In PYangLab/Cepo: Cepo for the identification of differentially stable genes

Introduction

We introduce Cepo, a method to determine genes governing cell identity from scRNA-seq data. We propose a biologically motivated metric—differential stability (DS)—to define cell identity. Our motivation is driven by the hypothesis that stable gene expression is a key component of cell identity. This hypothesis implies that genes marking a cell type should be (i) expressed and (ii) stable in its expression relative to other cell types. We translate these criteria into a computational framework where, using predefined cell-type labels, we compute a cell-type-specific score to prioritise genes that are differential stably expressed against other cell types between all cell-type pair comparisons.

Cepo is therefore distinct from most methods for differential analysis (e.g., differential expression) that prioritise differences in the mean abundance between cell types. Cepo is able to capture subtle variations in distribution that does not necessarily involve changes in mean. Cepo is particularly suitable for large atlas data as it is computationally efficient and fast. Moreover, Cepo can perform differential stability analysis for multi-group comparisons in single-cell data.

knitr::opts_chunk$set(crop = NULL)

To access the R code used in the vignettes, type:

browseVignettes("Cepo")

Questions relating to Cepo should be reported as a new issue at BugReports.

To cite Cepo, type:

citation("Cepo")

Package installation

The development version of Cepo can be installed with the following command:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("Cepo")

Differential stability analysis using Cepo

The differential stability analysis in Cepo aims to investigate differential stability patterns between cells of different cell types. To use Cepo one needs data with cell type labels (or cluster labels). If no cell-type labels are provided, cells first need to be clustered and classified in groups via some form of clustering algorithms. Cepo can then be applied to identify differentially stable genes between cell types.

Example data

Load the example dataset, a small and randomly sampled subset of the Cellbench dataset consisting of 3 cell types 895 cells and 894 genes.

library(Cepo)
library(SingleCellExperiment)
data("cellbench", package = "Cepo")
cellbench
cellbench = cellbench[!duplicated(rownames(cellbench)),]

Columns of the colData indicate the individual id and various metadata for each cell. colData contains celltype labels, which will be required to run Cepo. Differential stability analysis performed on the entire cell type repertoire.

colData(cellbench)[1:5,]

Note that, if cell-type labels are unknown, we would need to cluster cells into groups via some clustering algorithm. In the example dataset, we have 3 cell types, H1975, H2228 and HCC827, all of which are commonly used cell lines of lung adenocarcinomas.

unique(cellbench$celltype)

Run Cepo to generate list of cell identity genes

Main arguments of Cepo

There are two main arguments to Cepo: 1) exprsMat is the input data, which should be normalized data, such as counts per million (CPM) or log2-CPM (e.g., logcounts as created via scater::logNormCounts). 2) cellTypes receives as input a vector of cell-type labels. Note that the cell-type labels should be equal in length and ordered the same as the column names in exprsMat.

ds_res = Cepo(exprsMat = logcounts(cellbench),
              cellType = cellbench$celltype)

The Cepo function returns a list of two elements by default. The first element is a DataFrame of DS statistics. In this DataFrame, each column corresponds to the DS statistics for that celltype across all genes. A higher DS statistic value denotes a gene that is more prioritized as a differentially stable gene in that given cell type. In the output DataFrame, the columns correspond to each cell type and each row correspond to a gene.

ds_res

Filtering

In many cases, it is beneficial to perform filtering of lowly expressed genes prior to differential analysis. The parameter exprsPct specifies the threshold for filtering of lowly expressed genes should be performed. By default, this is set of NULL. A value between 0 and 1 should be provided. Whilst there is no set rule to the threshold, we recommend a value between 0.05 and 0.07, which will keep any genes that are expressed in 5-7% in at least one cell type, for microfluidic-based data.

ds_res_zprop = Cepo::Cepo(exprsMat = logcounts(cellbench),
                          cellTypes = cellbench$celltype,
                          exprsPct = 0.5)

The parameter logfc specifies minimum log fold-change in gene expression. A value of 0.2 will keep any genes that show at least abs(0.2) log fold change in gene expression in at least one cell type. By default, this value is NULL.

ds_res_logfc = Cepo(exprsMat = logcounts(cellbench),
                    cellTypes = cellbench$celltype,
                    logfc = 1)

Cepo outputs some useful stats, including the number of genes nrow and gene names rownames. By checking nrow, we can see that as expected with filtering the number of genes included in the Cepo run becomes fewer.

nrow(ds_res$stats)
nrow(ds_res_zprop$stats)
nrow(ds_res_logfc$stats)

Computing p-values

There are two methods to compute p-values in Cepo. The fast approach uses normal approximation of the Cepo statistics to estimate the null distribution. As this only required 100-200 sample runs of Cepo, it is much quicker, and the default approach, than the second permutation approach.

