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diff --git a/docs/articles/romic.html b/docs/articles/romic.html index 814808d..dce90ee 100644 --- a/docs/articles/romic.html +++ b/docs/articles/romic.html @@ -59,7 +59,7 @@ -
+
-
@@ -84,6 +85,10 @@ transpose
verbose
+
R’Omic: high-dimensional data manipulation
@@ -75,21 +75,44 @@R’Omic: high-dimensional data manipulation
Core data structures
-R ’Omic revolves around data structures for representing high-dimensional data. The data model can most easily be seen from the triple_omic object which represents features, samples, and measurements with a compact relational schema.
+R ’Omic revolves around data structures for representing +high-dimensional data. The data model can most easily be seen from the +triple_omic object which represents features, samples, and measurements +with a compact relational schema.
-
-
- features - one row per feature (e.g., gene, transcript, metabolite) +
- features - one row per feature (e.g., gene, transcript, +metabolite)
- samples - one row per sample -
- measurements - one row per observation (of a single feature in a single sample) +
- measurements - one row per observation (of a single feature in a +single sample)
The relationships among variables in these tables are tracked using a design table which identifies the feature, and sample primary keys which uniquely define a feature or sample. This table also tracks the variables associated with features, samples, and measurements so these tables can be joined and re-arranged. This feature is taken advantage of to aggregate the features, samples, and measurements into a single tidy_omic table.
+The relationships among variables in these tables are tracked using a +design table which identifies the feature, and sample primary keys which +uniquely define a feature or sample. This table also tracks the +variables associated with features, samples, and measurements so these +tables can be joined and re-arranged. This feature is taken advantage of +to aggregate the features, samples, and measurements into a single +tidy_omic table.
Example Data
-To demonstrate romic’s functionality, this vignette will focus on analysis of the Brauer et al. 2008 yeast gene expression experiment. In this experiment, the impact of a yeasts’ nutrient environment and growth rate on gene expression was explored using 2-color microarrays. There are 36 samples, organized in a full-factorial design (all x all levels), which differ in terms of which nutrient limits growth and how fast the culture is growing.
+To demonstrate romic’s functionality, this vignette will focus on +analysis of the Brauer et al. 2008 +yeast gene expression experiment. In this experiment, the impact of a +yeasts’ nutrient environment and growth rate on gene expression was +explored using 2-color microarrays. There are 36 samples, organized in a +full-factorial design (all x all levels), which differ in terms of which +nutrient limits growth and how fast the culture is growing.
-
-
- Limiting nutrient: growth is limited with reduced levels of Nitrogen, Phosphorous, Leucine, Uracil, Glucose or Sulfur. -
- Dilution rate: 0.05-0.3 hrs-1, is the rate that the culture is diluted and fresh media is supplied. This sets up a chemostationary condition where growth rate is entrained to the dilution rate. +
- Limiting nutrient: growth is limited with reduced levels of +Nitrogen, Phosphorous, +Leucine, Uracil, +Glucose or Sulfur. +
- Dilution rate: 0.05-0.3 hrs-1, is the rate that the +culture is diluted and fresh media is supplied. This sets up a +chemostationary condition where growth rate is entrained to the dilution +rate.
@@ -98,7 +121,9 @@ Dataset Creation
-Dataset Creation
library(dplyr)
library(romic)
library(dplyr)
library(romic)A tidy_omic dataset can be loaded by passing a data table and specifying which variables are unique to features, samples and measurements
+A tidy_omic dataset can be loaded by passing a data table and +specifying which variables are unique to features, samples and +measurements
tidy_brauer <- create_tidy_omic(
df = brauer_2008,
@@ -107,8 +132,13 @@ Dataset Creation sample_pk = "sample",
sample_vars = c("nutrient", "DR")
)
A triple_omic dataset can be loaded by providing a features, samples, and measurements tables and specifying which variarbles are the features’ and measurements’ primary keys.
