diff --git a/vignettes/tutorial.R b/vignettes/tutorial.R index 643f30d..32df7e8 100644 --- a/vignettes/tutorial.R +++ b/vignettes/tutorial.R @@ -29,10 +29,6 @@ library(rats) # Show the first rows of the table corresponding to one sample, from simulated data. head(sim_boot_data()[[2]][[1]]) -## ------------------------------------------------------------------------ -# Show the first rows of the table corresponding to one condition, from simulated data. -head(sim_count_data()[[2]]) - ## ------------------------------------------------------------------------ # Show the first rows of the table corresponding to the annotation, from simulated data. head(sim_count_data()[[1]]) @@ -97,6 +93,9 @@ print( names(ids) ) # DTU positive genes. print( ids[["dtu-genes"]] ) +## ------------------------------------------------------------------------ +print( names(mydtu) ) + ## ------------------------------------------------------------------------ # Parameter list's elements. print( names(mydtu$Parameters) ) diff --git a/vignettes/tutorial.Rmd b/vignettes/tutorial.Rmd index aab212d..980069e 100644 --- a/vignettes/tutorial.Rmd +++ b/vignettes/tutorial.Rmd @@ -1,7 +1,7 @@ --- title: "RATs Tutorial" author: "Kimon Froussios" -date: "`r date()`" +date: "23 AUG 2016" output: html_document: fig_caption: no @@ -113,14 +113,14 @@ library(rats) 2. Generic bootstrapped abundance estimates. 3. Generic abundance estimates. -1. From a [Sleuth](http://pachterlab.github.io/sleuth/) object `rats` extracts the bootstrapped abundance estimates. See the +From a [Sleuth](http://pachterlab.github.io/sleuth/) object `rats` extracts the bootstrapped abundance estimates. See the [introduction to Sleuth](http://rawgit.com/pachterlab/sleuth/master/inst/doc/intro.html) pages for details on how to load the transcript abundance estimate data from [Kallisto](http://pachterlab.github.io/kallisto/) into a Sleuth object, and see the [wasabi](http://github.com/COMBINE-lab/wasabi) tool for how to load the transcript abundance estimate data from [Sailfish](http://github.com/kingsfordgroup/sailfish) or [Salmon](https://github.com/COMBINE-lab/salmon) into a sleuth object. -2. Bootstrapped abundance estimates obtained by other means can be input as `list`s of `data.table`s. Two lists are needed, one per condition. +Bootstrapped abundance estimates obtained by other means can be input as `list`s of `data.table`s. Two lists are needed, one per condition. Each data table should contain the transcript identifiers in the first column, followed by columns containing the estimates from the bootstrap iterations: ```{r} @@ -128,13 +128,9 @@ Each data table should contain the transcript identifiers in the first column, f head(sim_boot_data()[[2]][[1]]) ``` -3. Generic abundance estimates, without bootstrapping information, can be input simply as two `data.table`s, one per condition. The first column should -contain the transcript identifiers, followed by columns listing the abundance per sample: - -```{r} -# Show the first rows of the table corresponding to one condition, from simulated data. -head(sim_count_data()[[2]]) -``` +Generic abundance estimates, without bootstrapping information, can be input simply as two `data.table`s, one per condition. The first column should +contain the transcript identifiers, followed by columns listing the abundance per sample. The format of each table is identical to the one shown above, +only the meaning of the numbers changes. **Regardless of data format**, `rats` also needs an annotation `data.frame` that matches transcript identifiers to gene identifiers. This looks like this: @@ -301,6 +297,13 @@ print( ids[["dtu-genes"]] ) ## Output structure +The output of `rats` is a list containing 3 elements: + +```{r} +print( names(mydtu) ) +``` + + ### Parameters `Parameters` is a list that contains information about the data and the settings. diff --git a/vignettes/tutorial.html b/vignettes/tutorial.html index a305204..d14e08f 100644 --- a/vignettes/tutorial.html +++ b/vignettes/tutorial.html @@ -11,6 +11,7 @@ + RATs Tutorial @@ -127,7 +128,7 @@

RATs Tutorial

Kimon Froussios

-

Mon Aug 22 22:10:19 2016

+

23 AUG 2016

Abstract

The rats package is aimed at people analysing gene expression and transcript abundance, particularly with RNA-Seq type of data in mind. This R Markdown vignette constitutes the main documentation for the package.

