---
title: 'satuRn - vignette'
author: "Jeroen Gilis"
bibliography: satuRn.bib
date: "30/11/2020"
output:
rmarkdown::html_document:
toc: true
toc_depth: 3
vignette: >
%\VignetteIndexEntry{satuRn - vignette}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, echo = FALSE}
library(knitr)
```
# Introduction
`satuRn` is an R package to perform differential transcript usage analyses in
bulk and single-cell transcriptomics datasets.
The package has three main functions.
1. The first function, `fitDTU`, is used to model transcript usage profiles by
means of a quasi-binomial generalized linear model.
2. Second, the `testDTU` function tests for differential usage of transcripts
between certain groups of interest (e.g. different treatment groups or
cell types).
3. Finally, the `plotDTU` can be used to visualize the usage profiles of
selected transcripts in different groups of interest.
All details about the `satuRn` model and statistical tests are described
in our preprint [@Gilis2021].
In this vignette, we analyze a small subset of the data from [@Tasic2018].
More specifically, an expression matrix and the corresponding metadata of
the subset data has been provided with the `satuRn` package. We will adopt
this dataset to showcase the different functionalities of `satuRn`.
# Package installation
`satuRn` can be installed from Bioconductor with:
```{r, eval=FALSE}
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("satuRn")
```
# Load libraries
```{r, message=FALSE, warning=FALSE}
library(satuRn)
library(AnnotationHub)
library(ensembldb)
library(edgeR)
library(SummarizedExperiment)
library(ggplot2)
library(DEXSeq)
library(stageR)
```
# Load data
The following data corresponds to a small subset of the dataset
from [@Tasic2018] and is readily available from the `satuRn` package.
To check how the subset was generate, please check `?Tasic_counts_vignette`.
```{r}
data(Tasic_counts_vignette) # transcript expression matrix
data(Tasic_metadata_vignette) # metadata
```
# Data pre-processing
We start the analysis from scratch, in order to additionally showcase some of
the prerequisite steps for performing a DTU analysis.
# Import transcript information
First, we need an object that links transcripts to their corresponding genes.
We suggest using the BioConductor R packages `AnnotationHub` and `ensembldb`
for this purpose.
```{r, message=FALSE, warning=FALSE}
ah <- AnnotationHub() # load the annotation resource.
all <- query(ah, "EnsDb") # query for all available EnsDb databases
ahEdb <- all[["AH75036"]] # for Mus musculus (choose correct release date)
txs <- transcripts(ahEdb)
```
## Data wrangling
Next, we perform some data wrangling steps to get the data in a format that is
suited for satuRn. First, we create a `DataFrame` or `Matrix` linking
transcripts to their corresponding genes.
! Important: `satuRn` is implemented such that the columns with transcript
identifiers is names `isoform_id`, while the column containing gene identifiers
should be named `gene_id`. In addition, following chunk removes transcripts
that are the only isoform expressed of a certain gene, as they cannot be used
in a DTU analysis.
```{r}
# Get the transcript information in correct format
txInfo <- as.data.frame(matrix(data = NA, nrow = length(txs), ncol = 2))
colnames(txInfo) <- c("isoform_id", "gene_id")
txInfo$isoform_id <- txs$tx_id
txInfo$gene_id <- txs$gene_id
rownames(txInfo) <- txInfo$isoform_id
# Remove transcripts that are the only isoform expressed of a certain gene
rownames(Tasic_counts_vignette) <- sub("\\..*", "",
rownames(Tasic_counts_vignette))
# remove transcript version identifiers
txInfo <- txInfo[txInfo$isoform_id %in% rownames(Tasic_counts_vignette), ]
txInfo <- subset(txInfo,
duplicated(gene_id) | duplicated(gene_id, fromLast = TRUE))
Tasic_counts_vignette <- Tasic_counts_vignette[which(
rownames(Tasic_counts_vignette) %in% txInfo$isoform_id), ]
```
## Filtering
Here we perform some feature-level filtering. For this task, we adopt
the filtering criterion that is implemented in the R package `edgeR`.
Alternatively, one could adopt the `dmFilter` criterion from the `DRIMSeq` R
package, which provides a more stringent filtering when both methods are run
in default settings. After filtering, we again remove transcripts that are
the only isoform expressed of a certain gene.
