 (R 4.4.1) #> systemfonts 1.1.0 2024-05-15 [2] RSPM (R 4.4.0) #> textshaping 0.4.0 2024-05-24 [2] RSPM (R 4.4.0) #> tibble 3.2.1 2023-03-20 [2] RSPM (R 4.4.0) #> tidyselect 1.2.1 2024-03-11 [1] RSPM (R 4.4.0) #> timechange 0.3.0 2024-01-18 [1] RSPM (R 4.4.0) #> UCSC.utils 1.1.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0) #> utf8 1.2.4 2023-10-22 [2] RSPM (R 4.4.0) #> vctrs 0.6.5 2023-12-01 [2] RSPM (R 4.4.0) #> vipor 0.4.7 2023-12-18 [1] RSPM (R 4.4.0) #> viridisLite 0.4.2 2023-05-02 [1] RSPM (R 4.4.0) #> xfun 0.48 2024-10-03 [2] RSPM (R 4.4.0) #> xml2 1.3.6 2023-12-04 [2] RSPM (R 4.4.0) #> xtable 1.8-4 2019-04-21 [2] RSPM (R 4.4.0) #> XVector 0.45.0 2024-05-01 [1] Bioconductor 3.20 (R 4.4.0) #> yaml 2.3.10 2024-07-26 [2] RSPM (R 4.4.0) #> zlibbioc 1.51.1 2024-06-05 [1] Bioconductor 3.20 (R 4.4.0) #> #> [1] /__w/_temp/Library #> [2] /usr/local/lib/R/site-library #> [3] /usr/local/lib/R/library #> #> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────"},{"path":"https://isee.github.io/iSEEde/articles/iSEEde.html","id":"bibliography","dir":"Articles","previous_headings":"","what":"Bibliography","title":"Introduction to iSEEde","text":"vignette generated using BiocStyle (Oleś, 2024) knitr (Xie, 2024) rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind scenes. Citations made RefManageR (McLean, 2017). [1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents R. R package version 2.28. 2024. URL: https://github.com/rstudio/rmarkdown. [2] M. W. McLean. “RefManageR: Import Manage BibTeX BibLaTeX References R”. : Journal Open Source Software (2017). DOI: 10.21105/joss.00338. [3] . Oleś. BiocStyle: Standard styles vignettes Bioconductor documents. R package version 2.33.1. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle. [4] R Core Team. R: Language Environment Statistical Computing. R Foundation Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/. [5] K. Rue-Albrecht. iSEEde: iSEE extension panels related differential expression analysis. R package version 1.3.1. 2024. URL: https://github.com/iSEE/iSEEde. [6] H. Wickham. “testthat: Get Started Testing”. : R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf. [7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2, https://r-lib.github.io/sessioninfo/. 2021. URL: https://github.com/r-lib/sessioninfo#readme. [8] Y. Xie. knitr: General-Purpose Package Dynamic Report Generation R. R package version 1.48. 2024. URL: https://yihui.org/knitr/.","code":""},{"path":[]},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"user-facing-storage-and-access","dir":"Articles","previous_headings":"Implementation","what":"User-facing storage and access","title":"Supported differential expression methods","text":"Differential expression results generally reported tables statistics, including (log2) fold-change, p-value, average expression, etc. statistics reported individual features (e.g., genes), rowData() component SummarizedExperiment() objects provides natural home information. Specifically, iSEEde stores retrieves differential expression results rowData(se)[[\"iSEEde\"]]. However, rowData() function used access edit information. Instead, functions embedContrastResults() contrastResults(), used store retrieve contrast results, respectively, functions ensure feature names kept synchronised enclosing SummarizedExperiment object. Moreover, function contrastResultsNames() can used retrieve names contrast available given SummarizedExperiment object.","code":""},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"additional-considerations","dir":"Articles","previous_headings":"Implementation","what":"Additional considerations","title":"Supported differential expression methods","text":"first challenge arises differential expression statistics computed subset features. case, embedContrastResults() populates missing information NA values. second challenge arises different names columns used individual differential expression methods store differential expression common statistics. address , iSEEde provides S4 classes creating common interface supported differential expression methods. class differential expression results implements following methods: pValue() returns vector raw p-values. log2FoldChange() returns vector log2-fold-change values. averageLog2() returns vector average log2-expression values.","code":""},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Supported differential expression methods","text":"example, use ?airway data set. briefly adjust reference level treatment factor untreated condition.","code":"library(\"airway\") data(\"airway\") airway$dex <- relevel(airway$dex, \"untrt\")"},{"path":[]},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"limma","dir":"Articles","previous_headings":"Supported methods","what":"Limma","title":"Supported differential expression methods","text":"first use edgeR::filterByExpr() remove genes whose counts low support rigorous differential expression analysis. run standard Limma-Voom analysis using edgeR::voomLmFit(), limma::makeContrasts(), limma::eBayes(). (Alternatively, used limma::treat() instead limma::eBayes().) linear model includes dex cell covariates, indicating treatment conditions cell types, respectively. , interested differences treatments, adjusted cell type, define comparison dextrt - dexuntrt contrast. final differential expression results fetched using limma::topTable(). , embed set differential expression results airway object using embedContrastResults() method. output limma::topTable() standard data.frame, class= argument must used manually identify method produced results. Supported classes listed object iSEEde::embedContrastResultsMethods, .e. manual method preferable automated heuristic (e.g. using column names data.frame identifying limma::topTable() output). results embedded airway object can accessed using contrastResults() function.","code":"library(\"edgeR\") design <- model.matrix(~ 0 + dex + cell, data = colData(airway)) keep <- filterByExpr(airway, design) fit <- voomLmFit(airway[keep, ], design, plot = FALSE) contr <- makeContrasts(\"dextrt - dexuntrt\", levels = design) fit <- contrasts.fit(fit, contr) fit <- eBayes(fit) res_limma <- topTable(fit, sort.by = \"P\", n = Inf) head(res_limma) #> gene_id gene_name entrezid gene_biotype gene_seq_start gene_seq_end #> ENSG00000165995 ENSG00000165995 CACNB2 NA protein_coding 18429606 18830798 #> ENSG00000120129 ENSG00000120129 DUSP1 NA protein_coding 172195093 172198198 #> ENSG00000146250 ENSG00000146250 PRSS35 NA protein_coding 84222194 84235423 #> ENSG00000189221 ENSG00000189221 MAOA NA protein_coding 43515467 43606068 #> ENSG00000157214 ENSG00000157214 STEAP2 NA protein_coding 89796904 89867451 #> ENSG00000152583 ENSG00000152583 SPARCL1 NA protein_coding 88394487 88452213 #> seq_name seq_strand seq_coord_system symbol logFC AveExpr t #> ENSG00000165995 10 1 NA CACNB2 3.205606 3.682244 37.68303 #> ENSG00000120129 5 -1 NA DUSP1 2.864778 6.644455 28.50569 #> ENSG00000146250 6 1 NA PRSS35 -2.828184 3.224885 -28.10830 #> ENSG00000189221 X 1 NA MAOA 3.256085 5.950559 27.66135 #> ENSG00000157214 7 1 NA STEAP2 1.894559 6.790009 27.40834 #> ENSG00000152583 4 -1 NA SPARCL1 4.489009 4.166904 27.34820 #> P.Value adj.P.Val B #> ENSG00000165995 1.115938e-10 1.881472e-06 14.45518 #> ENSG00000120129 1.148539e-09 4.309046e-06 13.01074 #> ENSG00000146250 1.291089e-09 4.309046e-06 12.44376 #> ENSG00000189221 1.475547e-09 4.309046e-06 12.75540 #> ENSG00000157214 1.592916e-09 4.309046e-06 12.71210 #> ENSG00000152583 1.622327e-09 4.309046e-06 12.33402 library(iSEEde) embedContrastResultsMethods #> limma #> \"iSEELimmaResults\" airway <- embedContrastResults(res_limma, airway, name = \"Limma-Voom\", class = \"limma\") contrastResults(airway) #> DataFrame with 63677 rows and 1 column #> Limma-Voom #> #> ENSG00000000003 #> ENSG00000000005 #> ENSG00000000419 #> ENSG00000000457 #> ENSG00000000460 #> ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> ENSG00000273492 #> ENSG00000273493 contrastResults(airway, \"Limma-Voom\") #> iSEELimmaResults with 63677 rows and 16 columns #> gene_id gene_name entrezid gene_biotype gene_seq_start gene_seq_end #> #> ENSG00000000003 ENSG00000000003 TSPAN6 NA protein_coding 99883667 99894988 #> ENSG00000000005 NA NA NA NA NA NA #> ENSG00000000419 ENSG00000000419 DPM1 NA protein_coding 49551404 49575092 #> ENSG00000000457 ENSG00000000457 SCYL3 NA protein_coding 169818772 169863408 #> ENSG00000000460 ENSG00000000460 C1orf112 NA protein_coding 169631245 169823221 #> ... ... ... ... ... ... ... #> ENSG00000273489 NA NA NA NA NA NA #> ENSG00000273490 NA NA NA NA NA NA #> ENSG00000273491 NA NA NA NA NA NA #> ENSG00000273492 NA NA NA NA NA NA #> ENSG00000273493 NA NA NA NA NA NA #> seq_name seq_strand seq_coord_system symbol logFC AveExpr t #> #> ENSG00000000003 X -1 NA TSPAN6 -0.464216 5.02559 -6.589673 #> ENSG00000000005 NA NA NA NA NA NA NA #> ENSG00000000419 20 -1 NA DPM1 0.125077 4.60191 1.632802 #> ENSG00000000457 1 -1 NA SCYL3 -0.042107 3.47269 -0.438929 #> ENSG00000000460 1 1 NA C1orf112 -0.228517 1.40857 -0.977961 #> ... ... ... ... ... ... ... ... #> ENSG00000273489 NA NA NA NA NA NA NA #> ENSG00000273490 NA NA NA NA NA NA NA #> ENSG00000273491 NA NA NA NA NA NA NA #> ENSG00000273492 NA NA NA NA NA NA NA #> ENSG00000273493 NA NA NA NA NA NA NA #> P.Value adj.P.Val B #> #> ENSG00000000003 0.000136603 0.00171857 0.899306 #> ENSG00000000005 NA NA NA #> ENSG00000000419 0.139297605 0.25279527 -6.274792 #> ENSG00000000457 0.671768770 0.77787936 -7.230811 #> ENSG00000000460 0.355374953 0.49644090 -6.208897 #> ... ... ... ... #> ENSG00000273489 NA NA NA #> ENSG00000273490 NA NA NA #> ENSG00000273491 NA NA NA #> ENSG00000273492 NA NA NA #> ENSG00000273493 NA NA NA"},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"deseq2","dir":"Articles","previous_headings":"Supported methods","what":"DESeq2","title":"Supported differential expression methods","text":"First, use DESeqDataSet() construct DESeqDataSet object analysis. run standard DESeq2 analysis using DESeq(). differential expression results fetched using results(). , embed set differential expression results airway object using embedContrastResults() method. instance, DESeq2 results stored recognisable ?DESeqResults object, can given directly embedContrastResults() method. function automatically pass results object iSEEDESeq2Results() constructor, identify . results embedded airway object can accessed using contrastResults() function.","code":"library(\"DESeq2\") dds <- DESeqDataSet(airway, ~ 0 + dex + cell) dds <- DESeq(dds) res_deseq2 <- results(dds, contrast = list(\"dextrt\", \"dexuntrt\")) head(res_deseq2) #> log2 fold change (MLE): dextrt vs dexuntrt #> Wald test p-value: dextrt vs dexuntrt #> DataFrame with 6 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue padj #> #> ENSG00000000003 708.602170 -0.3812539 0.100654 -3.787752 0.000152016 0.00128292 #> ENSG00000000005 0.000000 NA NA NA NA NA #> ENSG00000000419 520.297901 0.2068127 0.112219 1.842944 0.065337213 0.19646961 #> ENSG00000000457 237.163037 0.0379205 0.143445 0.264356 0.791505314 0.91141884 #> ENSG00000000460 57.932633 -0.0881679 0.287142 -0.307054 0.758802543 0.89500551 #> ENSG00000000938 0.318098 -1.3782416 3.499906 -0.393794 0.693733216 NA airway <- embedContrastResults(res_deseq2, airway, name = \"DESeq2\") contrastResults(airway) #> DataFrame with 63677 rows and 2 columns #> Limma-Voom DESeq2 #> #> ENSG00000000003 #> ENSG00000000005 #> ENSG00000000419 #> ENSG00000000457 #> ENSG00000000460 #> ... ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> ENSG00000273492 #> ENSG00000273493 contrastResults(airway, \"DESeq2\") #> iSEEDESeq2Results with 63677 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue padj #> #> ENSG00000000003 708.6022 -0.3812539 0.100654 -3.787752 0.000152016 0.00128292 #> ENSG00000000005 0.0000 NA NA NA NA NA #> ENSG00000000419 520.2979 0.2068127 0.112219 1.842944 0.065337213 0.19646961 #> ENSG00000000457 237.1630 0.0379205 0.143445 0.264356 0.791505314 0.91141884 #> ENSG00000000460 57.9326 -0.0881679 0.287142 -0.307054 0.758802543 0.89500551 #> ... ... ... ... ... ... ... #> ENSG00000273489 0.275899 1.483744 3.51398 0.422240 0.672850 NA #> ENSG00000273490 0.000000 NA NA NA NA NA #> ENSG00000273491 0.000000 NA NA NA NA NA #> ENSG00000273492 0.105978 -0.463688 3.52312 -0.131613 0.895290 NA #> ENSG00000273493 0.106142 -0.521372 3.53142 -0.147638 0.882628 NA"},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"edger","dir":"Articles","previous_headings":"Supported methods","what":"edgeR","title":"Supported differential expression methods","text":"run standard edgeR analysis using glmFit() glmLRT(). differential expression results fetched using topTags(). , embed set differential expression results airway object using embedContrastResults() method. instance, edgeR results stored recognisable ?TopTags object. , results object can given directly embedContrastResults() method. function automatically pass results object iSEEedgeRResults() constructor, identify . results embedded airway object can accessed using contrastResults() function.","code":"library(\"edgeR\") design <- model.matrix(~ 0 + dex + cell, data = colData(airway)) fit <- glmFit(airway, design, dispersion = 0.1) lrt <- glmLRT(fit, contrast = c(-1, 1, 0, 0, 0)) res_edger <- topTags(lrt, n = Inf) head(res_edger) #> Coefficient: -1*dexuntrt 1*dextrt #> gene_id gene_name entrezid gene_biotype gene_seq_start #> ENSG00000109906 ENSG00000109906 ZBTB16 NA protein_coding 113930315 #> ENSG00000179593 ENSG00000179593 ALOX15B NA protein_coding 7942335 #> ENSG00000127954 ENSG00000127954 STEAP4 NA protein_coding 87905744 #> ENSG00000152583 ENSG00000152583 SPARCL1 NA protein_coding 88394487 #> ENSG00000250978 ENSG00000250978 RP11-357D18.1 NA processed_transcript 66759637 #> ENSG00000163884 ENSG00000163884 KLF15 NA protein_coding 126061478 #> gene_seq_end seq_name seq_strand seq_coord_system symbol #> ENSG00000109906 114121398 11 1 NA ZBTB16 #> ENSG00000179593 7952452 17 1 NA ALOX15B #> ENSG00000127954 87936206 7 -1 NA STEAP4 #> ENSG00000152583 88452213 4 -1 NA SPARCL1 #> ENSG00000250978 66771420 5 -1 NA RP11-357D18.1 #> ENSG00000163884 126076285 3 -1 NA KLF15 #> iSEEde.Limma.Voom.gene_id iSEEde.Limma.Voom.gene_name iSEEde.Limma.Voom.entrezid #> ENSG00000109906 ENSG00000109906 ZBTB16 NA #> ENSG00000179593 ENSG00000179593 ALOX15B NA #> ENSG00000127954 ENSG00000127954 STEAP4 NA #> ENSG00000152583 ENSG00000152583 SPARCL1 NA #> ENSG00000250978 ENSG00000250978 RP11-357D18.1 NA #> ENSG00000163884 ENSG00000163884 KLF15 NA #> iSEEde.Limma.Voom.gene_biotype iSEEde.Limma.Voom.gene_seq_start #> ENSG00000109906 protein_coding 113930315 #> ENSG00000179593 protein_coding 7942335 #> ENSG00000127954 protein_coding 87905744 #> ENSG00000152583 protein_coding 88394487 #> ENSG00000250978 processed_transcript 66759637 #> ENSG00000163884 protein_coding 126061478 #> iSEEde.Limma.Voom.gene_seq_end iSEEde.Limma.Voom.seq_name #> ENSG00000109906 114121398 11 #> ENSG00000179593 