library(BiocStyle) knitr::opts_chunk$set(tidy=FALSE, dev="png", message=FALSE, error=FALSE, warning=TRUE)
Lorena Pantano Harvard TH Chan School of Public Health, Boston, US
library(DEGreport) data(humanGender)
We are going to do a differential expression analysis with edgeR/DESeq2.
We have an object that is coming from the edgeR package.
It contains a gene count matrix
for 85 TSI HapMap individuals, and the gender information. With that, we are
going to apply the glmFit
function or r Biocpkg("DESeq2")
to get genes differentially expressed
between males and females.
library(DESeq2) idx <- c(1:10, 75:85) dds <- DESeqDataSetFromMatrix(assays(humanGender)[[1]][1:1000, idx], colData(humanGender)[idx,], design=~group) dds <- DESeq(dds) res <- results(dds)
We need to extract the experiment design data.frame where the condition is Male or Female.
counts <- counts(dds, normalized = TRUE) design <- as.data.frame(colData(dds))
A main assumption in library size factor calculation of edgeR and DESeq2 (and others) is that the majority of genes remain unchanged. Plotting the distribution of gene ratios between each gene and the average gene can show how true this is. Not super useful for many samples because the plot becomes crowed.
degCheckFactors(counts[, 1:6])
p-value distribution gives an idea on how well you model is capturing the input data and as well whether it could be some problem for some set of genes. In general, you expect to have a flat distribution with peaks at 0 and 1. In this case, we add the mean count information to check if any set of genes are enriched in any specific p-value range.
Variation (dispersion) and average expression relationship shouldn't be a factor among the differentially expressed genes. When plotting average mean and standard deviation, significant genes should be randomly distributed.
In this case, it would be good to look at the ones that are totally outside the expected correlation.
You can put this tree plots together using degQC
.
degQC(counts, design[["group"]], pvalue = res[["pvalue"]])
Another important analysis to do if you have covariates is to calculate the correlation between PCs from PCA analysis to different variables you may think are affecting the gene expression. This is a toy example of how the function works with raw data, where clearly library size correlates with some of the PCs.
resCov <- degCovariates(log2(counts(dds)+0.5), colData(dds))
Also, the correlation among covariates and metrics from the analysis can be tested. This is useful when the study has multiple variables, like in clinical trials. The following code will return a correlation table, and plot the correlation heatmap for all the covariates and metrics in a table.
cor <- degCorCov(colData(dds)) names(cor)
A quick HTML report can be created with createReport
to show whether
a DE analysis is biased to a particular set of genes. It contains the output
of degQC,
degVB and degMB
.
createReport(colData(dds)[["group"]], counts(dds, normalized = TRUE), row.names(res)[1:20], res[["pvalue"]], path = "~/Downloads")
Here, we show some useful plots for differentially expressed genes.
DEGSet
is a class to store the DE results like the one from
results
function. r Biocpkg("DESeq2")
offers multiple way to ask for
contrasts/coefficients. With degComps
is easy to get multiple
results in a single object:
degs <- degComps(dds, combs = "group", contrast = list("group_Male_vs_Female", c("group", "Female", "Male"))) names(degs)
degs
contains 3 elements, one for each contrast/coefficient asked for.
It contains the results output in the element raw
and the output of
lfcShrink
in the element shrunken
.
To obtain the results from one of them, use the method dge
:
deg(degs[[1]])
By default it would output the shrunken
table always, as defined by
degDefault
, that contains the default table to get.
To get the original results table, use the parameter as this:
deg(degs[[1]], "raw", "tibble")
Note that the format of the output can be changed to tibble, or data.frame with
a third parameter tidy
.
The table will be always sorted by padj.
And easy way to get significant genes is:
significants(degs[[1]], fc = 0, fdr = 0.05)
This function can be used as well for a list of comparisons:
significants(degs, fc = 0, fdr = 0.05)
And it can returns the full table for a list:
significants(degs, fc = 0, fdr = 0.05, full = TRUE)
Since log2FoldChange are shrunken, the method for DEGSet class now can plot these changes as follow:
degMA(degs[[1]], diff = 2, limit = 3)
The blue arrows indicate how foldchange is affected by this new feature.
As well, it can plot the original MA plot:
degMA(degs[[1]], diff = 2, limit = 3, raw = TRUE)
or the correlation between the original log2FoldChange and the new ones:
degMA(degs[[1]], limit = 3, correlation = TRUE)
Volcano plot using the output of r Biocpkg("DESeq2")
. It mainly needs data.frame with
two columns (logFC and pVal). Specific genes can be plot using the option
plot\_text
(subset of the previous data.frame with a 3rd column to be used
to plot the gene name).
res[["id"]] <- row.names(res) show <- as.data.frame(res[1:10, c("log2FoldChange", "padj", "id")]) degVolcano(res[,c("log2FoldChange", "padj")], plot_text = show)
Note that the function is compatible with DEGset. Using
degVolcano(degs[[1]])
is valid.
Plot top genes coloring by group. Very useful for experiments with nested
groups. xs
can be time
or WT
/KO
, and group
can be treated
/untreated
.
Another classification can be added, like batch
that will plot points
with different shapes.
degPlot(dds = dds, res = res, n = 6, xs = "group")
Another option for plotting genes in a wide format:
degPlotWide(dds, rownames(dds)[1:5], group="group")
Markers can be used to show whether different conditions are enriched in different markers. For instance, in this example, Females and Males show different total expression for chromosome X/Y markers
data(geneInfo) degSignature(humanGender, geneInfo, group = "group")
If you have a DESeq2 object, you can use degResults to create a full report with markdown code inserted, including figures and table with top de-regulated genes, GO enrichment analysis and heatmaps and PCA plots. If you set \Rcode{path_results}, different files will be saved there.
resreport <- degResults(dds = dds, name = "test", org = NULL, do_go = FALSE, group = "group", xs = "group", path_results = NULL)
Browsing gene expression can help to validate results or select some gene for downstream analysis. Run the following lines if you want to visualize your expression values by condition:
degObj(counts, design, "degObj.rda") library(shiny) shiny::runGitHub("lpantano/shiny", subdir="expression")
In this section, we show how to detect pattern of expression. Mainly useful when
data is a time course experiment. degPatterns
needs a expression
matrix, the design experiment and the column used to group samples.
ma = assay(rlog(dds))[row.names(res)[1:100],] res <- degPatterns(ma, design, time = "group")
This section shows some useful functions during DEG analysis.
degFilter
helps to filter genes with a minimum read count by group.
cat("gene in original count matrix: 1000") filter_count <- degFilter(counts(dds), design, "group", min=1, minreads = 50) cat("gene in final count matrix", nrow(filter_count))
This functions allows you to create colors for metadata columns to be used as annotation for columns in a heatmap figure.
library(ComplexHeatmap) th <- HeatmapAnnotation(df = colData(dds), col = degColors(colData(dds), TRUE)) Heatmap(log2(counts(dds) + 0.5)[1:10,], top_annotation = th) library(pheatmap) pheatmap(log2(counts(dds) + 0.5)[1:10,], annotation_col = as.data.frame(colData(dds))[,1:4], annotation_colors = degColors(colData(dds)[1:4], con_values = c("white", "red") ) )
sessionInfo()
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