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Perform differential editing

Source: R/differential_editing.R
find_de_sites.Rd

Use edgeR or DESeq2 to perform differential editing analysis. This will work for designs that have 1 treatment and 1 control group. For more complex designs, we suggest you perform your own modeling.

Usage

find_de_sites(
  deobj,
  test = c("edgeR", "DESeq2"),
  sample_col = "sample",
  condition_col = "condition",
  condition_control = NULL,
  condition_treatment = NULL
)

Arguments

deobj

A RangedSummarizedExperiment object prepared for differential editing analysis by make_de_object()

test

Indicate if edgeR or DESeq2 should be run.

sample_col

The name of the column from colData(deobj) that contains your sample information. Default is sample. If you do not have a column named "sample", you must provide the appropriate sample column

condition_col

The name of the column from colData(deobj) that contains your treatment information. Default is condition, If you do not have a column named "condition", you must provide the appropriate condition column

condition_control

The name of the control condition. This must be a variable in your condition_col of colData(deobj). No default provided.

condition_treatment

The name of the treatment condition. This must be a variable in your condition_col of colData(deobj).

Value

A named list:

  • de_obj: The edgeR or deseq object used for differential editing analysis

  • results_full: Unfiltered differential editing results

  • sig_results: Filtered differential editing (FDR < 0.05)

  • model_matrix: The model matrix used for generating DE results

Examples

library(SummarizedExperiment)
bamfn <- raer_example("SRR5564269_Aligned.sortedByCoord.out.md.bam")
bam2fn <- raer_example("SRR5564277_Aligned.sortedByCoord.out.md.bam")
fafn <- raer_example("human.fasta")

bams <- rep(c(bamfn, bam2fn), each = 3)
sample_ids <- paste0(rep(c("KO", "WT"), each = 3), 1:3)
names(bams) <- sample_ids

fp <- FilterParam(only_keep_variants = TRUE)
rse <- pileup_sites(bams, fafn, param = fp)
rse$condition <- substr(rse$sample, 1, 2)

rse <- calc_edit_frequency(rse)
#> ℹ 6 sites had no coverage for calculating editing
dse <- make_de_object(rse)
res <- find_de_sites(dse,
    condition_control = "WT",
    condition_treatment = "KO"
)
#> Using classic mode.
res$sig_results[1:3, ]
#>                      logFC   logCPM        LR      PValue        FDR
#> site_SSR3_244_2 -11.108016 21.06907 13.122188 0.000291819 0.01984369
#> site_DHFR_361_2 -11.147610 21.96719 11.531065 0.000684428 0.02327055
#> site_SSR3_254_2  -7.938091 21.10149  9.926435 0.001629218 0.03692893