Filter low-count exons from RNA-seq read count data.
filterLowCounts(rs_data, filter_min_per_exon = 6, filter_min_per_sample = 3)
Filtering parameter: minimum number of reads per exon bin, summed across all biological samples. Default is 6.
Filtering parameter: minimum number of reads per biological sample; i.e. for each exon bin, at least one sample must have this number of reads. Default is 3.
Filters low-count exon bins from RNA-seq read count data. Any remaining single-exon genes (after filtering) are also removed (since differential splicing requires multiple exon bins).
Input data is assumed to be in the form of a
RegspliceData object. See
RegspliceData for details.
filter_min_per_sample control the
amount of filtering. Exon bins that meet the filtering conditions are kept. Default
values for the arguments are provided; however, these should be adjusted depending on
the total number of samples and the number of samples per condition.
After filtering low-count exon bins, any remaining genes containing only a single exon bin are also removed (since differential splicing requires multiple exon bins).
Filtering should be skipped when using exon microarray data. (When using the
regsplice wrapper function, filtering can be disabled with the argument
filter = FALSE).
Previous step: Filter zero-count exon bins with
Next step: Calculate normalization factors with
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file_counts <- system.file("extdata/vignette_counts.txt", package = "regsplice") data <- read.table(file_counts, header = TRUE, sep = "\t", stringsAsFactors = FALSE) head(data) counts <- data[, 2:7] tbl_exons <- table(sapply(strsplit(data$exon, ":"), function(s) s[])) gene_IDs <- names(tbl_exons) n_exons <- unname(tbl_exons) condition <- rep(c("untreated", "treated"), each = 3) rs_data <- RegspliceData(counts, gene_IDs, n_exons, condition) rs_data <- filterZeros(rs_data) rs_data <- filterLowCounts(rs_data)
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