7952452 17 #> ENSG00000127954 87936206 7 #> ENSG00000152583 88452213 4 #> ENSG00000250978 66771420 5 #> ENSG00000163884 126076285 3 #> iSEEde.Limma.Voom.seq_strand iSEEde.Limma.Voom.seq_coord_system #> ENSG00000109906 1 NA #> ENSG00000179593 1 NA #> ENSG00000127954 -1 NA #> ENSG00000152583 -1 NA #> ENSG00000250978 -1 NA #> ENSG00000163884 -1 NA #> iSEEde.Limma.Voom.symbol iSEEde.Limma.Voom.logFC iSEEde.Limma.Voom.AveExpr #> ENSG00000109906 ZBTB16 7.069003 1.3807218 #> ENSG00000179593 ALOX15B 7.988755 -1.4798754 #> ENSG00000127954 STEAP4 5.184088 1.6193300 #> ENSG00000152583 SPARCL1 4.489009 4.1669039 #> ENSG00000250978 RP11-357D18.1 6.031081 -0.7521822 #> ENSG00000163884 KLF15 4.433096 3.2401058 #> iSEEde.Limma.Voom.t iSEEde.Limma.Voom.P.Value iSEEde.Limma.Voom.adj.P.Val #> ENSG00000109906 23.94855 4.892822e-09 4.852528e-06 #> ENSG00000179593 21.82342 1.743170e-06 1.076551e-04 #> ENSG00000127954 20.01478 2.162145e-08 1.012605e-05 #> ENSG00000152583 27.34820 1.622327e-09 4.309046e-06 #> ENSG00000250978 16.32481 1.154612e-07 2.187276e-05 #> ENSG00000163884 22.89074 7.117995e-09 5.425717e-06 #> iSEEde.Limma.Voom.B iSEEde.DESeq2.baseMean iSEEde.DESeq2.log2FoldChange #> ENSG00000109906 8.652823 385.07103 7.352629 #> ENSG00000179593 2.986534 67.24305 9.505983 #> ENSG00000127954 9.354944 286.38412 5.207161 #> ENSG00000152583 12.334016 997.43977 4.574919 #> ENSG00000250978 6.074174 56.31819 6.327386 #> ENSG00000163884 10.885003 561.10717 4.459129 #> iSEEde.DESeq2.lfcSE iSEEde.DESeq2.stat iSEEde.DESeq2.pvalue iSEEde.DESeq2.padj #> ENSG00000109906 0.5363902 13.707612 9.141717e-43 2.256376e-40 #> ENSG00000179593 1.0545033 9.014654 1.974926e-19 1.252965e-17 #> ENSG00000127954 0.4930839 10.560396 4.547384e-26 5.057701e-24 #> ENSG00000152583 0.1840561 24.856114 2.220777e-136 4.001395e-132 #> ENSG00000250978 0.6777980 9.335209 1.007922e-20 7.206645e-19 #> ENSG00000163884 0.2348638 18.986019 2.225778e-80 2.506504e-77 #> logFC logCPM LR PValue FDR #> ENSG00000109906 7.183385 4.132638 238.3947 8.805179e-54 5.606874e-49 #> ENSG00000179593 10.015847 1.627629 181.0331 2.883024e-41 9.179116e-37 #> ENSG00000127954 5.087069 3.672567 146.9725 7.957020e-34 1.688931e-29 #> ENSG00000152583 4.498698 5.510213 140.2205 2.382274e-32 3.792402e-28 #> ENSG00000250978 6.128131 1.377260 137.4681 9.526183e-32 1.213198e-27 #> ENSG00000163884 4.367962 4.681216 129.2203 6.069471e-30 6.441428e-26 airway <- embedContrastResults(res_edger, airway, name = \"edgeR\") contrastResults(airway) #> DataFrame with 63677 rows and 3 columns #> Limma-Voom DESeq2 edgeR #> #> ENSG00000000003 #> ENSG00000000005 #> ENSG00000000419 #> ENSG00000000457 #> ENSG00000000460 #> ... ... ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> ENSG00000273492 #> ENSG00000273493 contrastResults(airway, \"edgeR\") #> iSEEedgeRResults with 63677 rows and 5 columns #> logFC logCPM LR PValue FDR #> #> ENSG00000000003 -0.4628153 5.05930 2.018481 0.155394 1 #> ENSG00000000005 0.0000000 -3.45546 0.000000 1.000000 1 #> ENSG00000000419 0.1247724 4.60783 0.146545 0.701860 1 #> ENSG00000000457 -0.0445216 3.48326 0.018241 0.892565 1 #> ENSG00000000460 -0.1618126 1.48518 0.210342 0.646500 1 #> ... ... ... ... ... ... #> ENSG00000273489 2.48209 -3.28549 3.02143 0.082171 1 #> ENSG00000273490 0.00000 -3.45546 0.00000 1.000000 1 #> ENSG00000273491 0.00000 -3.45546 0.00000 1.000000 1 #> ENSG00000273492 -1.24012 -3.36894 0.91097 0.339857 1 #> ENSG00000273493 -1.75243 -3.36862 1.57193 0.209928 1"},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"live-app","dir":"Articles","previous_headings":"","what":"Live app","title":"Supported differential expression methods","text":"example, used iSEEde functions DETable(), VolcanoPlot(), MAPlot() add panels facilitate visualisation differential expression results iSEE app. Specifically, add one set panels differential expression method used vignette (.e., Limma-Voom, DESeq2, edgeR).","code":"library(iSEE) app <- iSEE(airway, initial = list( DETable(ContrastName=\"Limma-Voom\", HiddenColumns = c(\"AveExpr\", \"t\", \"B\"), PanelWidth = 4L), VolcanoPlot(ContrastName = \"Limma-Voom\", PanelWidth = 4L), MAPlot(ContrastName = \"Limma-Voom\", PanelWidth = 4L), DETable(ContrastName=\"DESeq2\", HiddenColumns = c(\"baseMean\", \"lfcSE\", \"stat\"), PanelWidth = 4L), VolcanoPlot(ContrastName = \"DESeq2\", PanelWidth = 4L), MAPlot(ContrastName = \"DESeq2\", PanelWidth = 4L), DETable(ContrastName=\"edgeR\", HiddenColumns = c(\"logCPM\", \"LR\"), PanelWidth = 4L), VolcanoPlot(ContrastName = \"edgeR\", PanelWidth = 4L), MAPlot(ContrastName = \"edgeR\", PanelWidth = 4L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"comparing-two-contrasts","dir":"Articles","previous_headings":"","what":"Comparing two contrasts","title":"Supported differential expression methods","text":"?LogFCLogFCPlot class allows users compare log2 fold-change value features two differential expression contrasts. example, add one ?LogFCLogFCPlot panel comparing contrast using Limma-Voom DESeq2 methods, alongside one ?VolcanoPlot panel two contrasts. Moreover, pre-select area ?LogFCLogFCPlot highlight selected features two ?VolcanoPlot panels.","code":"library(iSEE) app <- iSEE(airway, initial = list( VolcanoPlot(ContrastName=\"Limma-Voom\", RowSelectionSource = \"LogFCLogFCPlot1\", ColorBy = \"Row selection\", PanelWidth = 4L), LogFCLogFCPlot(ContrastNameX=\"Limma-Voom\", ContrastNameY=\"DESeq2\", BrushData = list( xmin = 3.6, xmax = 8.2, ymin = 3.8, ymax = 9.8, mapping = list(x = \"X\", y = \"Y\"), direction = \"xy\", brushId = \"LogFCLogFCPlot1_Brush\", outputId = \"LogFCLogFCPlot1\"), PanelWidth = 4L), VolcanoPlot(ContrastName=\"DESeq2\", RowSelectionSource = \"LogFCLogFCPlot1\", ColorBy = \"Row selection\", PanelWidth = 4L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"reproducibility","dir":"Articles","previous_headings":"","what":"Reproducibility","title":"Supported differential expression methods","text":"iSEEde package (Rue-Albrecht, 2024) made possible thanks : R (R Core Team, 2024) BiocStyle (Oleś, 2024) knitr (Xie, 2024) RefManageR (McLean, 2017) rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, Iannone, 2024) sessioninfo (Wickham, Chang, Flight, Müller, Hester, 2021) testthat (Wickham, 2011) package developed using biocthis. Code creating vignette Date vignette generated. Wallclock time spent generating vignette. R session information.","code":"## Create the vignette library(\"rmarkdown\") system.time(render(\"methods.Rmd\", \"BiocStyle::html_document\")) ## Extract the R code library(\"knitr\") knit(\"methods.Rmd\", tangle = TRUE) #> [1] \"2024-10-16 08:36:36 UTC\" #> Time difference of 26.619 secs #> ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────── #> setting value #> version R version 4.4.1 (2024-06-14) #> os Ubuntu 22.04.5 LTS #> system x86_64, linux-gnu #> ui X11 #> language en #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz UTC #> date 2024-10-16 #> pandoc 3.4 @ /usr/bin/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-8 2024-09-12 [1] RSPM (R 4.4.0) #> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0) #> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0) #> bibtex 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2024-07-26 [2] RSPM (R 4.4.0) #> zlibbioc 1.51.1 2024-06-05 [1] Bioconductor 3.20 (R 4.4.0) #> #> [1] /__w/_temp/Library #> [2] /usr/local/lib/R/site-library #> [3] /usr/local/lib/R/library #> #> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────"},{"path":"https://isee.github.io/iSEEde/articles/methods.html","id":"bibliography","dir":"Articles","previous_headings":"","what":"Bibliography","title":"Supported differential expression methods","text":"vignette generated using BiocStyle r Citep(bib[[\"BiocStyle\"]]) knitr (Xie, 2024) rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind scenes. Citations made RefManageR (McLean, 2017). [1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents R. R package version 2.28. 