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Identify sites with differential editing between cells in single cell datasets
Source:R/differential_editing.R
find_scde_sites.Rd
Compare editing frequencies between clusters or celltypes. REF and ALT counts from each cluster are pooled to create pseudobulk estimates. Each pair of clusters are compared using fisher exact tests. Statistics are aggregated across each pairwise comparison using scran::combineMarkers.
Arguments
- sce
SingleCellExperiment object with
nRef
andnAlt
assays.- group
column name from colData used to define groups to compare.
- rowData
if TRUE, rowData from the input SingleCellExperiment will be included in the output DataFrames
- BPPARAM
BiocParallel backend for control how parallel computations are performed.
- ...
Additional arguments passed to scran::combineMarkers
Value
A named list of DataFrames containing results for each cluster specified by
group
. The difference in editing frequencies between cluster pairs are
denoted as dEF
. See scran::combineMarkers for a description of additional
output fields.
Examples
### generate example data ###
library(Rsamtools)
library(GenomicRanges)
bam_fn <- raer_example("5k_neuron_mouse_possort.bam")
gr <- GRanges(c("2:579:-", "2:625:-", "2:645:-", "2:589:-", "2:601:-"))
gr$REF <- c(rep("A", 4), "T")
gr$ALT <- c(rep("G", 4), "C")
cbs <- unique(scanBam(bam_fn, param = ScanBamParam(tag = "CB"))[[1]]$tag$CB)
cbs <- na.omit(cbs)
outdir <- tempdir()
bai <- indexBam(bam_fn)
fp <- FilterParam(library_type = "fr-second-strand")
sce <- pileup_cells(bam_fn, gr, cbs, outdir, param = fp)
# mock some clusters
set.seed(42)
sce$clusters <- paste0("cluster_", sample(1:3, ncol(sce), replace = TRUE))
res <- find_scde_sites(sce, "clusters")
#> → 244 cells had no REF or ALT counts and were excluded from the analysis
res[[1]]
#> DataFrame with 4 rows and 10 columns
#> self.average other.average self.detected other.detected
#> <numeric> <numeric> <numeric> <numeric>
#> site_2_579_2_AG 0.690208 0.653495 0.727273 0.707399
#> site_2_625_2_AG 0.926220 0.991736 0.838384 0.901309
#> site_2_589_2_AG 0.862085 0.831895 0.868687 0.861512
#> site_2_601_2_TC 0.838316 0.850864 0.858586 0.871928
#> Top p.value FDR summary.dEF dEF.cluster_2
#> <integer> <numeric> <numeric> <numeric> <numeric>
#> site_2_579_2_AG 1 0.872535 1 0.01784427 0.02257960
#> site_2_625_2_AG 1 1.000000 1 -0.03537552 -0.03537552
#> site_2_589_2_AG 3 1.000000 1 -0.00772495 -0.00772495
#> site_2_601_2_TC 4 1.000000 1 -0.00121012 -0.00121012
#> dEF.cluster_3
#> <numeric>
#> site_2_579_2_AG 0.017844268
#> site_2_625_2_AG -0.008314991
#> site_2_589_2_AG -0.000749251
#> site_2_601_2_TC 0.000124844