@@ -248,10 +249,10 @@

Input formats

  1. A Sleuth object.
  2. Generic bootstrapped abundance estimates.
    3. -

  3. Generic abundance estimates.

    4. -

  4. From a Sleuth object rats extracts the bootstrapped abundance estimates. See the introduction to Sleuth pages for details on how to load the transcript abundance estimate data from Kallisto into a Sleuth object, and see the wasabi tool for how to load the transcript abundance estimate data from Sailfish or Salmon into a sleuth object.

    5. -

  5. Bootstrapped abundance estimates obtained by other means can be input as lists of data.tables. Two lists are needed, one per condition. Each data table should contain the transcript identifiers in the first column, followed by columns containing the estimates from the bootstrap iterations:

    6. +
  6. Generic abundance estimates.
+

From a Sleuth object rats extracts the bootstrapped abundance estimates. See the introduction to Sleuth pages for details on how to load the transcript abundance estimate data from Kallisto into a Sleuth object, and see the wasabi tool for how to load the transcript abundance estimate data from Sailfish or Salmon into a sleuth object.

+

Bootstrapped abundance estimates obtained by other means can be input as lists of data.tables. Two lists are needed, one per condition. Each data table should contain the transcript identifiers in the first column, followed by columns containing the estimates from the bootstrap iterations:

# Show the first rows of the table corresponding to one sample, from simulated data.
 head(sim_boot_data()[[2]][[1]])
##    target_id V1 V2 V3
@@ -261,18 +262,7 @@ 
Input formats

 ## 4:  1D1C:two 76 80 72
 ## 5:    1B1C.1  0  0  0
 ## 6:    1B1C.2 52 55 50
-
    -
  1. Generic abundance estimates, without bootstrapping information, can be input simply as two data.tables, one per condition. The first column should contain the transcript identifiers, followed by columns listing the abundance per sample:
    2. -
-
# Show the first rows of the table corresponding to one condition, from simulated data.
-head(sim_count_data()[[2]])
-
##    target_id V1 V2 V3
-## 1:     NIB.1  0  0  0
-## 2:    1A1N-2 20 21 18
-## 3:  1D1C:one  0  0  0
-## 4:  1D1C:two 76 80 72
-## 5:    1B1C.1  0  0  0
-## 6:    1B1C.2 52 55 50
+

Generic abundance estimates, without bootstrapping information, can be input simply as two data.tables, one per condition. The first column should contain the transcript identifiers, followed by columns listing the abundance per sample. The format of each table is identical to the one shown above, only the meaning of the numbers changes.

Regardless of data format, rats also needs an annotation data.frame that matches transcript identifiers to gene identifiers. This looks like this:

# Show the first rows of the table corresponding to the annotation, from simulated data.
 head(sim_count_data()[[1]])
@@ -392,6 +382,9 @@

Quick results

Output structure

+

The output of rats is a list containing 3 elements:

+
print( names(mydtu) )
+
## [1] "Parameters"  "Genes"       "Transcripts"

Parameters

Parameters is a list that contains information about the data and the settings.

@@ -556,22 +549,22 @@

Example results

## 3: NA NA NA NA NA ## 4: NA NA NA NA NA ## 5: NA NA NA NA NA -## 6: 0.83 0.002297371 0.0006862716 0.002867174 0.0009853328 +## 6: 0.82 0.002913862 0.0009343038 0.004617752 0.001553497 ## 7: NA NA NA NA NA -## 8: 1.00 0.000000000 0.0000000000 0.000000000 0.0000000000 +## 8: 1.00 0.000000000 0.0000000000 0.000000000 0.000000000 ## 9: NA NA NA NA NA -## 10: 0.00 0.784661756 0.7036691880 0.139088825 0.1925920036 +## 10: 0.00 0.776276584 0.6948216461 0.142400556 0.196181482 ## boot_minAB boot_minBA boot_maxAB boot_maxBA boot_na ## 1: NA NA NA NA NA ## 2: NA NA NA NA NA ## 3: NA NA NA NA NA ## 4: NA NA NA NA NA ## 5: NA NA NA NA NA -## 6: 5.030128e-05 1.550451e-05 0.01321885 0.00480727 0 +## 6: 5.030128e-05 1.550451e-05 0.0154553 0.00480727 0 ## 7: NA NA NA NA NA -## 8: 0.000000e+00 0.000000e+00 0.00000000 0.00000000 0 +## 8: 0.000000e+00 0.000000e+00 0.0000000 0.00000000 0 ## 9: NA NA NA NA NA -## 10: 5.143483e-01 3.311102e-01 0.99150834 0.98871978 0 +## 10: 5.143483e-01 3.311102e-01 0.9915083 0.98871978 0