The output of running the p-value computation is a DataFrame of p-values associated with the DS statistics. In this DataFrame, each column corresponds to the p-values associated with the DS statistics.

ds_res_pvalues = Cepo(exprsMat = logcounts(cellbench),
                      cellType = cellbench$celltype, 
                      computePvalue = 200,
                      prefilter_pzero = 0.4)

We can visualise the correlation between the Cepo statistics and -log10 p-values.

idx = rownames(ds_res_pvalues$stats)

par(mfrow=c(1,3))
for (i in unique(cellbench$celltype)) {

  plot(rank(ds_res_pvalues$stats[[i]]),
       rank(-log10(ds_res_pvalues$pvalues[idx, i])),
       main = i,
       xlab = "rank Cepo statistics",
       ylab = "rank -log10 p-values")

}
par(mfrow=c(1,1))

The permutation approach requires the users to set the computePvalue argument to a number of bootstrap runs required (we recommend this to be at least 10000). Each column of the DataFrame corresponds to the p-values associated with the DS statistics obtained through bootstrap on the cells.

ds_res_pvalues = Cepo(exprsMat = logcounts(cellbench),
                      cellType = cellbench$celltype, 
                      # we use a low value for demonstration purposes
                      computePvalue = 100,
                      computeFastPvalue = FALSE)
ds_res_pvalues

Visualizing results

We can visualize the overlap of differential stability genes between cell types.

library(UpSetR)
res_name = topGenes(object = ds_res, n = 500)
upset(fromList(res_name), nsets = 3)

Density plot of two genes from each cell type.

plotDensities(x = cellbench,
              cepoOutput = ds_res,
              nGenes = 2,
              assay = "logcounts",
              celltypeColumn = "celltype")

We can also specify the genes to be plotted.

plotDensities(x = cellbench,
              cepoOutput = ds_res,
              genes = c("PLTP", "CPT1C", "MEG3", "SYCE1", "MICOS10P3", "HOXB7"),
              assay = "logcounts",
              celltypeColumn = "celltype")

Running Cepo in a pipeline

Example data

We will load an example dataset, a small, randomly subsampled subset of the human pancreas datasets from the scMerge paper consisting of 3 batches, 2 cell types, 528 cells, and 1358 genes.

data("sce_pancreas", package = "Cepo")
sce_pancreas

Given the presences of batches, we will visualize the data for any batch effect. Clearly these is separation of the data points by batch.

library(scater)
sce = sce_pancreas
sce = scater::logNormCounts(sce)
sce = scater::runPCA(sce)

scater::plotPCA(
  sce, 
  colour_by = "cellTypes", 
  shape_by = "batch")

scMerge to remove batch effect

We can run the analysis on batch corrected data. For this, we can implement batch correction methods on the data suing batch correction methods such as scMerge.

library(scMerge)
data("segList", package = "scMerge")
head(segList$human$human_scSEG)

corrected <- scMerge(
  sce_combine = sce,
  ctl = segList$human$human_scSEG,
  kmeansK = c(2, 2),
  assay_name = "scMerge",
  cell_type = sce$cellTypes)

Let us visualise the corrected data.

corrected = runPCA(corrected,
                            exprs_values = "scMerge")

scater::plotPCA(
  corrected,
  colour_by = "cellTypes",
  shape_by = "batch")

ds_res = Cepo::Cepo(exprsMat = assay(corrected, "scMerge"),
              cellType = corrected$cellTypes)

Running Cepo by batch

Rather than running Cepo on the corrected values, we can run the differential analysis independently on individual batches using the block argument. By default, the block argument is set to NULL, ignoring batch information. If batches are present and the data is not corrected for batch effect, ensure you run the analyses by block.

ds_res_batches = Cepo::Cepo(exprsMat = logcounts(sce),
                            cellTypes = sce$cellTypes,
                            block = sce$batch,
                            minCelltype = 2)

Note that the resulting output in a list of Cepo class objects where each slot denotes the individual results for the three batches, as well as the averaged results saved as average.

names(ds_res_batches)

We can confirm that the Cepo statistics from across batches demonstrate a strong correlation. The clustered correlation heatmap below shows that there is high correlation between the scores of the same cell type across batches.

idx = Reduce(intersect, lapply(ds_res_batches, function(x) names(x$stats[, 1])))

combinedRes = as.data.frame(do.call(cbind, lapply(ds_res_batches, function(x) 
  x$stats[idx,]
)))

batches = rep(names(ds_res_batches), sapply(ds_res_batches, function(x) length(x$stats)))
cty = unlist(lapply(ds_res_batches, function(x) names(x$stats)), use.name = FALSE)
colnames(combinedRes) = gsub("[.]", "_", colnames(combinedRes))

annot = data.frame(
  batch = batches,
  celltype = cty
)
rownames(annot) = colnames(combinedRes)

pheatmap::pheatmap(cor(combinedRes),
                   annotation = annot)

Downstream analyses using Cepo genes

Marker gene identification and visualisation

One of the useful applications of Cepo is to find marker genes or cell identity genes on clustered data. We can visualise the top three marker genes for beta and ductal cells on the PCA.

cepo_genes = Cepo::topGenes(ds_res_batches$average, n = 3)

markersPlot = lapply(cepo_genes, function(x) {
  pp = lapply(x, function(gene) {
  p = scater::plotPCA(
  corrected,
  colour_by = gene,
  shape_by = "cellTypes")
  return(p)
  })
  pp = patchwork::wrap_plots(pp, ncol = 3) + patchwork::plot_layout(guides = "auto")
  return(pp)
})
patchwork::wrap_plots(markersPlot, nrow = 2)

Gene set enrichment analysis

We can also perform a plethora of downstream analyses, from gene set enrichment analyses to deconvolution of bulk RNA-seq, with the cell identity gene scores generated from the Cepo package. As an example, we will perform gene set enrichment analysis using the fgsea and escape package.

library(escape)
library(fgsea)
hallmarkList <- getGeneSets(species = "Homo sapiens", 
                           library = "H")

fgseaRes <- fgsea(pathways = hallmarkList, 
                  stats    = sort(ds_res_batches[4]$average$stats[,"beta"]),
                  minSize = 15,
                  maxSize = 500)

enriched_beta <- -log10(fgseaRes[order(pval), "padj"][[1]])
names(enriched_beta) <- fgseaRes[order(pval), "pathway"][[1]]

Note the top 5 enriched pathways for beta cells.

enriched_beta[1:5]

Finally, we can visualise the enrichment using the plotEnrichment function from the fgsea package.

plotEnrichment(hallmarkList[["HALLMARK-PANCREAS-BETA-CELLS"]],
               sort(ds_res_batches$average$stats[, "beta"])) + labs(title="HALLMARK-PANCREAS-BETA-CELLS")

Running out-of-memory computation with Cepo

To facilitate analysis of high-throughput atlas data consisting of millions of cells, Cepo also enables out-of-memory and parallel computation.

The Cepo function naturally handles matrices under the DelayedArray wrapper. Briefly, DelayedArray is a wrapper around many matrix classes in R, including matrix, sparseMatrix and HDF5Array. The last of which allows for out-of-memory computation, which means the computation is done outside of RAM. This will inevitably slow down the computational speed, but the major gain in doing this is that we can perform computations on data much larger than what our RAM can store at once.

library(DelayedArray)
library(HDF5Array)
da_matrix = DelayedArray(realize(logcounts(cellbench), "HDF5Array"))
class(da_matrix)
class(seed(da_matrix))

da_output = Cepo(exprsMat = da_matrix, cellType = cellbench$celltype)

Even though out-of-memory computation is slow, one way that we can speed up the computation is through parallel processing. This requires some configurations of the DelayedArray package via the setAutoBPPARAM function. BiocParallel package uses the MulticoreParam parameter for Linux/Mac and SnowParam for Windows.

library(BiocParallel)

BPPARAM = if (.Platform$OS.type == "windows") {
  BiocParallel::SnowParam(workers = 2)
} else {
  BiocParallel::MulticoreParam(workers = 2)
}

DelayedArray::setAutoBPPARAM(BPPARAM = BPPARAM) ## Setting two cores for computation

da_output_parallel = Cepo(exprsMat = da_matrix, cellTypes = cellbench$celltype)

DelayedArray::setAutoBPPARAM(BPPARAM = SerialParam()) ## Revert back to only one core

Session info

sessionInfo()

PYangLab/Cepo documentation built on June 29, 2024, 7:16 p.m.