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Package prerequisites

• R (version 3.6.3 was used in the respective paper)
• stats package
• devtools package, to install controlFreq

Definitions

AI (allelic imbalance)

relative proportion of a fixed allele, frequently determined from "informative" reads that covers distinguishing sites.

Allele counts table

a table with 2n+1 columns: "ID" and pairs of columns for each sample (2n) with allelic counts (sample1_allele1, sample1_allele2, sample2_allele1, sample2_allele2, ...). For this table creation recommendations, see fastq2allelictabs repository.

iQCC

an overdispersion measure, may be considered as widening coefficient comparative to binomial expectations.

Usage

compute_iQCC_for_selected_samples(df = <allelic-counts-table>, 
                                  reps = <vector-of-sample-numbers>, 
                                  inf_Q = 1, sup_Q = 1)

Returns a list of iQCC triplets for each sample within selected numbers: lower (iQCC_included_inAI), upper (iQCC_excluded_inAI) and exact (iQCC_geom_mean) estimates.

Arguments

df : Allele counts dataframe: with 2n+1 columns, "ID" and 2n columns with ref & alt counts (rep1_ref, rep1_alt, rep2_ref, rep2_alt, ...)

reps : Optional (default=NA, all replicates), a vector of replicate numbers for which the analysis should be applied

inf_Q : Optional (default=1) Only use the data rows for which inf_Q yields a well-defined distribution. The default inf_Q=1 simplifies to the binomial distribution, therefore it does not restrict the input.

sup_Q : Optional (default=1) Only use the data rows for which sup_Q yields a well-defined distribution. The default sup_Q=1 does not restrict the input. The parameter can be used to effectively filter the rows with a low coverage, e.g. setting sup_Q=30 filters out the data rows with the coverage less than 30.

Differential ASE analysis

For CI(AI) estimation and AI differential analysis for two samples, see Qllelic repository. Use iQCC values instead of QCCs.