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library(BiocParallel) register(SerialParam())
This vignette describes GESECA (gene set co-regulation analysis): a method to identify gene sets that have high gene correlation. We will show how GESECA can be used to find regulated pathways in multi-conditional data, where there is no obvious contrast that can be used to rank genes for GSEA analysis. As examples we will consider a time course microarray experiment and a spatial transcriptomics dataset.
GESECA takes as an input:
Note: genes identifier type should be the same for both elements of P and for row names of matrix E.
By default, GESECA method performs centering for rows of the matrix E. So, after that, the gene values are assumed to have zero mean. Then for each gene set p in P let us introduce the gene set score in the following form:
score <- sum(colSums(E[p, ])**2) / length(p)
This score was inspired by the variance of principal components from the principal component analysis (PCA). Therefore, the given score can be viewed in terms of explained variance by the gene set p. Geometrically, this can be considered as an embedding of samples into a one-dimensional space, given by a unit vector in which nonzero positions correspond to genes from gene set p.
In the case of row-centered matrix E the variance of highly correlated genes is summed up to a higher score. While the genes that are not correlated cancel each other and the total gene set variance is low. See the toy example:
{width=90%}
Another major feature of the proposed score is that it does not require an explicit sample annotation or a contrast. As the result, GESECA can be applied to various types of sequencing technologies: RNA-seq, single-cell sequencing, spatial RNA-seq, etc.
To assess statistical significance for a given gene set p we calculate
an empirical P-value by using gene permutations.
The definition of the P-value is given by the following expression:
[
\mathrm{P} \left(\text{random score} \geqslant \text{score of p} \right).
]
The estimation of the given P-value is done by sampling random gene sets
with the same size as p from the row names of matrix E.
In practice, the theoretical P-value can be extremely small,
so we use the adaptive multilevel Markov Chain Monte Carlo scheme,
that we used previously in fgseaMultilevel
procedure.
For more details, see the preprint.
In the first example we will consider a time course data of Th2 activation from the dataset GSE200250.
First, let prepare the dataset. We load it from Gene Expression Omnibus, apply log and quantile normalization and filter lowly expressed genes.
library(GEOquery) library(limma) gse200250 <- getGEO("GSE200250", AnnotGPL = TRUE)[[1]] es <- gse200250 es <- es[, grep("Th2_", es$title)] es$time <- as.numeric(gsub(" hours", "", es$`time point:ch1`)) es <- es[, order(es$time)] exprs(es) <- normalizeBetweenArrays(log2(exprs(es)), method="quantile") es <- es[order(rowMeans(exprs(es)), decreasing=TRUE), ] es <- es[!duplicated(fData(es)$`Gene ID`), ] rownames(es) <- fData(es)$`Gene ID` es <- es[!grepl("///", rownames(es)), ] es <- es[rownames(es) != "", ] fData(es) <- fData(es)[, c("ID", "Gene ID", "Gene symbol")] es <- es[head(order(rowMeans(exprs(es)), decreasing=TRUE), 12000), ] head(exprs(es))
Then we obtain the pathway list. Here we use Hallmarks collection from MSigDB database.
library(msigdbr) pathwaysDF <- msigdbr("mouse", category="H") pathways <- split(as.character(pathwaysDF$entrez_gene), pathwaysDF$gs_name)
Now we can run GESECA analysis:
library(fgsea) set.seed(1) gesecaRes <- geseca(pathways, exprs(es), minSize = 15, maxSize = 500)
The resulting table contain GESECA scores and the corresponding P-values:
head(gesecaRes, 10)
We can plot gene expression profile of HALLMARK_E2F_TARGETS pathway and see that these genes are strongly activated at 24 hours time point:
plotCoregulationProfile(pathway=pathways[["HALLMARK_E2F_TARGETS"]], E=exprs(es), titles = es$title, conditions=es$`time point:ch1`)
Hypoxia genes have slightly different profile, getting activated around 48 hours:
plotCoregulationProfile(pathway=pathways[["HALLMARK_HYPOXIA"]], E=exprs(es), titles = es$title, conditions=es$`time point:ch1`)
To get an overview of the top pathway patterns we can use plotGesecaTable
function:
plotGesecaTable(gesecaRes |> head(10), pathways, E=exprs(es), titles = es$title)
When the expression matrix contains many samples, a PCA-reduced expression matrix
can be used instead of the full matrix to improve the performance.
Let reduce the sample space from r ncol(es)
to 10 dimensions, preserving as much gene variation as possible.
E <- t(base::scale(t(exprs(es)), scale=FALSE)) pcaRev <- prcomp(E, center=FALSE) Ered <- pcaRev$x[, 1:10] dim(Ered)
Now we can run GESECA on the reduced matrix, however we need to disable automatic centering, as we already have done it before the reduction.
set.seed(1) gesecaResRed <- geseca(pathways, Ered, minSize = 15, maxSize = 500, center=FALSE) head(gesecaResRed, 10)
The scores and P-values are similar to the ones we obtained for the full matrix.
library(ggplot2) ggplot(data=merge(gesecaRes[, list(pathway, logPvalFull=-log10(pval))], gesecaResRed[, list(pathway, logPvalRed=-log10(pval))])) + geom_point(aes(x=logPvalFull, y=logPvalRed)) + coord_fixed() + theme_classic()
Les us load neccesary libraries. We a going to use Seurat
package for working with singe cell data.
