Before I compute the principal components, I use the vstįunction to compute a variance stabilizing transformation (VST) How to examine differences across the most variable genes using a PCA 5), xlim= c( 5, 1e5))īelow I will continue with per-gene analysis, but first, I demonstrate Gene-est estimate is used, without information from other genes. Relative to the rest of the dataset, and in these cases, only the The blueĬircles at the top of the plot represent genes with high dispersion Sharing information across genes ( final, blue points). There are two per-gene estimates, an initial estimate which looks onlyĪt the data for a single gene ( gene-est, black points), and a finalĮstimate that incorporates information from each gene, as well as So that the differences affecting counts across samples are minimized. Note that many of the plots in DESeq2 refer to “normalizedĬounts” here this just implies scaling the counts by the size factor, \(\log_\) inīelow I plot the estimates over the mean of scaled counts for each The red line is a smooth curve through the log ratios (here 2), xlab= "log10 proportion (geometric mean)", ylab= "log10 fold change") abline( h= 0, col= rgb( 0, 0, 1. Library(rafalib) maplot( log10(p), log10(p), n= nrow(p), cex=. For more on how these estimates are computed, consult The abundance assay of the gse object containsĮstimates of the proportions of the molecules, in transcripts per The length of the feature) does not estimate the proportion of While later we will discuss a robust estimator for sequencing depthīefore I create the proportions, it’s important to remember that,īecause genes with longer transcripts will produce more cDNAįragments, the proportion estimated here (without taking into account Total count, colSums(cts) to divide the counts for each sample, Or more, to cut down on the number of points to plot. I willįirst subset to only those genes where both samples have a count of 5 I will make a plotĮxamining the proportion of the total count for each gene. Group and one from the OCT4 treated group. Let us first consider just two samples, one from the OCT4 untreated Thousands of genes, although there is inevitably a range of sequencingĬs <- colSums( assay(gse, "counts")) hist(cs / 1e6, col= "grey", border= "white", main= "", xlab= "column sums (per million)") Of fragments per sample, which are distributed across tens of Typical mammalian RNA-seq experiment, we might expect tens of millions Of reads) that have been assigned to the genes for each sample. RNA-seq analysis, but mostly for introducing the reader to some basicįirst, it’s useful to explore the varying number of fragments (pairs Includes code and plots that are not typically performed during Note that the first section of this chapter Perform per-gene testing for differential expression using theĭESeq2 package, and multiple test correction, including the IHW Offer for log ratio comparisons between samples. I will note the varying precision that the counts I will begin by investigating the estimated counts that were importedįrom the Salmon software, and comparing these counts across and Proposed by ( 18) in the DSS Bioconductor package. The approach taken byĭESeq2 for estimation of dispersion is similar to the method Some other popular Bioconductor packages for RNA-seq analysis include theĮdgeR package ( 15, 16) and the limma-voom Samples, and perform differential testing per gene ( 14). Scaling factors, estimate biological dispersion within groups of I will then use the DESeq2 package to calculate Often used to analyze RNA-seq data, in particular gene-level count In this section, I will discuss the statistical models that are
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