qsvaR
Differential expressions analysis requires the ability to normalize
complex datasets. In the case of postmortem brain tissue we are tasked
with removing the effects of bench degradation. The qsvaR package
combines an established method for removing the effects of degradation
from RNA-seq data with easy to use functions. It is the second iteration
of the qSVA framework (Jaffe et al, PNAS,
2017).
The first step in the qsvaR workflow is to create an
RangedSummarizedExperiment
object with the transcripts identified in our qSVA experiment. If you
already have a
RangedSummarizedExperiment
of transcripts we can do this with the getDegTx() function as shown
below.If not this can be generated with the
SPEAQeasy (a RNA-seq
pipeline maintained by our lab) pipeline using the --qsva flag. If you
already have a
RangedSummarizedExperiment
object with transcripts then you do not need to run
SPEAQeasy. This flag
requires a full path to a text file, containing one Ensembl transcript
ID per line for each transcript desired in the final transcripts R
output object (called rse_tx). The sig_transcripts argument in this
package should contain the same Ensembl transcript IDs as the text file
for the --qsva flag.The goal of qsvaR is to provide software that
can remove the effects of bench degradation from RNA-seq data.
Installation Instructions
Get the latest stable R release from CRAN. Then install qsvaR using
from Bioconductor the following code:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("qsvaR")And the development version from GitHub with:
BiocManager::install("LieberInstitute/qsvaR")Example
This is a basic example which shows how to obtain the quality surrogate
variables (qSVs) for the brainseq phase II
dataset. qSVs are essentially
principal components from an rna-seq experiment designed to model bench
degradation. For more on principal components you can read and
introductory article
here.
At the start of this script we will have an
RangedSummarizedExperiment
and a list of all the transcripts found in our degradation study. At the
end we will have a table with differential expression results that is
adjusted for qSVs.
## R packages we'll use
library("qsvaR")
library("limma")library("qsvaR")
## We'll download example data from the BrainSeq Phase II project
## described at http://eqtl.brainseq.org/phase2/.
##
## We'll use BiocFileCache to cache these files so you don't have to download
## them again for other examples.
bfc <- BiocFileCache::BiocFileCache()
rse_file <- BiocFileCache::bfcrpath(
"https://s3.us-east-2.amazonaws.com/libd-brainseq2/rse_tx_unfiltered.Rdata",
x = bfc
)
## Now that we have the data in our computer, we can load it.
load(rse_file, verbose = TRUE)
#> Loading objects:
#> rse_txIn this next step we subset for the transcripts associated with
degradation. These were determined by Joshua M. Stolz et al, 2022. We
have provided three models to choose from. Here the names
"cell_component", "top1500", and "standard" refer to models that
were determined to be effective in removing degradation effects. The
"standard" model involves taking the union of the top 1000 transcripts
associated with degradation from the interaction model and the main
effect model. The "top1500" model is the same as the "standard"
model except the union of the top 1500 genes associated with degradation
is selected. The most effective of our models, "cell_component",
involved deconvolution of the degradation matrix to determine the
proportion of cell types within our studied tissue. These proportions
were then added to our model.matrix() and the union of the top 1000
transcripts in the interaction model, the main effect model, and the
cell proportions model were used to generate this model of qSVs. In this
example we will choose "cell_component" when using the getDegTx()
and select_transcripts() functions.
The above venn diagram shows the overlap between transcripts in each of the previously mentioned models.
## Next we get the degraded transcripts for qSVA from the "cell_component"
## model
DegTx <- getDegTx(rse_tx, type = "cell_component")
## Now we can compute the Principal Components (PCs) of the degraded
## transcripts
pcTx <- getPCs(DegTx, "tpm")Next we use the k_qsvs() function to calculate how many PCs we will
need to account for the variation. A model matrix accounting for
relevant variables should be used. Common variables such as Age, Sex,
Race and Religion are often included in the model. Again we are using
our RangedSummarizedExperiment DegTx as the rse_tx option. Next we
specify the mod with our model.matrix(). model.matrix() creates a
design (or model) matrix, e.g., by expanding factors to a set of dummy
variables (depending on the contrasts) and expanding interactions
similarly. For more information on creating a design matrix for your
experiment see the documentation
here.
Again we use the assayname option to specify the we are using the
tpm assay, where TPM stands for transcripts per million.
