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<!-- is generated from README.Rmd. Please edit that file --> # qsvaR <!-- badges: start --> [![Lifecycle: stable](]( [![Bioc release status](]( [![Bioc devel status](]( [![Bioc downloads rank](]( [![Bioc support](]( [![Bioc history](]( [![Bioc last commit](]( [![Bioc dependencies](]( [![Codecov test coverage](]( [![R build status](]( [![GitHub issues](]( [![GitHub pulls](]( [![DOI](]( <!-- badges: end --> 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: ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("qsvaR") ``` And the development version from GitHub with: ``` r 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](,eliminate%20ones%20that%20do%20not). 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 ## R packages we'll use library("qsvaR") library("limma") ``` ``` r library("qsvaR") ## We'll download example data from the BrainSeq Phase II project ## described at ## ## 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( "", x = bfc ) ## Now that we have the data in our computer, we can load it. load(rse_file, verbose = TRUE) #> Loading objects: #> rse_tx ``` In 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. <div class="figure"> <img src="./man/figures/transcripts_venn_diagramm.png" alt="The above venn diagram shows the overlap between transcripts in each of the previously mentioned models." width="100%" /> <p class="caption"> The above venn diagram shows the overlap between transcripts in each of the previously mentioned models. </p> </div> ``` r ## 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*. ``` r ## 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] 34 ``` Now that we have our PCs and the number we need we can generate our qSVs. ``` r ## Obtain the k qSVs qsvs <- get_qsvs(pcTx, k) dim(qsvs) #> [1] 900 34 ``` This can be done in one step with our wrapper function `qSVA` which just combinds all the previous mentioned functions. ``` r ## 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 34 ``` ## Differential 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. ``` r 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 4.035041 ``` Finally, 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. <div class="figure"> <img src="./man/figures/DEqual_example.png" alt="Cartoon showing patterns in DEqual plots" width="100%" /> <p class="caption"> Cartoon showing patterns in DEqual plots </p> </div> ``` r ## Generate a DEqual() plot using the model results with qSVs DEqual(sigTx) ``` <div class="figure"> <img src="man/figures/README-DEqual-1.png" alt="Result of Differential Expression with qSVA normalization." width="100%" /> <p class="caption"> Result of Differential Expression with qSVA normalization. </p> </div> For comparison, here is the `DEqual()` plot for the model without qSVs. ``` r ## 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))) ``` <div class="figure"> <img src="man/figures/README-DEqual-no-qSVs-1.png" alt="Result of Differential Expression without qSVA normalization." width="100%" /> <p class="caption"> Result of Differential Expression without qSVA normalization. </p> </div> 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.