2024. URL: https://github.com/rstudio/rmarkdown. [2] M. W. McLean. “RefManageR: Import Manage BibTeX BibLaTeX References R”. : Journal Open Source Software (2017). DOI: 10.21105/joss.00338. [3] . Oleś. BiocStyle: Standard styles vignettes Bioconductor documents. R package version 2.33.1. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle. [4] R Core Team. R: Language Environment Statistical Computing. R Foundation Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/. [5] K. Rue-Albrecht. iSEEde: iSEE extension panels related differential expression analysis. R package version 1.3.1. 2024. URL: https://github.com/iSEE/iSEEde. [6] H. Wickham. “testthat: Get Started Testing”. : R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf. [7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2, https://r-lib.github.io/sessioninfo/. 2021. URL: https://github.com/r-lib/sessioninfo#readme. [8] Y. Xie. knitr: General-Purpose Package Dynamic Report Generation R. R package version 1.48. 2024. URL: https://yihui.org/knitr/.","code":""},{"path":"https://isee.github.io/iSEEde/articles/rounding.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Rounding numeric values","text":"example, use ?airway data set. briefly adjust reference level treatment factor untreated condition.","code":"library(\"airway\") data(\"airway\") airway$dex <- relevel(airway$dex, \"untrt\")"},{"path":"https://isee.github.io/iSEEde/articles/rounding.html","id":"differential-expression","dir":"Articles","previous_headings":"","what":"Differential expression","title":"Rounding numeric values","text":"generate example results, run standard edgeR analysis using glmFit() glmLRT(). differential expression results fetched using topTags(). , embed set differential expression results airway object using embedContrastResults() method. results embedded airway object can accessed using contrastResults() function.","code":"library(\"edgeR\") design <- model.matrix(~ 0 + dex + cell, data = colData(airway)) fit <- glmFit(airway, design, dispersion = 0.1) lrt <- glmLRT(fit, contrast = c(-1, 1, 0, 0, 0)) res_edger <- topTags(lrt, n = Inf) head(res_edger) #> Coefficient: -1*dexuntrt 1*dextrt #> gene_id gene_name entrezid gene_biotype gene_seq_start #> ENSG00000109906 ENSG00000109906 ZBTB16 NA protein_coding 113930315 #> ENSG00000179593 ENSG00000179593 ALOX15B NA protein_coding 7942335 #> ENSG00000127954 ENSG00000127954 STEAP4 NA protein_coding 87905744 #> ENSG00000152583 ENSG00000152583 SPARCL1 NA protein_coding 88394487 #> ENSG00000250978 ENSG00000250978 RP11-357D18.1 NA processed_transcript 66759637 #> ENSG00000163884 ENSG00000163884 KLF15 NA protein_coding 126061478 #> gene_seq_end seq_name seq_strand seq_coord_system symbol logFC logCPM #> ENSG00000109906 114121398 11 1 NA ZBTB16 7.183385 4.132638 #> ENSG00000179593 7952452 17 1 NA ALOX15B 10.015847 1.627629 #> ENSG00000127954 87936206 7 -1 NA STEAP4 5.087069 3.672567 #> ENSG00000152583 88452213 4 -1 NA SPARCL1 4.498698 5.510213 #> ENSG00000250978 66771420 5 -1 NA RP11-357D18.1 6.128131 1.377260 #> ENSG00000163884 126076285 3 -1 NA KLF15 4.367962 4.681216 #> LR PValue FDR #> ENSG00000109906 238.3947 8.805179e-54 5.606874e-49 #> ENSG00000179593 181.0331 2.883024e-41 9.179116e-37 #> ENSG00000127954 146.9725 7.957020e-34 1.688931e-29 #> ENSG00000152583 140.2205 2.382274e-32 3.792402e-28 #> ENSG00000250978 137.4681 9.526183e-32 1.213198e-27 #> ENSG00000163884 129.2203 6.069471e-30 6.441428e-26 library(iSEEde) airway <- embedContrastResults(res_edger, airway, name = \"edgeR\") contrastResults(airway) #> DataFrame with 63677 rows and 1 column #> edgeR #> #> ENSG00000000003 #> ENSG00000000005 #> ENSG00000000419 #> ENSG00000000457 #> ENSG00000000460 #> ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> ENSG00000273492 #> ENSG00000273493 contrastResults(airway, \"edgeR\") #> iSEEedgeRResults with 63677 rows and 5 columns #> logFC logCPM LR PValue FDR #> #> ENSG00000000003 -0.4628153 5.05930 2.018481 0.155394 1 #> ENSG00000000005 0.0000000 -3.45546 0.000000 1.000000 1 #> ENSG00000000419 0.1247724 4.60783 0.146545 0.701860 1 #> ENSG00000000457 -0.0445216 3.48326 0.018241 0.892565 1 #> ENSG00000000460 -0.1618126 1.48518 0.210342 0.646500 1 #> ... ... ... ... ... ... #> ENSG00000273489 2.48209 -3.28549 3.02143 0.082171 1 #> ENSG00000273490 0.00000 -3.45546 0.00000 1.000000 1 #> ENSG00000273491 0.00000 -3.45546 0.00000 1.000000 1 #> ENSG00000273492 -1.24012 -3.36894 0.91097 0.339857 1 #> ENSG00000273493 -1.75243 -3.36862 1.57193 0.209928 1"},{"path":"https://isee.github.io/iSEEde/articles/rounding.html","id":"set-a-default-rounding-configuration","dir":"Articles","previous_headings":"","what":"Set a default rounding configuration","title":"Rounding numeric values","text":"Differential expression methods generally return precise numeric values several digits decimal point. level precision can unnecessarily overwhelming users may wish round numeric values limited number significant digits. builtin default configuration rounding iSEEde RoundDigit = FALSE SignifDigits = 3. words, numeric values rounded, users activate rounding functionality, numeric values rounded three significant digits. defaults can changed using panelDefaults() function. default panel settings configured, use DETable() function display contrast results rounded numeric values.","code":"panelDefaults(RoundDigits = TRUE, SignifDigits = 2L) library(iSEE) app <- iSEE(airway, initial = list( DETable(ContrastName=\"edgeR\", HiddenColumns = c(\"logCPM\", \"LR\"), PanelWidth = 12L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/articles/rounding.html","id":"configuring-rounding-in-individual-panels","dir":"Articles","previous_headings":"","what":"Configuring rounding in individual panels","title":"Rounding numeric values","text":"default rounding configuration can overridden individual panel configurations. slots RoundDigits SignifDigits can set directly individual calls DETable() constructor function. example , add two tables, one rounding numeric values default value two significant digits set , rounding values three significant digits.","code":"library(iSEE) app <- iSEE(airway, initial = list( DETable(ContrastName=\"edgeR\", HiddenColumns = c(\"logCPM\", \"LR\"), PanelWidth = 6L, RoundDigits = TRUE), DETable(ContrastName=\"edgeR\", HiddenColumns = c(\"logCPM\", \"LR\"), PanelWidth = 6L, RoundDigits = TRUE, SignifDigits = 3L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/articles/rounding.html","id":"reproducibility","dir":"Articles","previous_headings":"","what":"Reproducibility","title":"Rounding numeric values","text":"iSEEde package (Rue-Albrecht, 2024) made possible thanks : R (R Core Team, 2024) BiocStyle (Oleś, 2024) knitr (Xie, 2024) RefManageR (McLean, 2017) rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, Iannone, 2024) sessioninfo (Wickham, Chang, Flight, Müller, Hester, 2021) testthat (Wickham, 2011) package developed using biocthis. Code creating vignette Date vignette generated. Wallclock time spent generating vignette. R session information.","code":"## Create