## Using a simple statistical model we determine the number of PCs needed (k)
mod <- model.matrix(~ Dx + Age + Sex + Race + Region,
data = colData(rse_tx)
)
k <- k_qsvs(DegTx, mod, "tpm")
print(k)
#> [1] 34Now that we have our PCs and the number we need we can generate our qSVs.
## Obtain the k qSVs
qsvs <- get_qsvs(pcTx, k)
dim(qsvs)
#> [1] 900 34This can be done in one step with our wrapper function qSVA which just
combinds all the previous mentioned functions.
## Example use of the wrapper function qSVA()
qsvs_wrapper <- qSVA(rse_tx = rse_tx, type = "cell_component", mod = mod, assayname = "tpm")
dim(qsvs_wrapper)
#> [1] 900 34Differential Expression
Next we can use a standard limma package approach to do differential
expression on the data. The key here is that we add our qSVs to the
statistical model we use through model.matrix(). Here we input our
Ranged SummarizedExperiment
object and our model.matrix with qSVs. Note here that the
Ranged SummarizedExperiment object is the original object loaded with
the full list of transcripts, not the the one we subsetted for qSVs.
This is because while PCs can be generated from a subset of genes,
differential expression is best done on the full dataset. The expected
output is a sigTx object that shows the results of differential
expression.
library("limma")
## Add the qSVs to our statistical model
mod_qSVA <- cbind(
mod,
qsvs
)
## Extract the transcript expression values and put them in the
## log2(TPM + 1) scale
txExprs <- log2(assays(rse_tx)$tpm + 1)
## Run the standard linear model for differential expression
fitTx <- lmFit(txExprs, mod_qSVA)
eBTx <- eBayes(fitTx)
## Extract the differential expression results
sigTx <- topTable(eBTx,
coef = 2,
p.value = 1, number = nrow(rse_tx)
)
## Explore the top results
head(sigTx)
#> logFC AveExpr t P.Value adj.P.Val
#> ENST00000553142.5 -0.06547988 2.0390889 -5.999145 2.921045e-09 0.0005786386
#> ENST00000552074.5 -0.12911383 2.4347985 -5.370828 1.009549e-07 0.0099992338
#> ENST00000510632.1 0.08994392 0.9073516 4.920042 1.037016e-06 0.0473143146
#> ENST00000530589.1 -0.10297938 2.4271711 -4.918806 1.043399e-06 0.0473143146
#> ENST00000572236.1 -0.05358333 0.6254025 -4.819980 1.697403e-06 0.0473143146
#> ENST00000450454.6 0.08446871 1.0042440 4.816539 1.726143e-06 0.0473143146
#> B
#> ENST00000553142.5 10.200286
#> ENST00000552074.5 6.767821
#> ENST00000510632.1 4.524039
#> ENST00000530589.1 4.518145
#> ENST00000572236.1 4.051142
#> ENST00000450454.6 4.035041Finally, you can compare the resulting t-statistics from your
differential expression model against the degradation time t-statistics
adjusting for the six different brain regions. This type of plot is
called DEqual plot and was shown in the initial qSVA framework paper
(Jaffe et al, PNAS, 2017). We
are really looking for two patterns exemplified here in Figure 1
(cartoon shown earlier). A direct positive correlation with degradation
shown in Figure 1 on the right tells us that there is signal in the data
associated with qSVs. An example of nonconfounded data or data that has
been modeled can be seen in Figure 1 on the right with its lack of
relationship between the x and y variables.
Cartoon showing patterns in DEqual plots
## Generate a DEqual() plot using the model results with qSVs
DEqual(sigTx)
Result of Differential Expression with qSVA normalization.
For comparison, here is the DEqual() plot for the model without qSVs.
## Generate a DEqual() plot using the model results without qSVs
DEqual(topTable(eBayes(lmFit(txExprs, mod)), coef = 2, p.value = 1, number = nrow(rse_tx)))
Result of Differential Expression without qSVA normalization.
In these two DEqual plots we can see that the first is much better. With a correlation of -0.014 we can effectively conclude that we have removed the effects of degradation from the data. In the second plot after modeling for several common variables we still have a correlation of 0.5 with the degradation experiment. This high correlation shows we still have a large amount of signal from degradation in our data potentially confounding our case-control (SCZD vs neurotypical controls) differential expression results.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent