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Calculate the Adenosine Editing Index (AEI) in single cells

Source: R/calc_scAEI.R
calc_scAEI.Rd

The Adenosine Editing Index describes the magnitude of A-to-I editing in a sample. The index is a weighted average of editing events (G bases) observed at A positions. The vast majority A-to-I editing occurs in ALU elements in the human genome, and these regions have a high A-to-I editing signal compared to other regions such as coding exons. This function will examine enumerate edited and non-edited base counts at the supplied sites and return summary AEI metric per cell. Potential editing sites within repeat regions can be generated using get_scAEI_sites().

Usage

calc_scAEI(
  bamfiles,
  sites,
  cell_barcodes,
  param = FilterParam(),
  edit_from = "A",
  edit_to = "G",
  output_dir = NULL,
  return_sce = FALSE,
  ...
)

get_scAEI_sites(fasta, genes, alus, edit_from = "A", edit_to = "G")

Arguments

bamfiles

a path to a BAM file (for 10x libraries), or a vector of paths to BAM files (smart-seq2). Can be supplied as a character vector, BamFile, or BamFileList.

sites

a GRanges object produced by get_scAEI_sites() containing sites to process.

cell_barcodes

A character vector of single cell barcodes to process. If processing multiple BAM files (e.g. smart-seq-2), provide a character vector of unique identifiers for each input BAM, to name each BAM file in the output files.

param

object of class FilterParam() which specify various filters to apply to reads and sites during pileup.

edit_from

This should correspond to the base (A, C, G, T) you expect in the reference. Ex. for A to I editing events, this would be A.

edit_to

This should correspond to the base (A, C, G, T) you expect in an edited site. Ex. for A to I editing events, this would be G.

output_dir

Output directory for nRef and nAlt sparseMatrix files. If NULL, a temporary directory will be used.

return_sce

if TRUE, data is returned as a SingleCellExperiment, if FALSE a DataFrame containing computed AEI values will be returned.

...

additional arguments to pileup_cells()

fasta

Path to a genome fasta file

genes

A GRanges object with gene coordinates.Alternatively a TxDb object, which if supplied, will be used extract gene coordinates.

alus

GRanges with repeat regions to query for calculating the AEI, typically ALU repeats. The strand of the supplied intervals will be ignored for defining repeat regions.

Value

A DataFrame containing computed AEI values, count of editing events (n_alt), and count of reference events (n_ref) per cell. If return_sce is TRUE, then a SingleCellExperiment is returned with the AEI values stored in the colData.

References

Roth, S.H., Levanon, E.Y. & Eisenberg, E. Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity. Nat Methods 16, 1131–1138 (2019). https://doi.org/10.1038/s41592-019-0610-9

Examples

suppressPackageStartupMessages(library(Rsamtools))
library(GenomicRanges)

bam_fn <- raer_example("5k_neuron_mouse_possort.bam")
bai <- indexBam(bam_fn)

# cell barcodes to query
cbs <- c("TGTTTGTTCCATCCGT-1", "CAACCAACATAATCGC-1", "TGGAACTCAAGCTGTT-1")

# genes used to infer transcribed strand
genes_gr <- GRanges(c(
    "2:100-400:-",
    "2:500-605:-",
    "2:600-680:+"
))

# alu intervals
alus_gr <- GRanges(c(
    "2:110-380",
    "2:510-600",
    "2:610-670"
))

# genome fasta file, used to find A bases
fa_fn <- raer_example("mouse_tiny.fasta")

# get positions of potential A -> G changes in alus
sites <- get_scAEI_sites(fa_fn, genes_gr, alus_gr)

fp <- FilterParam(
    library_type = "fr-second-strand",
    min_mapq = 255
)
calc_scAEI(bam_fn, sites, cbs, fp)
#> DataFrame with 3 rows and 3 columns
#>                          AEI     n_alt     n_ref
#>                    <numeric> <numeric> <numeric>
#> TGTTTGTTCCATCCGT-1   5.08475         3        56
#> CAACCAACATAATCGC-1  20.00000         3        12
#> TGGAACTCAAGCTGTT-1   0.00000         0         9