the vignette library(\"rmarkdown\") system.time(render(\"rounding.Rmd\", \"BiocStyle::html_document\")) ## Extract the R code library(\"knitr\") knit(\"rounding.Rmd\", tangle = TRUE) #> [1] \"2024-10-16 08:36:50 UTC\" #> Time difference of 11.68 secs #> ─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────── #> setting value #> version R version 4.4.1 (2024-06-14) #> os Ubuntu 22.04.5 LTS #> system x86_64, linux-gnu #> ui X11 #> language en #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz UTC #> date 2024-10-16 #> pandoc 3.4 @ /usr/bin/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-8 2024-09-12 [1] RSPM (R 4.4.0) #> airway * 1.25.0 2024-05-02 [1] Bioconductor 3.20 (R 4.4.0) #> backports 1.5.0 2024-05-23 [1] RSPM (R 4.4.0) #> bibtex 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RSPM (R 4.4.0) #> zlibbioc 1.51.1 2024-06-05 [1] Bioconductor 3.20 (R 4.4.0) #> #> [1] /__w/_temp/Library #> [2] /usr/local/lib/R/site-library #> [3] /usr/local/lib/R/library #> #> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────"},{"path":"https://isee.github.io/iSEEde/articles/rounding.html","id":"bibliography","dir":"Articles","previous_headings":"","what":"Bibliography","title":"Rounding numeric values","text":"vignette generated using BiocStyle r Citep(bib[[\"BiocStyle\"]]) knitr (Xie, 2024) rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind scenes. Citations made RefManageR (McLean, 2017). [1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents R. R package version 2.28. 2024. URL: https://github.com/rstudio/rmarkdown. [2] M. W. McLean. “RefManageR: Import Manage BibTeX BibLaTeX References R”. : Journal Open Source Software (2017). DOI: 10.21105/joss.00338. [3] . Oleś. BiocStyle: Standard styles vignettes Bioconductor documents. R package version 2.33.1. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle. [4] R Core Team. R: Language Environment Statistical Computing. R Foundation Statistical Computing. Vienna, Austria, 2024. URL: https://www.R-project.org/. [5] K. Rue-Albrecht. iSEEde: iSEE extension panels related differential expression analysis. R package version 1.3.1. 2024. URL: https://github.com/iSEE/iSEEde. [6] H. Wickham. “testthat: Get Started Testing”. : R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf. [7] H. Wickham, W. Chang, R. Flight, et al. sessioninfo: R Session Information. R package version 1.2.2, https://r-lib.github.io/sessioninfo/. 2021. URL: https://github.com/r-lib/sessioninfo#readme. [8] Y. Xie. knitr: General-Purpose Package Dynamic Report Generation R. R package version 1.48. 2024. URL: https://yihui.org/knitr/.","code":""},{"path":"https://isee.github.io/iSEEde/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Kevin Rue-Albrecht. Author, maintainer. Thomas Sandmann. Contributor. Denali Therapeutics. Funder.","code":""},{"path":"https://isee.github.io/iSEEde/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Rue-Albrecht K (2024). iSEEde: iSEE extension panels related differential expression analysis. R package version 1.3.1, https://github.com/iSEE/iSEEde.","code":"@Manual{, title = {iSEEde: iSEE extension for panels related to differential expression analysis}, author = {Kevin Rue-Albrecht}, year = {2024}, note = {R package version 1.3.1}, url = {https://github.com/iSEE/iSEEde}, }"},{"path":"https://isee.github.io/iSEEde/index.html","id":"iseede","dir":"","previous_headings":"","what":"iSEE extension for panels related to differential expression analysis","title":"iSEE extension for panels related to differential expression analysis","text":"goal iSEEde provide panels facilitate interactive visualisation differential expression results iSEE applications.","code":""},{"path":"https://isee.github.io/iSEEde/index.html","id":"installation-instructions","dir":"","previous_headings":"","what":"Installation instructions","title":"iSEE extension for panels related to differential expression analysis","text":"Get latest stable R release CRAN. install iSEEde Bioconductor using following code: development version GitHub :","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"iSEEde\") BiocManager::install(\"iSEE/iSEEde\")"},{"path":"https://isee.github.io/iSEEde/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"iSEE extension for panels related to differential expression analysis","text":"basic example shows load package:","code":"library(\"iSEEde\") library(\"airway\") library(\"DESeq2\") library(\"iSEE\") # Example data ---- data(\"airway\") airway$dex <- relevel(airway$dex, \"untrt\") dds <- DESeqDataSet(airway, ~ 0 + dex + cell) dds <- DESeq(dds) res_deseq2 <- results(dds, contrast = list(\"dextrt\", \"dexuntrt\")) # iSEE / iSEEde --- airway <- embedContrastResults(res_deseq2, airway, name = \"dex: trt vs untrt\") app <- iSEE(airway, initial = list( DETable(ContrastName=\"dex: trt vs untrt\", HiddenColumns = c(\"baseMean\", \"lfcSE\", \"stat\"), PanelWidth = 4L), VolcanoPlot(ContrastName=\"dex: trt vs untrt\", PanelWidth = 4L), MAPlot(ContrastName=\"dex: trt vs untrt\", PanelWidth = 4L) )) if (interactive()) { shiny::runApp(app) }"},{"path":"https://isee.github.io/iSEEde/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"iSEE extension for panels related to differential expression analysis","text":"citation output using citation('iSEEde') R. Please run check updates cite iSEEde. Please note iSEEde made possible thanks many R bioinformatics software authors, cited either vignettes /paper(s) describing package.","code":"print(citation(\"iSEEde\"), bibtex = TRUE) #> #> To cite package 'iSEEde' in publications use: #> #> Rue-Albrecht K (2022). _iSEEde: iSEE extension for panels related to #> differential expression analysis_. R package version 0.99.0, #> . #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, #> title = {iSEEde: iSEE extension for panels related to differential expression analysis}, #> author = {Kevin Rue-Albrecht}, #> year = {2022}, #> note = {R package version 0.99.0}, #> url = {https://github.com/iSEE/iSEEde}, #> }"},{"path":"https://isee.github.io/iSEEde/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"iSEE extension for panels related to differential expression analysis","text":"Please note iSEEde project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://isee.github.io/iSEEde/index.html","id":"development-tools","dir":"","previous_headings":"","what":"Development tools","title":"iSEE extension for panels related to differential expression analysis","text":"Continuous code testing possible thanks GitHub actions usethis, remotes, rcmdcheck customized use Bioconductor’s docker containers BiocCheck. Code coverage assessment possible thanks codecov covr. documentation website automatically updated thanks pkgdown. code styled automatically thanks styler. documentation formatted thanks devtools roxygen2. details, check dev directory. package developed using biocthis.","code":""},{"path":"https://isee.github.io/iSEEde/index.html","id":"code-of-conduct-1","dir":"","previous_headings":"","what":"Code of Conduct","title":"iSEE extension for panels related to differential expression analysis","text":"Please note iSEEde project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://isee.github.io/iSEEde/reference/DETable-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The DETable class — DETable-class","title":"The DETable class — DETable-class","text":"DETable class RowTable subclass dedicated creating volcano plot. retrieves table results selected differential expression contrast creates interactive table row represents feature.","code":""},{"path":"https://isee.github.io/iSEEde/reference/DETable-class.html","id":"slot-overview","dir":"Reference","previous_headings":"","what":"Slot overview","title":"The DETable class — DETable-class","text":"following slots control test procedure: ContrastName, character scalar indicating name contrast display. RoundDigits, logical scalar indicating whether round numeric values (see SignifDigits). SignifDigits, integer scalar indicating number significant digits use rounding numbers (see RoundDigits). addition, class inherits slots parent RowTable Table classes.","code":""},{"path":"https://isee.github.io/iSEEde/reference/DETable-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The DETable class — DETable-class","text":"","code":"x <- DETable() x #> Panel object of class DETable #> Get or set individual parameters with ‘[[’ #> Available parameters: #> ContrastName: NA #> DataBoxOpen: FALSE #> HiddenColumns: #> PanelHeight: 500 #> PanelId: NA #> PanelWidth: 4 #> RoundDigits: FALSE #> RowSelectionDynamicSource: FALSE #> RowSelectionSource: --- #> Search: #> SearchColumns: #> Selected: NA #> SelectionBoxOpen: FALSE #> SignifDigits: 3 #> VersionInfo: list of length 1"},{"path":"https://isee.github.io/iSEEde/reference/LogFCLogFCPlot-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The LogFCLogFCPlot class — LogFCLogFCPlot-class","title":"The LogFCLogFCPlot class — LogFCLogFCPlot-class","text":"LogFCLogFCPlot class RowDataPlot subclass dedicated comparing log-fold-change value two contrasts. retrieves log-fold change two selected contrasts creates row-based plot point represents feature.","code":""},{"path":"https://isee.github.io/iSEEde/reference/LogFCLogFCPlot-class.html","id":"slot-overview","dir":"Reference","previous_headings":"","what":"Slot overview","title":"The LogFCLogFCPlot class — LogFCLogFCPlot-class","text":"following slots control test procedure: ContrastNameX, character scalar indicating name contrast display x-axis. ContrastNameY, character scalar indicating name contrast display y-axis. addition, class inherits slots parent RowDotPlot, DotPlot, Panel classes.","code":""},{"path":"https://isee.github.io/iSEEde/reference/LogFCLogFCPlot-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The LogFCLogFCPlot class — LogFCLogFCPlot-class","text":"","code":"x <- LogFCLogFCPlot() x #> Panel object of class LogFCLogFCPlot #> Get or set individual parameters with ‘[[’ #> Available parameters: #> BrushData: #> ColorBy: None #> ColorByDefaultColor: black #> ColorByFeatureDynamicSource: FALSE #> ColorByFeatureName: NA #> ColorByFeatureNameColor: red #> ColorByFeatureSource: --- #> ColorByRowData: NA #> ColorBySampleDynamicSource: FALSE #> ColorBySampleName: NA #> ColorBySampleNameAssay: logcounts #> ColorBySampleSource: --- #> ContourAdd: FALSE #> ContourColor: blue #> ContrastNameX: NA #> ContrastNameY: NA #> CustomLabels: FALSE #> CustomLabelsText: NA #> DataBoxOpen: FALSE #> Downsample: FALSE #> DownsampleResolution: 200 #> FacetColumnBy: None #> FacetColumnByRowData: NA #> FacetRowBy: None #> FacetRowByRowData: NA #> FixAspectRatio: FALSE #> FontSize: 1 #> HoverInfo: TRUE #> LabelCenters: FALSE #> LabelCentersBy: NA #> LabelCentersColor: black #> LegendPointSize: 1 #> LegendPosition: Bottom #> PanelHeight: 500 #> PanelId: NA #> PanelWidth: 4 #> PointAlpha: 1 #> PointSize: 1 #> RowSelectionDynamicSource: FALSE #> RowSelectionRestrict: FALSE #> RowSelectionSource: --- #> SelectionAlpha: 0.1 #> SelectionBoxOpen: FALSE #> SelectionHistory: #> ShapeBy: None #> ShapeByRowData: NA #> SizeBy: None #> SizeByRowData: NA #> TooltipRowData: #> VersionInfo: list of length 1 #> ViolinAdd: TRUE #> VisualBoxOpen: FALSE #> VisualChoices: Color #> ZoomData:"},{"path":"https://isee.github.io/iSEEde/reference/MAPlot-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The MAPlot class — MAPlot-class","title":"The MAPlot class — MAPlot-class","text":"MAPlot RowDataPlot subclass dedicated creating MA plot. retrieves log-fold change (M) mean average () values creates row-based plot point represents feature.","code":""},{"path":"https://isee.github.io/iSEEde/reference/MAPlot-class.html","id":"slot-overview","dir":"Reference","previous_headings":"","what":"Slot overview","title":"The MAPlot class — MAPlot-class","text":"following slots control test procedure: ContrastName, character scalar indicating name contrast display. addition, class inherits slots parent RowDotPlot, DotPlot, Panel classes.","code":""},{"path":"https://isee.github.io/iSEEde/reference/MAPlot-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The MAPlot class — MAPlot-class","text":"","code":"x <- MAPlot() x #> Panel object of class MAPlot #> Get or set individual parameters with ‘[[’ #> Available parameters: #> BrushData: #> ColorBy: None #> ColorByDefaultColor: black #> ColorByFeatureDynamicSource: FALSE #> ColorByFeatureName: NA #> ColorByFeatureNameColor: red #> ColorByFeatureSource: --- #> ColorByRowData: NA #> ColorBySampleDynamicSource: FALSE #> ColorBySampleName: NA #> ColorBySampleNameAssay: logcounts #> ColorBySampleSource: --- #> ContourAdd: FALSE #> ContourColor: blue #> ContrastName: NA #> CustomLabels: FALSE #> CustomLabelsText: NA #> DataBoxOpen: FALSE #> Downsample: FALSE #> DownsampleResolution: 200 #> FacetColumnBy: None #> FacetColumnByRowData: NA #> FacetRowBy: None #> FacetRowByRowData: NA #> FixAspectRatio: FALSE #> FontSize: 1 #> HoverInfo: TRUE #> LabelCenters: FALSE #> LabelCentersBy: NA #> LabelCentersColor: black #> LegendPointSize: 1 #> LegendPosition: Bottom #> PanelHeight: 500 #> PanelId: NA #> PanelWidth: 4 #> PointAlpha: 1 #> PointSize: 1 #> RowSelectionDynamicSource: FALSE #> RowSelectionRestrict: FALSE #> RowSelectionSource: --- #> SelectionAlpha: 0.1 #> SelectionBoxOpen: FALSE #> SelectionHistory: #> ShapeBy: None #> ShapeByRowData: NA #> SizeBy: None #> SizeByRowData: NA #> TooltipRowData: #> VersionInfo: list of length 1 #> ViolinAdd: TRUE #> VisualBoxOpen: FALSE #> VisualChoices: Color #> ZoomData:"},{"path":"https://isee.github.io/iSEEde/reference/VolcanoPlot-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The VolcanoPlot class — VolcanoPlot-class","title":"The VolcanoPlot class — VolcanoPlot-class","text":"VolcanoPlot RowDataPlot subclass dedicated creating volcano plot. retrieves log-fold change p-value creates row-based plot point represents feature.","code":""},{"path":"https://isee.github.io/iSEEde/reference/VolcanoPlot-class.html","id":"slot-overview","dir":"Reference","previous_headings":"","what":"Slot overview","title":"The VolcanoPlot class — VolcanoPlot-class","text":"following slots control test procedure: ContrastName, character scalar indicating name contrast display. addition, class inherits slots parent RowDotPlot, DotPlot, Panel classes.","code":""},{"path":"https://isee.github.io/iSEEde/reference/VolcanoPlot-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The VolcanoPlot class — VolcanoPlot-class","text":"","code":"x <- VolcanoPlot() x #> Panel object of class VolcanoPlot #> Get or set individual parameters with ‘[[’ #> Available parameters: #> BrushData: #> ColorBy: None #> ColorByDefaultColor: black #> ColorByFeatureDynamicSource: FALSE #> ColorByFeatureName: NA #> ColorByFeatureNameColor: red #> ColorByFeatureSource: --- #> ColorByRowData: NA #> ColorBySampleDynamicSource: FALSE #> ColorBySampleName: NA #> ColorBySampleNameAssay: logcounts #> ColorBySampleSource: --- #> ContourAdd: FALSE #> ContourColor: blue #> ContrastName: NA #> CustomLabels: FALSE #> CustomLabelsText: NA #> DataBoxOpen: FALSE #> Downsample: FALSE #> DownsampleResolution: 200 #> FacetColumnBy: None #> FacetColumnByRowData: NA #> FacetRowBy: None #> FacetRowByRowData: NA #> FixAspectRatio: FALSE #> FontSize: 1 #> HoverInfo: TRUE #> LabelCenters: FALSE #> LabelCentersBy: NA #> LabelCentersColor: black #> LegendPointSize: 1 #> LegendPosition: Bottom #> PanelHeight: 500 #> PanelId: NA #> PanelWidth: 4 #> PointAlpha: 1 #> PointSize: 1 #> RowSelectionDynamicSource: FALSE #> RowSelectionRestrict: FALSE #> RowSelectionSource: --- #> SelectionAlpha: 0.1 #> SelectionBoxOpen: FALSE #> SelectionHistory: #> ShapeBy: None #> ShapeByRowData: NA #> SizeBy: None #> SizeByRowData: NA #> TooltipRowData: #> VersionInfo: list of length 1 #> ViolinAdd: TRUE #> VisualBoxOpen: FALSE #> VisualChoices: Color #> ZoomData:"},{"path":"https://isee.github.io/iSEEde/reference/contrastResults.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract contrast results embedded in a SummarizedExperiment object — contrastResultsNames","title":"Extract contrast results embedded in a SummarizedExperiment object — contrastResultsNames","text":"contrastResults returns either contrasts results stored object single contrast result name. contrastResultsNames returns names contrast results embedded object.","code":""},{"path":"https://isee.github.io/iSEEde/reference/contrastResults.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract contrast results embedded in a SummarizedExperiment object — contrastResultsNames","text":"","code":"contrastResultsNames(object) contrastResults(object, name)"},{"path":"https://isee.github.io/iSEEde/reference/contrastResults.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract contrast results embedded in a SummarizedExperiment object — contrastResultsNames","text":"object SummarizedExperiment object. name (Optional) Name single contrast result name extract. Use contrastResultsNames(object) list available names.","code":""},{"path":"https://isee.github.io/iSEEde/reference/contrastResults.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract contrast results embedded in a SummarizedExperiment object — contrastResultsNames","text":"contrastResultsNames: names embedded contrast results available. contrastResults: DataFrame differential expression statistics. name missing, contrastResults returns nested DataFrame column contains results single contrast. name given, contrastResults returns DataFrame contains results single contrast.","code":""},{"path":"https://isee.github.io/iSEEde/reference/contrastResults.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract contrast results embedded in a SummarizedExperiment object — contrastResultsNames","text":"","code":"library(\"iSEEde\") library(\"airway\") library(\"DESeq2\") library(\"iSEE\") ## # Example data ---- ## data(\"airway\") airway$dex <- relevel(airway$dex, \"untrt\") dds <- DESeqDataSet(airway, ~ 0 + dex + cell) dds <- DESeq(dds) #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing res_deseq2 <- results(dds, contrast = list(\"dextrt\", \"dexuntrt\")) airway <- embedContrastResults(res_deseq2, airway, name = \"dex: trt vs untrt\") ## # List result names --- ## contrastResultsNames(airway) #> [1] \"dex: trt vs untrt\" ## # Extract results --- ## contrastResults(airway) #> DataFrame with 63677 rows and 1 column #> dex: trt vs untrt #> #> ENSG00000000003 #> ENSG00000000005 #> ENSG00000000419 #> ENSG00000000457 #> ENSG00000000460 #> ... ... #> ENSG00000273489 #> ENSG00000273490 #> ENSG00000273491 #> ENSG00000273492 #> ENSG00000273493 contrastResults(airway, \"dex: trt vs untrt\") #> iSEEDESeq2Results with 63677 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue #> #> ENSG00000000003 708.6022 -0.3812539 0.100654 -3.787752 0.000152016 #> ENSG00000000005 0.0000 NA NA NA NA #> ENSG00000000419 520.2979 0.2068127 0.112219 1.842944 0.065337213 #> ENSG00000000457 237.1630 0.0379205 0.143445 0.264356 0.791505314 #> ENSG00000000460 57.9326 -0.0881679 0.287142 -0.307054 0.758802543 #> ... ... ... ... ... ... #> ENSG00000273489 0.275899 1.483744 3.51398 0.422240 0.672850 #> ENSG00000273490 0.000000 NA NA NA NA #> ENSG00000273491 0.000000 NA NA NA NA #> ENSG00000273492 0.105978 -0.463688 3.52312 -0.131613 0.895290 #> ENSG00000273493 0.106142 -0.521372 3.53142 -0.147638 0.882628 #> padj #> #> ENSG00000000003 0.00128292 #> ENSG00000000005 NA #> ENSG00000000419 0.19646961 #> ENSG00000000457 0.91141884 #> ENSG00000000460 0.89500551 #> ... ... #> ENSG00000273489 NA #> ENSG00000273490 NA #> ENSG00000273491 NA #> ENSG00000273492 NA #> ENSG00000273493 NA"},{"path":"https://isee.github.io/iSEEde/reference/de-generics.html","id":null,"dir":"Reference","previous_headings":"","what":"Generics for Differential Expression Results — de-generics","title":"Generics for Differential Expression Results — de-generics","text":"overview generics accessing common pieces information differential expression results.","code":""},{"path":"https://isee.github.io/iSEEde/reference/de-generics.html","id":"definitions","dir":"Reference","previous_headings":"","what":"Definitions","title":"Generics for Differential Expression Results — de-generics","text":"pValue(x) returns named numeric vector raw p-values. log2FoldChange(x) returns named numeric vector log2-fold-change values. averageLog2(x) returns named numeric vector average log2-expression values.","code":""},{"path":"https://isee.github.io/iSEEde/reference/de-generics.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generics for Differential Expression Results — de-generics","text":"Kevin Rue-Albrecht","code":""},{"path":"https://isee.github.io/iSEEde/reference/de-generics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generics for Differential Expression Results — de-generics","text":"","code":"showMethods(pValue) #> Function: pValue (package iSEEde) #> x=\"iSEEDESeq2Results\" #> x=\"iSEELimmaResults\" #> x=\"iSEEedgeRResults\" #> showMethods(log2FoldChange) #> Function: log2FoldChange (package iSEEde) #> x=\"iSEEDESeq2Results\" #> x=\"iSEELimmaResults\" #> x=\"iSEEedgeRResults\" #> showMethods(averageLog2) #> Function: averageLog2 (package iSEEde) #> x=\"iSEEDESeq2Results\" #> x=\"iSEELimmaResults\" #> x=\"iSEEedgeRResults\" #>"},{"path":"https://isee.github.io/iSEEde/reference/iSEEDESeq2Results-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","title":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","text":"iSEEDESeq2Results class used provide common interface differential expression results produced DESeq2 package. provides methods access common differential expression statistics (e.g., log2 fold-change, p-value, log2 average abundance).","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEEDESeq2Results-class.