```{r}
filter_edgeR <- filterByExpr(Tasic_counts_vignette,
design = NULL,
group = Tasic_metadata_vignette$brain_region,
lib.size = NULL,
min.count = 10,
min.total.count = 30,
large.n = 20,
min.prop = 0.7
) # more stringent than default to reduce run time of the vignette
table(filter_edgeR)
Tasic_counts_vignette <- Tasic_counts_vignette[filter_edgeR, ]
# Update txInfo according to the filtering procedure
txInfo <- txInfo[which(
txInfo$isoform_id %in% rownames(Tasic_counts_vignette)), ]
# remove txs that are the only isoform expressed within a gene (after filtering)
txInfo <- subset(txInfo,
duplicated(gene_id) | duplicated(gene_id, fromLast = TRUE))
Tasic_counts_vignette <- Tasic_counts_vignette[which(rownames(
Tasic_counts_vignette) %in% txInfo$isoform_id), ]
# satuRn requires the transcripts in the rowData and
# the transcripts in the count matrix to be in the same order.
txInfo <- txInfo[match(rownames(Tasic_counts_vignette), txInfo$isoform_id), ]
```
## Create a design matrix
Here we set up the design matrix of the experiment. The subset of the dataset
from [@Tasic2018] contains cells of several different cell types
(variable `cluster`) in two different areas of the mouse neocortex
(variable `brain_region`). As such, we can model the data with
a factorial design, i.e. by generating a new variable `group` that encompasses
all different cell type - brain region combinations.
```{r}
Tasic_metadata_vignette$group <- paste(Tasic_metadata_vignette$brain_region,
Tasic_metadata_vignette$cluster,
sep = ".")
```
## Generate SummarizedExperiment
All three main functions of `satuRn` require a `SummarizedExperiment` object as
an input class. See the SummarizedExperiment vignette [@SummarizedExperiment]
for more information on this object class.
Do not forget to include the design matrix formula (see above) to
the SummarizedExperiment as indicated below. As such, the object contains all
the information required for the downstream DTU analysis.
```{r, message=FALSE}
sumExp <- SummarizedExperiment::SummarizedExperiment(
assays = list(counts = Tasic_counts_vignette),
colData = Tasic_metadata_vignette,
rowData = txInfo
)
# specify design formula from colData
metadata(sumExp)$formula <- ~ 0 + as.factor(colData(sumExp)$group)
sumExp
```
# Fit quasi-binomial generalized linear models models
The `fitDTU` function of `satuRn` is used to model transcript usage in different
groups of samples or cells. Here we adopt the default settings of the function.
Without parallelized execution, this code runs for approximately 15 seconds
on a 2018 macbook pro laptop.
```{r}
system.time({
sumExp <- satuRn::fitDTU(
object = sumExp,
formula = ~ 0 + group,
parallel = FALSE,
BPPARAM = BiocParallel::bpparam(),
verbose = TRUE
)
})
```
The resulting model fits are now saved into the `rowData` of
our SummarizedExperiment object under the name `fitDTUModels`.
These models can be accessed as follows:
```{r}
rowData(sumExp)[["fitDTUModels"]]$"ENSMUST00000037739"
```
The models are instances of the `StatModel` class as defined in
the `satuRn` package. These contain all relevant information for
the downstream analysis. For more details, read the StatModel documentation
with ?satuRn::`StatModel-class`.
# Test for DTU
Here we test for differential transcript usage between select groups
of interest. In this example, the groups of interest are the three different
cell types that are present in the dataset associated with this vignette.
## Set up contrast matrix
First, we set up a contrast matrix. This allows us to test for differential
transcript usage between groups of interest. The `group` factor in this
toy example contains three levels; (1) ALM.L5_IT_ALM_Tmem163_Dmrtb1,
(2) ALM.L5_IT_ALM_Tnc, (3) VISp.L5_IT_VISp_Hsd11b1_Endou.
Here we show to assess DTU between cells of the groups 1 and 3
and between cells of groups 2 and 3.
The contrast matrix can be constructed manually;
```{r}
group <- as.factor(Tasic_metadata_vignette$group)
design <- model.matrix(~ 0 + group) # construct design matrix
colnames(design) <- levels(group)
L <- matrix(0, ncol = 2, nrow = ncol(design)) # initialize contrast matrix
rownames(L) <- colnames(design)
colnames(L) <- c("Contrast1", "Contrast2")
L[c("VISp.L5_IT_VISp_Hsd11b1_Endou","ALM.L5_IT_ALM_Tnc"),1] <-c(1,-1)
L[c("VISp.L5_IT_VISp_Hsd11b1_Endou","ALM.L5_IT_ALM_Tmem163_Dmrtb1"),2] <-c(1,-1)
L # contrast matrix
```
This can also be done automatically with the `makeContrasts` function of
the `limma` R package.
```{r}
group <- as.factor(Tasic_metadata_vignette$group)
design <- model.matrix(~ 0 + group) # construct design matrix
colnames(design) <- levels(group)
L <- limma::makeContrasts(
Contrast1 = VISp.L5_IT_VISp_Hsd11b1_Endou - ALM.L5_IT_ALM_Tnc,
Contrast2 = VISp.L5_IT_VISp_Hsd11b1_Endou - ALM.L5_IT_ALM_Tmem163_Dmrtb1,
levels = design
)
L # contrast matrix
```
## Perform the test
Next we can perform differential usage testing using `testDTU`. We again adopt
default settings. For more information on the parameter settings, please comsult
the help file of the testDTU function.