-++## 1 measurement variables were defined as the +## left overs from the specified feature and sample varaibles: +## expressionA triple_omic dataset can be loaded by providing a features, samples, +and measurements tables and specifying which variarbles are the +features’ and measurements’ primary keys.
+-triple_brauer <- create_triple_omic( measurement_df = brauer_2008 %>% select(name, sample, expression), feature_df = brauer_2008 %>% select(name:systematic_name) %>% distinct(), @@ -116,8 +146,11 @@Dataset Creation feature_pk = "name", sample_pk = "sample" )
Generally, we wouldn’t maintain both a triple and tidy omic version of a dataset but rather convert back and forth between these representation based on the needs of the analysis. To convert between these classes we can use:
-+Generally, we wouldn’t maintain both a triple and tidy omic version +of a dataset but rather convert back and forth between these +representation based on the needs of the analysis. To convert between +these classes we can use:
+@@ -125,8 +158,12 @@# convert back and forth between tidy and triple representations triple_brauer <- tidy_to_triple(tidy_brauer) tidy_brauer <- triple_to_tidy(triple_brauer)Dataset Creation
Data Manipulation
-Most functions actually don’t care whether you provide a tidy or triple representation of your dataset. These functions take a T*Omic object (i.e., tidy or triple omic), and apply an operation and return whichever class was provided. We can see this by filtering a triple_omic.
-@@ -91,6 +92,10 @@+Most functions actually don’t care whether you provide a tidy or +triple representation of your dataset. These functions take a T*Omic +object (i.e., tidy or triple omic), and apply an operation and return +whichever class was provided. We can see this by filtering a +triple_omic.
+-filtered_brauer <- brauer_2008_triple %>% filter_tomic( filter_type = "category", @@ -140,8 +177,10 @@Data Manipulation= "DR", filter_value = c(0.05, 0.2) )
We could also modify a table directly and then update it in the tomic object. For this workflow, update_tomic is used so the design can keep up with any fields that have changed.
-+We could also modify a table directly and then update it in the tomic +object. For this workflow, update_tomic is used so the design can keep +up with any fields that have changed.
+updated_features <- brauer_2008_triple$features %>% dplyr::filter(BP == "biological process unknown") %>% dplyr::mutate(chromosome = purrr::map_int(systematic_name, function(x) { @@ -156,8 +195,10 @@Data Manipulation
Visualisation
-Romic includes a few versatile ggplot2-based plotting functions which can show a complete set of measurements, as a heatmap, or univariate/bivariate slices of features, samples or measurements.
-@@ -100,6 +101,10 @@+Romic includes a few versatile ggplot2-based plotting functions which +can show a complete set of measurements, as a heatmap, or +univariate/bivariate slices of features, samples or measurements.
+plot_heatmap( filtered_brauer, value_var = "expression", @@ -166,7 +207,7 @@Visualisation= "grob" )
-
diff --git a/docs/articles/romic_files/figure-html/static_heatmap-1.png b/docs/articles/romic_files/figure-html/static_heatmap-1.png index 4ed01da..83eadbc 100644 Binary files a/docs/articles/romic_files/figure-html/static_heatmap-1.png and b/docs/articles/romic_files/figure-html/static_heatmap-1.png differ diff --git a/docs/articles/romic_files/figure-html/univariate_plot-1.png b/docs/articles/romic_files/figure-html/univariate_plot-1.png index 8ffc255..85b004c 100644 Binary files a/docs/articles/romic_files/figure-html/univariate_plot-1.png and b/docs/articles/romic_files/figure-html/univariate_plot-1.png differ diff --git a/docs/index.html b/docs/index.html index b03c17e..bc1ea33 100644 --- a/docs/index.html +++ b/docs/index.html @@ -64,6 +64,7 @@+centered_tidy <- tidy_brauer %>% center_tomic() @@ -178,7 +219,10 @@Visualisation
Interactive Visualisation
-Romic maintains a valid data model when various operations are performed, and includes visualization which flexibly allow a range of views into a dataset. These attributes are leveraged through interactive data visualization shiny apps.