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","text":"class inherits slots directly parent class DataFrame.","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEEDESeq2Results-class.html","id":"constructor","dir":"Reference","previous_headings":"","what":"Constructor","title":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","text":"iSEEDESeq2Results(data, row.names = rownames(data)) creates instance iSEEDESeq2Results class, : data data.frame produced DESeq2::results() DESeq2::lfcShrink(). row.names character vector rownames SummarizedExperiment object object embedded. Must superset rownames(data).","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEEDESeq2Results-class.html","id":"supported-methods","dir":"Reference","previous_headings":"","what":"Supported methods","title":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","text":"embedContrastResults(x, se, name, ...) embeds x se identifier name. See embedContrastResults() details. pValue(x) returns vector raw p-values. log2FoldChange(x) returns vector log2-fold-change values. averageLog2(x) returns vector average log2-expression values.","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEEDESeq2Results-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","text":"Kevin Rue-Albrecht","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEEDESeq2Results-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The iSEEDESeq2Results class — iSEEDESeq2Results-class","text":"","code":"library(DESeq2) ## # From DESeq2::DESeq() ---- ## cnts <- matrix(rnbinom(n = 1000, mu = 100, size = 1 / 0.5), ncol = 10) rownames(cnts) <- paste(\"Gene\", 1:100) cond <- factor(rep(1:2, each = 5)) # object construction dds <- DESeqDataSetFromMatrix(cnts, DataFrame(cond), ~cond) #> converting counts to integer mode # standard analysis dds <- DESeq(dds) #> estimating size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> -- note: fitType='parametric', but the dispersion trend was not well captured by the #> function: y = a/x + b, and a local regression fit was automatically substituted. #> specify fitType='local' or 'mean' to avoid this message next time. #> final dispersion estimates #> fitting model and testing res <- results(dds) head(res) #> log2 fold change (MLE): cond 2 vs 1 #> Wald test p-value: cond 2 vs 1 #> DataFrame with 6 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue padj #> #> Gene 1 102.5201 -0.727757 0.804034 -0.905132 0.365395 0.960799 #> Gene 2 83.0402 -0.409929 0.563552 -0.727402 0.466979 0.974797 #> Gene 3 85.6498 0.101207 0.521651 0.194013 0.846166 0.974797 #> Gene 4 100.5620 0.197987 0.592194 0.334328 0.738132 0.974797 #> Gene 5 82.6255 -0.541261 0.622164 -0.869965 0.384320 0.960799 #> Gene 6 100.7767 -0.256369 0.715565 -0.358274 0.720138 0.974797 ## # iSEEDESeq2Results ---- ## # Embed the DESeq2 results in the SummarizedExperiment object dds <- embedContrastResults(res, dds, name = \"DESeq2\") ## # Access ---- ## contrastResultsNames(dds) #> [1] \"DESeq2\" contrastResults(dds) #> DataFrame with 100 rows and 1 column #> DESeq2 #> #> Gene 1 #> Gene 2 #> Gene 3 #> Gene 4 #> Gene 5 #> ... ... #> Gene 96 #> Gene 97 #> Gene 98 #> Gene 99 #> Gene 100 contrastResults(dds, \"DESeq2\") #> iSEEDESeq2Results with 100 rows and 6 columns #> baseMean log2FoldChange lfcSE stat pvalue padj #> #> Gene 1 102.5201 -0.727757 0.804034 -0.905132 0.365395 0.960799 #> Gene 2 83.0402 -0.409929 0.563552 -0.727402 0.466979 0.974797 #> Gene 3 85.6498 0.101207 0.521651 0.194013 0.846166 0.974797 #> Gene 4 100.5620 0.197987 0.592194 0.334328 0.738132 0.974797 #> Gene 5 82.6255 -0.541261 0.622164 -0.869965 0.384320 0.960799 #> ... ... ... ... ... ... ... #> Gene 96 129.3170 -0.0744697 0.503853 -0.1478006 0.882500 0.974797 #> Gene 97 81.2144 -0.3133681 0.577976 -0.5421822 0.587693 0.974797 #> Gene 98 63.7106 -0.1904979 0.613499 -0.3105104 0.756173 0.974797 #> Gene 99 144.0617 0.1101995 0.693533 0.1588958 0.873751 0.974797 #> Gene 100 102.9799 0.0354005 0.528068 0.0670377 0.946552 0.989373 head(pValue(contrastResults(dds, \"DESeq2\"))) #> Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 #> 0.3653954 0.4669795 0.8461658 0.7381317 0.3843196 0.7201381 head(log2FoldChange(contrastResults(dds, \"DESeq2\"))) #> Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 #> -0.7277568 -0.4099289 0.1012071 0.1979872 -0.5412611 -0.2563687 head(averageLog2(contrastResults(dds, \"DESeq2\"))) #> Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 #> 6.679763 6.375739 6.420378 6.651942 6.368515 6.655019"},{"path":"https://isee.github.io/iSEEde/reference/iSEELimmaResults-class.html","id":null,"dir":"Reference","previous_headings":"","what":"The iSEELimmaResults class — iSEELimmaResults-class","title":"The iSEELimmaResults class — iSEELimmaResults-class","text":"iSEELimmaResults class used provide common interface differential expression results produced limma package. provides methods access common differential expression statistics (e.g., log fold-change, p-value, log2 average abundance).","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEELimmaResults-class.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The iSEELimmaResults class — iSEELimmaResults-class","text":"class inherits slots directly parent class DataFrame.","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEELimmaResults-class.html","id":"constructor","dir":"Reference","previous_headings":"","what":"Constructor","title":"The iSEELimmaResults class — iSEELimmaResults-class","text":"iSEELimmaResults(data, row.names = rownames(data)) creates instance iSEELimmaResults class, : data data.frame produced limma::topTable(). row.names character vector rownames SummarizedExperiment object object embedded. Must superset rownames(data).","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEELimmaResults-class.html","id":"supported-methods","dir":"Reference","previous_headings":"","what":"Supported methods","title":"The iSEELimmaResults class — iSEELimmaResults-class","text":"embedContrastResults(x, se, name, class = \"limma\", ...) embeds x se identifier name. See embedContrastResults() details. pValue(x) returns vector raw p-values. log2FoldChange(x) returns vector log2-fold-change values. averageLog2(x) returns vector average log2-expression values.","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEELimmaResults-class.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The iSEELimmaResults class — iSEELimmaResults-class","text":"Kevin Rue-Albrecht","code":""},{"path":"https://isee.github.io/iSEEde/reference/iSEELimmaResults-class.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The iSEELimmaResults class — iSEELimmaResults-class","text":"","code":"library(limma) #> #> Attaching package: ‘limma’ #> The following object is masked from ‘package:DESeq2’: #> #> plotMA #> The following object is masked from ‘package:BiocGenerics’: #> #> plotMA library(SummarizedExperiment) ## # From limma::lmFit() ---- ## sd <- 0.3 * sqrt(4 / rchisq(100, df = 4)) y <- matrix(rnorm(100 * 6, sd = sd), 100, 6) rownames(y) <- paste(\"Gene\", 1:100) y[1:2, 4:6] <- y[1:2, 4:6] + 2 design <- cbind(Grp1 = 1, Grp2vs1 = c(0, 0, 0, 1, 1, 1)) fit <- lmFit(y, design) fit <- eBayes(fit) tt <- topTable(fit, coef = 2) head(tt) #> logFC AveExpr t P.Value adj.P.Val B #> Gene 2 2.2640913 0.92510941 11.350832 1.402741e-05 0.001402741 3.852453 #> Gene 78 -2.2011871 -0.49464729 -4.289664 4.117023e-03 0.205851148 -2.336193 #> Gene 1 1.9110762 1.35369919 3.728917 8.166075e-03 0.272202489 -3.080341 #> Gene 61 -0.6188866 -0.03854689 -3.322904 1.381124e-02 0.345281103 -3.645980 #> Gene 74 -0.5626678 -0.14647983 -2.617643 3.632140e-02 0.695593282 -4.667721 #> Gene 22 -0.4379104 -0.22422247 -2.420919 4.803481e-02 0.695593282 -4.956539 ## # iSEELimmaResults ---- ## # Simulate the original SummarizedExperiment object se <- SummarizedExperiment(assays = list(counts = y)) # Embed the Limma-Voom results in the SummarizedExperiment object se <- embedContrastResults(tt, se, name = \"Limma-Voom\", class = \"limma\") ## # Access ---- ## contrastResultsNames(se) #> [1] \"Limma-Voom\" contrastResults(se) #> DataFrame with 100 rows and 1 column #> Limma-Voom #> #> Gene 1 #> Gene 2