```{r}
sumExp <- satuRn::testDTU(
object = sumExp,
contrasts = L,
plot = FALSE,
sort = FALSE
)
```
The test results are now saved into the `rowData` of our SummarizedExperiment
object under the name `fitDTUResult_` followed by the name of the contrast
of interest (i.e. the column names of the contrast matrix).
The results can be accessed as follows:
```{r}
head(rowData(sumExp)[["fitDTUResult_Contrast1"]]) # first contrast
```
```{r}
head(rowData(sumExp)[["fitDTUResult_Contrast2"]]) # second contrast
```
# Visualize DTU
Finally, we may visualize the usage of select transcripts in select groups
of interest.
```{r, warning=FALSE}
group1 <- rownames(colData(sumExp))[colData(sumExp)$group ==
"VISp.L5_IT_VISp_Hsd11b1_Endou"]
group2 <- rownames(colData(sumExp))[colData(sumExp)$group ==
"ALM.L5_IT_ALM_Tnc"]
plots <- satuRn::plotDTU(
object = sumExp,
contrast = "Contrast1",
groups = list(group1, group2),
coefficients = list(c(0, 0, 1), c(0, 1, 0)),
summaryStat = "model",
transcripts = c("ENSMUST00000081554",
"ENSMUST00000195963",
"ENSMUST00000132062"),
genes = NULL,
top.n = 6
)
# to have same layout as in our paper
for (i in seq_along(plots)) {
current_plot <- plots[[i]] +
scale_fill_manual(labels = c("VISp", "ALM"), values = c("royalblue4",
"firebrick")) +
scale_x_discrete(labels = c("Hsd11b1_Endou", "Tnc"))
print(current_plot)
}
```
# Two-stage testing procedure
`satuRn` returns transcript-level p-values for each of the specified contrasts.
While we have shown that `satuRn` is able to adequately control the false
discovery rate (FDR) at the transcript level [@Gilis2021],
[@VandenBerge2017] argued that it is often desirable to control the FDR at
the gene level. This boosts statistical power and eases downstream
biological interpretation and validation, which typically occur at
the gene level.
To this end, [@VandenBerge2017] developed a testing procedure that is
implemented in the BioConductor R package `stageR`. The procedure consists of
two stages; a screening stage and a confirmation stage.
In the screening stage, gene-level FDR-adjusted p-values are computed,
which aggregate the evidence for differential transcript usage over all
transcripts within the gene. Only genes with an FDR below the desired
nominal level are further considered in the second stage.
In the confirmation stage, transcript-level p-values are adjusted for
those genes, using a FWER-controlling method on the FDR-adjusted
significance level.
In its current implementation, `stageR` can only perform stage-wise testing if
only one contrast is of interest in a DTU setting. An analogous correction for
the assessment of multiple contrasts for multiple transcripts per gene
has not yet been implemented.
Below, we demonstrate how the transcript-level p-values for the first contrast
as returned by `satuRn` can be post-processed using `stageR`.
We rely on the `perGeneQValue` function:
```{r, stage-wise testing}
# transcript level p-values from satuRn
pvals <- rowData(sumExp)[["fitDTUResult_Contrast1"]]$empirical_pval
# compute gene level q-values
geneID <- factor(rowData(sumExp)$gene_id)
geneSplit <- split(seq(along = geneID), geneID)
pGene <- sapply(geneSplit, function(i) min(pvals[i]))
pGene[is.na(pGene)] <- 1
theta <- unique(sort(pGene))
# gene-level significance testing
q <- DEXSeq:::perGeneQValueExact(pGene, theta, geneSplit)
qScreen <- rep(NA_real_, length(pGene))
qScreen <- q[match(pGene, theta)]
qScreen <- pmin(1, qScreen)
names(qScreen) <- names(geneSplit)
# prepare stageR input
tx2gene <- as.data.frame(rowData(sumExp)[c("isoform_id", "gene_id")])
colnames(tx2gene) <- c("transcript", "gene")
pConfirmation <- matrix(matrix(pvals),
ncol = 1,
dimnames = list(rownames(tx2gene), "transcript")
)
# create a stageRTx object
stageRObj <- stageR::stageRTx(
pScreen = qScreen,
pConfirmation = pConfirmation,
pScreenAdjusted = TRUE,
tx2gene = tx2gene
)
# perform the two-stage testing procedure
stageRObj <- stageR::stageWiseAdjustment(
object = stageRObj,
method = "dtu",
alpha = 0.05,
allowNA = TRUE
)
# retrieves the adjusted p-values from the stageRTx object
padj <- stageR::getAdjustedPValues(stageRObj,
order = TRUE,
onlySignificantGenes = FALSE
)
head(padj)
```
# Session
```{r}
sessionInfo()
```
# References