+Romic maintains a valid data model when various operations are +performed, and includes visualization which flexibly allow a range of +views into a dataset. These attributes are leveraged through interactive +data visualization shiny apps.
Check them out with ?app_heatmap and ?app_flow
ROMIC standardizes the formatting of genomic data to open up general visualizations approaches which can be used for exploratory data analysis (EDA).
+Package Setup
@@ -92,9 +93,11 @@Data Model⚠️:
+
- Wide ⚠️: +
- A matrix of I features x J samples including extra columns for feature-level attributes and extra rows for sample-level attributes.
-- While ’omic data is often shared in this format, it is problematic for three reasons.
+
- While ’omic data is often shared in this format, it is problematic for three reasons. +
- Each column of a dataframe has the same classes, and all values of a matrix have a common class. If sample-level variables are provided (such as the experimental design) then they would need to be tied to column attributes which are difficult to work with, although this is easier with specialized data structures such as H5AD.
- Matrix data is difficult to deal with. We would have to track which columns are measurements and which are feature attributes, and if we wanted to say select a subset of features and color them by a sample attribute, it would be colossal pain.
- If we have two or more observation-level attributes, such as raw, normalized and log-transformed measurements then the information cannot nicely be formatted as a matrix.
@@ -102,18 +105,22 @@Data Model⭐:
+
- Tidy ⭐: +
-
- A table with one row for each measurement (I x J)
- Each measurement is associated with the attributes of its corresponding features and samples. This is helpful for measurement-level operations such as plotting informed by the experimental design.
-- A couple of downsides of this representation are that:
+
- A couple of downsides of this representation are that: +
- Feature- and sample-level attributes are highly duplicated so the representation is not particularly compact.
- Because feature- and sample attributes are duplicated, its difficult apply feature- or sample-level manipulation. For example, adding a gene’s GO category would involve separately adding it for each measurement of that gene. This invariably adds extra complexity and time to the code and there is a risk that an inconsistent output is produced (such as a gene being associated with different GO categories depending on which measurement it is).
- Triple ✨:
-
- Represent a dataset as three tables:
+
- Triple ✨: +
+
- Represent a dataset as three tables: +
- Features (I rows) - All unique features and feature-level attributes
- Samples (J rows) - All unique samples and sample-level attributes
- Measurements (I x J rows) - One row per observation. This will look like the tidy table above except the only feature- and sample-level attributes are the feature primary key (a variable which uniquely defines a feature) and the sample primary key (a variable which uniquely defines a sample)
@@ -154,7 +161,8 @@Visualizations plot_univariate create a histogram of a numeric feature present in features, samples or measurements
- -plot_bivariate creates a bivariate visualization of features, samples or measurements (including feature and sample attributes). If two numeric features are used then a scatter plot is created, while if the x-axis variables is categorical, a boxplot is created. Many types of visualizations can be created using this approach:
+plot_bivariate creates a bivariate visualization of features, samples or measurements (including feature and sample attributes). If two numeric features are used then a scatter plot is created, while if the x-axis variables is categorical, a boxplot is created. Many types of visualizations can be created using this approach: +
- plotting PC1 ~ PC2 in the samples tables
- creating a volcano plot of feature significance and effect sizes
- plotting measurement magnitudes for specific features with elements of the experimental design on the y-axis.
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index a63a91d..af84698 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,7 +1,7 @@ -pandoc: 2.7.3 +pandoc: 2.19.2 pkgdown: 2.0.7 pkgdown_sha: ~ articles: romic: romic.html -last_built: 2023-02-03T00:04Z +last_built: 2023-09-19T20:37Z diff --git a/docs/reference/add_pcs.html b/docs/reference/add_pcs.html index 2b26ac1..6d467c5 100644 --- a/docs/reference/add_pcs.html +++ b/docs/reference/add_pcs.html @@ -58,7 +58,8 @@Add PCA Loadings
center_rows = TRUE, npcs = NULL, missing_val_method = "drop_samples", - label_percent_varex = TRUE + label_percent_varex = TRUE, + verbose = TRUE )Arguments
+ + If true then PCs will be labelled by the percent of variability they explain.
verbose ++ extra reporting messages
@@ -90,6 +91,10 @@Value
@@ -114,35 +119,36 @@Examples
#> 40 features dropped due to missing values #> $features #> # A tibble: 460 × 4 -#> name BP MF syste…¹ -#> <chr> <chr> <chr> <chr> -#> 1 YOL029C biological process unknown mole… YOL029C -#> 2 SCW11 cytokinesis, completion of separation gluc… YGL028C -#> 3 YHR036W biological process unknown mole… YHR036W -#> 4 BGL2 cell wall organization and biogenesis gluc… YGR282C -#> 5 ACT1 cell wall organization and biogenesis* stru… YFL039C -#> 6 FKH1 pseudohyphal growth* tran… YIL131C -#> 7 HOC1 cell wall mannoprotein biosynthesis* tran… YJR075W -#> 8 CSN12 adaptation to pheromone during conjugation with cellul… mole… YJR084W -#> 9 YAL046C biological process unknown mole… YAL046C -#> 10 SLG1 cell wall organization and biogenesis* tran… YOR008C -#> # … with 450 more rows, and abbreviated variable name ¹systematic_name +#> name BP MF systematic_name +#> <chr> <chr> <chr> <chr> +#> 1 YOL029C biological process unknown mole… YOL029C +#> 2 SCW11 cytokinesis, completion of separation gluc… YGL028C +#> 3 YHR036W biological process unknown mole… YHR036W +#> 4 BGL2 cell wall organization and biogenesis gluc… YGR282C +#> 5 ACT1 cell wall organization and biogenesis* stru… YFL039C +#> 6 FKH1 pseudohyphal growth* tran… YIL131C +#> 7 HOC1 cell wall mannoprotein biosynthesis* tran… YJR075W +#> 8 CSN12 adaptation to pheromone during conjugation wit… mole… YJR084W +#> 9 YAL046C biological process unknown mole… YAL046C +#> 10 SLG1 cell wall organization and biogenesis* tran… YOR008C +#> # ℹ 450 more rows #> #> $samples #> # A tibble: 36 × 8 -#> sample nutrient DR `PC1 (30%)` `PC2 (24%)` `PC3 (14%)` `PC4 (8%)` PC5 (6…¹ -#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 G0.05 G 0.05 -0.252 0.00514 0.317 0.0234 -0.166 -#> 2 G0.1 G 0.1 -0.169 -0.0478 0.272 -0.104 -0.172 -#> 3 G0.15 G 0.15 -0.177 -0.0824 0.272 -0.0165 -0.193 -#> 4 G0.2 G 0.2 -0.153 -0.109 0.257 -0.00839 -0.174 -#> 5 G0.25 G 0.25 -0.0111 -0.177 0.179 0.00250 -0.163 -#> 6 G0.3 G 0.3 0.0561 -0.213 0.154 0.0247 -0.154 -#> 7 N0.05 N 0.05 -0.380 0.153 -0.175 0.427 -0.135 -#> 8 N0.1 N 0.1 -0.284 0.0849 -0.255 0.323 -0.109 -#> 9 N0.15 N 0.15 -0.0754 0.0482 -0.320 0.0141 -0.0906 -#> 10 N0.2 N 0.2 -0.0200 -0.0235 -0.311 0.0379 -0.0873 -#> # … with 26 more rows, and abbreviated variable name ¹`PC5 (6%)` +#> sample nutrient DR `PC1 (30%)` `PC2 (24%)` `PC3 (14%)` `PC4 (8%)` +#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 G0.05 G 0.05 -0.252 0.00514 0.317 0.0234 +#> 2 G0.1 G 0.1 -0.169 -0.0478 0.272 -0.104 +#> 3 G0.15 G 0.15 -0.177 -0.0824 0.272 -0.0165 +#> 4 G0.2 G 0.2 -0.153 -0.109 0.257 -0.00839 +#> 5 G0.25 G 0.25 -0.0111 -0.177 0.179 0.00250 +#> 6 G0.3 G 0.3 0.0561 -0.213 0.154 0.0247 +#> 7 N0.05 N 0.05 -0.380 0.153 -0.175 0.427 +#> 8 N0.1 N 0.1 -0.284 0.0849 -0.255 0.323 +#> 9 N0.15 N 0.15 -0.0754 0.0482 -0.320 0.0141 +#> 10 N0.2 N 0.2 -0.0200 -0.0235 -0.311 0.0379 +#> # ℹ 26 more rows +#> # ℹ 1 more variable: `PC5 (6%)` <dbl> #> #> $measurements #> # A tibble: 16,560 × 3 @@ -158,7 +164,7 @@Examples