library(Seurat)
As an example dataset we will use GSE116240 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116240). The dataset features single cell RNA sequencing of aortic CD45+ cells and foam cells from atherosclerotic aorta and is extensively described in the corresponding publication (https://pubmed.ncbi.nlm.nih.gov/30359200/). We also thank the authors for providing the corresponding Seurat object used for the publication.
obj <- readRDS(url("https://alserglab.wustl.edu/files/fgsea/GSE116240.rds")) obj newIds <- c("0"="Adventitial MF", "3"="Adventitial MF", "5"="Adventitial MF", "1"="Intimal non-foamy MF", "2"="Intimal non-foamy MF", "4"="Intimal foamy MF", "7"="ISG+ MF", "8"="Proliferating cells", "9"="T-cells", "6"="cDC1", "10"="cDC2", "11"="Non-immune cells") obj <- RenameIdents(obj, newIds) DimPlot(obj) + ggplot2::coord_fixed()
We apply an appropriate normalization (note that we are using 10000 genes, which will be later used as a gene universe for the analysis):
obj <- SCTransform(obj, verbose = FALSE, variable.features.n = 10000)
To speed up the analysis, instead of using the full transformed
gene expression matrix, we will consider only its first
principal components. Note that a "reverse" PCA should be done:
the principal components should correspond to linear combinations
of the cells, not linear combinations of the genes as in "normal" PCA.
By default SCTransform
returns centered gene expression, so we
can run PCA directly.
length(VariableFeatures(obj)) # make sure it's a full gene universe of 10000 genes obj <- RunPCA(obj, assay = "SCT", verbose = FALSE, rev.pca = TRUE, reduction.name = "pca.rev", reduction.key="PCR_", npcs = 50) E <- obj@reductions$pca.rev@feature.loadings
Following the authors we are going to use KEGG pathway collection.
library(msigdbr) pathwaysDF <- msigdbr("mouse", category="C2", subcategory = "CP:KEGG") pathways <- split(pathwaysDF$gene_symbol, pathwaysDF$gs_name)
Now we can run the analysis (we set center=FALSE
because we use the
reduced matrix):
set.seed(1) gesecaRes <- geseca(pathways, E, minSize = 5, maxSize = 500, center = FALSE, eps=1e-100) head(gesecaRes, 10)
Now we can plot profiles of the top pathways (we need to specify the reduction as we are using the one from the publication):
topPathways <- gesecaRes[, pathway] |> head(4) titles <- sub("KEGG_", "", topPathways) ps <- plotCoregulationProfileReduction(pathways[topPathways], obj, title=titles, reduction="tsne") cowplot::plot_grid(plotlist=ps[1:4], ncol=2)
We can see that inflammatory pathways (e.g. KEGG_LEISHMANIA_INFECTION) are more associated with the non-foamy intimal macrophages, which was one of the main points of the Kim et al. Another pathway highlighted by the authors, KEGG_LYSOSOME, is specific to intimal foamy macrophages:
plotCoregulationProfileReduction(pathways$KEGG_LYSOSOME, obj, title=sprintf("KEGG_LYSOSOME (pval=%.2g)", gesecaRes[match("KEGG_LYSOSOME", pathway), pval]), reduction="tsne")
Similarly to single cell RNA-seq, GESECA can be applied to spatial transcriptomics profiling. As an example we will use glioblastoma sample from Ravi et al (https://pubmed.ncbi.nlm.nih.gov/35700707/).
library(Seurat) obj <- readRDS(url("https://alserglab.wustl.edu/files/fgsea/275_T_seurat.rds"))
As for scRNA-seq, we apply normalization for 10000 genes and run a do a PCA reduction.
obj <- SCTransform(obj, assay = "Spatial", verbose = FALSE, variable.features.n = 10000) obj <- RunPCA(obj, assay = "SCT", verbose = FALSE, rev.pca = TRUE, reduction.name = "pca.rev", reduction.key="PCR_", npcs = 50) E <- obj@reductions$pca.rev@feature.loadings
We will use HALLMARK pathways as the gene set collection.
library(msigdbr) pathwaysDF <- msigdbr("human", category="H") pathways <- split(pathwaysDF$gene_symbol, pathwaysDF$gs_name)
Now we can run the analysis (remember, we set center=FALSE
because we use the
reduced matrix):
set.seed(1) gesecaRes <- geseca(pathways, E, minSize = 15, maxSize = 500, center = FALSE) head(gesecaRes, 10)
Finally, let us plot spatial expression of the top four pathways:
topPathways <- gesecaRes[, pathway] |> head(4) titles <- sub("HALLMARK_", "", topPathways) ps <- plotCoregulationProfileSpatial(pathways[topPathways], obj, title=titles) cowplot::plot_grid(plotlist=ps, ncol=2)
Consistent with the Ravi et al, we see a distinct hypoxic region (defined by HALLMARK_HYPOXIA) and a reactive immune region (defined by HALLMARK_INTERFERON_GAMMA_RESPONSE). Further, we can explore behavior of HALLMARK_OXIDATIVE_PHOSPHORYLATION pathway to see that oxidative metabolism is more characteristic to the "normal" tissue region:
plotCoregulationProfileSpatial(pathways$HALLMARK_OXIDATIVE_PHOSPHORYLATION, obj, title=sprintf("HALLMARK_OXIDATIVE_PHOSPHORYLATION (pval=%.2g)", gesecaRes[ match("HALLMARK_OXIDATIVE_PHOSPHORYLATION", pathway), pval]))
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