gene_selection: Uses average TPM values and the covariance of TPM values to...

In KarenGoncalves/CustomSelection: Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis

Uses average TPM values and the covariance of TPM values to select reference genes from RNAseq data

Description

If counts_to_tpm and DAFS functions were already computed, this function will use their results to select the genes with lowest covariance, among those considered as expressed according to DAFS, as references.

Usage

gene_selection(countsToTpm_result, dafs_result, top_genes = 0.5)

Arguments

countsToTpm_result	Result of the counts_to_tpm function or data frame with other expression unit (FPKM, RPKM, CPM)
dafs_result	Result of DAFS fucntion: vector with threshold for noise/true expression for each sample (columns from tpm data frame) in log2
top_genes	Percentage of genes (left after filtering) to be selected as references, default is 0.5%.

Details

If there is no interest on keeping the results from DAFS or the TPM values for all genes in each sample, run customReferences instead.

Value

Data frame with genes as rows and two columns: Mean (average TPM) and Covariance.

Examples

data("sample_counts"); data("ath_featureLength")
tpm <- Counts_to_tpm(counts = sample_counts, featureLength = ath_featureLength)
dafs <- DAFS(tpm[[1]])
genes <- gene_selection(countsToTpm_result = tpm[[1]], dafs_result = dafs, top_genes = 0.5)

KarenGoncalves/CustomSelection documentation built on Oct. 24, 2023, 12:39 a.m.