There are 10 genes in the annotation used. Here are some possible scenarios:

diff --git a/vignettes/tutorial.md b/vignettes/tutorial.md index 6ef04d9..5a9feec 100644 --- a/vignettes/tutorial.md +++ b/vignettes/tutorial.md @@ -1,6 +1,6 @@ # RATs Tutorial Kimon Froussios -`r date()` +23 AUG 2016 # Relative Abundance of Transcripts (RATs). @@ -99,14 +99,14 @@ library(rats) 2. Generic bootstrapped abundance estimates. 3. Generic abundance estimates. -1. From a [Sleuth](http://pachterlab.github.io/sleuth/) object `rats` extracts the bootstrapped abundance estimates. See the +From a [Sleuth](http://pachterlab.github.io/sleuth/) object `rats` extracts the bootstrapped abundance estimates. See the [introduction to Sleuth](http://rawgit.com/pachterlab/sleuth/master/inst/doc/intro.html) pages for details on how to load the transcript abundance estimate data from [Kallisto](http://pachterlab.github.io/kallisto/) into a Sleuth object, and see the [wasabi](http://github.com/COMBINE-lab/wasabi) tool for how to load the transcript abundance estimate data from [Sailfish](http://github.com/kingsfordgroup/sailfish) or [Salmon](https://github.com/COMBINE-lab/salmon) into a sleuth object. -2. Bootstrapped abundance estimates obtained by other means can be input as `list`s of `data.table`s. Two lists are needed, one per condition. +Bootstrapped abundance estimates obtained by other means can be input as `list`s of `data.table`s. Two lists are needed, one per condition. Each data table should contain the transcript identifiers in the first column, followed by columns containing the estimates from the bootstrap iterations: @@ -125,24 +125,9 @@ head(sim_boot_data()[[2]][[1]]) ## 6: 1B1C.2 52 55 50 ``` -3. Generic abundance estimates, without bootstrapping information, can be input simply as two `data.table`s, one per condition. The first column should -contain the transcript identifiers, followed by columns listing the abundance per sample: - - -```r -# Show the first rows of the table corresponding to one condition, from simulated data. -head(sim_count_data()[[2]]) -``` - -``` -## target_id V1 V2 V3 -## 1: NIB.1 0 0 0 -## 2: 1A1N-2 20 21 18 -## 3: 1D1C:one 0 0 0 -## 4: 1D1C:two 76 80 72 -## 5: 1B1C.1 0 0 0 -## 6: 1B1C.2 52 55 50 -``` +Generic abundance estimates, without bootstrapping information, can be input simply as two `data.table`s, one per condition. The first column should +contain the transcript identifiers, followed by columns listing the abundance per sample. The format of each table is identical to the one shown above, +only the meaning of the numbers changes. **Regardless of data format**, `rats` also needs an annotation `data.frame` that matches transcript identifiers to gene identifiers. This looks like this: @@ -347,6 +332,18 @@ print( ids[["dtu-genes"]] ) ## Output structure +The output of `rats` is a list containing 3 elements: + + +```r +print( names(mydtu) ) +``` + +``` +## [1] "Parameters" "Genes" "Transcripts" +``` + + ### Parameters `Parameters` is a list that contains information about the data and the settings. @@ -578,22 +575,22 @@ print( mydtu$Genes ) ## 3: NA NA NA NA NA ## 4: NA NA NA NA NA ## 5: NA NA NA NA NA -## 6: 0.83 0.002297371 0.0006862716 0.002867174 0.0009853328 +## 6: 0.82 0.002913862 0.0009343038 0.004617752 0.001553497 ## 7: NA NA NA NA NA -## 8: 1.00 0.000000000 0.0000000000 0.000000000 0.0000000000 +## 8: 1.00 0.000000000 0.0000000000 0.000000000 0.000000000 ## 9: NA NA NA NA NA -## 10: 0.00 0.784661756 0.7036691880 0.139088825 0.1925920036 +## 10: 0.00 0.776276584 0.6948216461 0.142400556 0.196181482 ## boot_minAB boot_minBA boot_maxAB boot_maxBA boot_na ## 1: NA NA NA NA NA ## 2: NA NA NA NA NA ## 3: NA NA NA NA NA ## 4: NA NA NA NA NA ## 5: NA NA NA NA NA -## 6: 5.030128e-05 1.550451e-05 0.01321885 0.00480727 0 +## 6: 5.030128e-05 1.550451e-05 0.0154553 0.00480727 0 ## 7: NA NA NA NA NA -## 8: 0.000000e+00 0.000000e+00 0.00000000 0.00000000 0 +## 8: 0.000000e+00 0.000000e+00 0.0000000 0.00000000 0 ## 9: NA NA NA NA NA -## 10: 5.143483e-01 3.311102e-01 0.99150834 0.98871978 0 +## 10: 5.143483e-01 3.311102e-01 0.9915083 0.98871978 0 ``` There are 10 genes in the annotation used. Here are some possible scenarios: @@ -713,19 +710,19 @@ print( mydtu$Transcripts ) ## 6: NA NA NA NA NA NA ## 7: NA NA NA NA NA NA ## 8: NA NA NA NA NA NA -## 9: TRUE 0.73 3.420637e-02 2.555941e-02 6.605471e-03 1.095659e-01 -## 10: TRUE 0.73 3.420637e-02 2.555941e-02 6.605471e-03 1.095659e-01 +## 9: TRUE 0.71 3.517732e-02 3.203049e-02 6.605471e-03 1.095659e-01 +## 10: TRUE 0.71 3.517732e-02 3.203049e-02 6.605471e-03 1.095659e-01 ## 11: NA NA NA NA NA NA -## 12: FALSE 0.09 5.871506e-01 3.743486e-01 1.380439e-02 1.000000e+00 -## 13: TRUE 1.00 2.253061e-78 4.627819e-78 5.067939e-84 2.129120e-77 -## 14: TRUE 1.00 7.211996e-49 1.620455e-48 6.413781e-53 6.559376e-48 -## 15: TRUE 0.00 1.658158e-21 2.826097e-21 1.066067e-24 8.294307e-21 -## 16: TRUE 1.00 1.147936e-44 1.666157e-44 1.516132e-46 7.296158e-44 +## 12: FALSE 0.02 6.565423e-01 3.300701e-01 1.380439e-02 1.000000e+00 +## 13: TRUE 1.00 1.754386e-78 4.381734e-78 5.067939e-84 2.129120e-77 +## 14: TRUE 1.00 3.094905e-49 7.881022e-49 6.413781e-53 6.559376e-48 +## 15: TRUE 0.00 1.950232e-21 3.008824e-21 1.066067e-24 8.294307e-21 +## 16: TRUE 1.00 1.372119e-44 1.970160e-44 1.516132e-46 7.296158e-44 ## 17: NA NA NA NA NA NA -## 18: FALSE 0.00 7.338926e-01 1.546231e-01 3.567434e-01 9.643718e-01 +## 18: FALSE 0.00 7.186030e-01 1.667606e-01 3.567434e-01 9.643718e-01 ## 19: NA NA NA NA NA NA -## 20: FALSE 0.00 9.501605e-01 6.529350e-02 7.948790e-01 1.000000e+00 -## 21: FALSE 0.00 9.501605e-01 6.529350e-02 7.948790e-01 1.000000e+00 +## 20: FALSE 0.00 9.408455e-01 7.414637e-02 7.948790e-01 1.000000e+00 +## 21: FALSE 0.00 9.408455e-01 7.414637e-02 7.948790e-01 1.000000e+00 ## sig boot_freq boot_mean boot_stdev boot_min boot_max ## boot_na ## 1: NA diff --git a/vignettes/tutorial_files/figure-html/unnamed-chunk-25-1.png b/vignettes/tutorial_files/figure-html/unnamed-chunk-25-1.png index 8acf444..bdf0dc0 100644 Binary files a/vignettes/tutorial_files/figure-html/unnamed-chunk-25-1.png and b/vignettes/tutorial_files/figure-html/unnamed-chunk-25-1.png differ diff --git a/vignettes/tutorial_files/figure-html/unnamed-chunk-26-1.png b/vignettes/tutorial_files/figure-html/unnamed-chunk-26-1.png index ddbde80..8cd57ad 100644 Binary files a/vignettes/tutorial_files/figure-html/unnamed-chunk-26-1.png and b/vignettes/tutorial_files/figure-html/unnamed-chunk-26-1.png differ