Skip to contents

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.

Usage

find_scde_sites(sce, group, rowData = FALSE, BPPARAM = SerialParam(), ...)

Arguments

sce

SingleCellExperiment object with nRef and nAlt 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