#> 8 CSN12 G0.05 -0.49 #> 9 YAL046C G0.05 0.05 #> 10 SLG1 G0.05 -0.06 -#> # … with 16,550 more rows +#> # ℹ 16,550 more rows #> #> $design #> $design$features @@ -214,7 +220,7 @@Examples
#> 8 8 11.9 0.0283 PC8 (2%) #> 9 9 9.73 0.0189 PC9 (2%) #> 10 10 8.15 0.0133 PC10 (2%) -#> # … with 26 more rows +#> # ℹ 26 more rows #> #> #> attr(,"class") diff --git a/docs/reference/center_tomic.html b/docs/reference/center_tomic.html index 0f3328d..6709e2d 100644 --- a/docs/reference/center_tomic.html +++ b/docs/reference/center_tomic.html @@ -76,20 +76,19 @@Examples
center_tomic(brauer_2008_tidy) #> $data #> # A tibble: 18,000 × 8 -#> name BP MF syste…¹ sample nutri…² DR expres…³ -#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> -#> 1 YOL029C biological process unkno… mole… YOL029C G0.05 G 0.05 -0.149 -#> 2 YOL029C biological process unkno… mole… YOL029C G0.1 G 0.1 -0.109 -#> 3 YOL029C biological process unkno… mole… YOL029C G0.15 G 0.15 0.341 -#> 4 YOL029C biological process unkno… mole… YOL029C G0.2 G 0.2 0.251 -#> 5 YOL029C biological process unkno… mole… YOL029C G0.25 G 0.25 0.101 -#> 6 YOL029C biological process unkno… mole… YOL029C G0.3 G 0.3 0.0311 -#> 7 YOL029C biological process unkno… mole… YOL029C N0.05 N 0.05 0.331 -#> 8 YOL029C biological process unkno… mole… YOL029C N0.1 N 0.1 0.221 -#> 9 YOL029C biological process unkno… mole… YOL029C N0.15 N 0.15 0.111 -#> 10 YOL029C biological process unkno… mole… YOL029C N0.2 N 0.2 0.00111 -#> # … with 17,990 more rows, and abbreviated variable names ¹systematic_name, -#> # ²nutrient, ³expression +#> name BP MF systematic_name sample nutrient DR expression +#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> +#> 1 YOL029C biological pr… mole… YOL029C G0.05 G 0.05 -0.149 +#> 2 YOL029C biological pr… mole… YOL029C G0.1 G 0.1 -0.109 +#> 3 YOL029C biological pr… mole… YOL029C G0.15 G 0.15 0.341 +#> 4 YOL029C biological pr… mole… YOL029C G0.2 G 0.2 0.251 +#> 5 YOL029C biological pr… mole… YOL029C G0.25 G 0.25 0.101 +#> 6 YOL029C biological pr… mole… YOL029C G0.3 G 0.3 0.0311 +#> 7 YOL029C biological pr… mole… YOL029C N0.05 N 0.05 0.331 +#> 8 YOL029C biological pr… mole… YOL029C N0.1 N 0.1 0.221 +#> 9 YOL029C biological pr… mole… YOL029C N0.15 N 0.15 0.111 +#> 10 YOL029C biological pr… mole… YOL029C N0.2 N 0.2 0.00111 +#> # ℹ 17,990 more rows #> #> $design #> $design$features diff --git a/docs/reference/convert_wide_to_tidy_omic.html b/docs/reference/convert_wide_to_tidy_omic.html index 321f8b6..61ae810 100644 --- a/docs/reference/convert_wide_to_tidy_omic.html +++ b/docs/reference/convert_wide_to_tidy_omic.html @@ -58,7 +58,8 @@Convert Wide to Tidy Omic
feature_vars = NULL, sample_var = "sample", measurement_var = "abundance", - omic_type_tag = "general" + omic_type_tag = "general", + verbose = TRUE )Arguments
+ + an optional subtype of omic data: metabolomics, lipidomics, proteomics, genomics, general
verbose ++ extra reporting messages
Value
@@ -121,22 +126,24 @@Examples
feature_pk = "name", feature_vars = c("BP", "MF", "systematic_name") ) +#> 1 measurement variables were defined as the +#> left overs from the specified feature and sample varaibles: +#> abundance #> $data #> # A tibble: 18,000 × 6 -#> name BP MF syste…¹ sample abund…² -#> <chr> <chr> <chr> <chr> <chr> <dbl> -#> 1 YOL029C biological process unknown mole… YOL029C G0.05 -0.22 -#> 2 SCW11 cytokinesis, completion of separation gluc… YGL028C G0.05 -0.67 -#> 3 YHR036W biological process unknown mole… YHR036W G0.05 -0.91 -#> 4 BGL2 cell wall organization and biogenesis gluc… YGR282C G0.05 -0.08 -#> 5 ACT1 cell wall organization and biogenesis* stru… YFL039C G0.05 -0.04 -#> 6 FKH1 pseudohyphal growth* tran… YIL131C G0.05 -0.57 -#> 7 HOC1 cell wall mannoprotein biosynthesis* tran… YJR075W G0.05 -0.42 -#> 8 CSN12 adaptation to pheromone during conjugat… mole… YJR084W G0.05 -0.49 -#> 9 YAL046C biological process unknown mole… YAL046C G0.05 0.05 -#> 10 SLG1 cell wall organization and biogenesis* tran… YOR008C G0.05 -0.06 -#> # … with 17,990 more rows, and abbreviated variable names ¹systematic_name, -#> # ²abundance +#> name BP MF systematic_name sample abundance +#> <chr> <chr> <chr> <chr> <chr> <dbl> +#> 1 YOL029C biological process unknown mole… YOL029C G0.05 -0.22 +#> 2 SCW11 cytokinesis, completion of se… gluc… YGL028C G0.05 -0.67 +#> 3 YHR036W biological process unknown mole… YHR036W G0.05 -0.91 +#> 4 BGL2 cell wall organization and bi… gluc… YGR282C G0.05 -0.08 +#> 5 ACT1 cell wall organization and bi… stru… YFL039C G0.05 -0.04 +#> 6 FKH1 pseudohyphal growth* tran… YIL131C G0.05 -0.57 +#> 7 HOC1 cell wall mannoprotein biosyn… tran… YJR075W G0.05 -0.42 +#> 8 CSN12 adaptation to pheromone durin… mole… YJR084W G0.05 -0.49 +#> 9 YAL046C biological process unknown mole… YAL046C G0.05 0.05 +#> 10 SLG1 cell wall organization and bi… tran… YOR008C G0.05 -0.06 +#> # ℹ 17,990 more rows #> #> $design #> $design$features diff --git a/docs/reference/create_tidy_omic.html b/docs/reference/create_tidy_omic.html index ad07c44..50d571e 100644 --- a/docs/reference/create_tidy_omic.html +++ b/docs/reference/create_tidy_omic.html @@ -58,7 +58,8 @@Create Tidy Omic
feature_vars = NULL, sample_pk, sample_vars = NULL, - omic_type_tag = "general" + omic_type_tag = "general", + verbose = TRUE )Arguments
+ + an optional subtype of omic data: metabolomics, lipidomics, proteomics, genomics, general
verbose ++ extra reporting messages
Value
@@ -155,21 +160,24 @@Examples
feature_vars = "feature_group", sample_pk = "sample_id", sample_vars = "sample_group" ) +#> 1 measurement variables were defined as the +#> left overs from the specified feature and sample varaibles: +#> value #> $data #> # A tibble: 50 × 5 #> feature_id feature_group sample_id sample_group value #> <int> <chr> <chr> <chr> <dbl> -#> 1 1 a A a 0.668 -#> 2 1 a B a -0.923 -#> 3 1 a C b -0.340 -#> 4 1 a D b 1.77 -#> 5 1 a E b -2.12 -#> 6 2 a A a -1.99 -#> 7 2 a B a 2.70 -#> 8 2 a C b 0.881 -#> 9 2 a D b -0.0384 -#> 10 2 a E b -0.872 -#> # … with 40 more rows +#> 1 1 a A a 1.34 +#> 2 1 a B a 1.02 +#> 3 1 a C b 2.04 +#> 4 1 a D b -0.156 +#> 5 1 a E b 0.702 +#> 6 2 a A a -1.28 +#> 7 2 a B a -0.0231 +#> 8 2 a C b 0.343 +#> 9 2 a D b 0.757 +#> 10 2 a E b 0.910 +#> # ℹ 40 more rows #> #> $design #> $design$features diff --git a/docs/reference/downsample_heatmap.html b/docs/reference/downsample_heatmap.html index d580712..d198443 100644 --- a/docs/reference/downsample_heatmap.html +++ b/docs/reference/downsample_heatmap.html @@ -50,7 +50,13 @@Downsample Heatmap
-+downsample_heatmap(tidy_data, value_var, design, max_display_features = 1000)downsample_heatmap( + tidy_data, + value_var, + design, + max_display_features = 1000, + verbose = TRUE +)@@ -73,6 +79,10 @@Arguments
+ + aggregate and downsample distinct feature to this number to speed to up heatmap rendering.
verbose ++ extra reporting messages
Value
diff --git a/docs/reference/export_tomic_as_tidy.html b/docs/reference/export_tomic_as_tidy.html index 2de6057..457d752 100644 --- a/docs/reference/export_tomic_as_tidy.html +++ b/docs/reference/export_tomic_as_tidy.html @@ -52,7 +52,7 @@Export T*Omic in Tidy Format
-+export_tomic_as_tidy(tomic, dir_path, name_preamble)export_tomic_as_tidy(tomic, dir_path, name_preamble, verbose = TRUE)@@ -68,6 +68,10 @@Arguments
name_preamble + + start of output file name
verbose ++ extra reporting messages
Value
diff --git a/docs/reference/export_tomic_as_triple.html b/docs/reference/export_tomic_as_triple.html index fb8ef64..62b55e4 100644 --- a/docs/reference/export_tomic_as_triple.html +++ b/docs/reference/export_tomic_as_triple.html @@ -50,7 +50,7 @@Export T*Omic as Triple
-+export_tomic_as_triple(tomic, dir_path, name_preamble)export_tomic_as_triple(tomic, dir_path, name_preamble, verbose = TRUE)@@ -66,6 +66,10 @@Arguments
name_preamble + + start of output file name
verbose ++ extra reporting messages
Value
diff --git a/docs/reference/export_tomic_as_wide.html b/docs/reference/export_tomic_as_wide.html index 34595d6..2ed845f 100644 --- a/docs/reference/export_tomic_as_wide.html +++ b/docs/reference/export_tomic_as_wide.html @@ -59,7 +59,8 @@Export T*Omic as Wide Data
dir_path, name_preamble, value_var = NULL, - transpose = FALSE + transpose = FALSE, + verbose = TRUE )
Arguments
if TRUE then samples will be stored as rows
extra reporting messages