customReferences: 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

This function uses the Counts_to_tpm and the DAFS function to select the reference genes.

Usage

customReferences(counts, featureLength, top_genes = 0.5)

Arguments

counts	Data frame genes/features (in the rows) by samples (in the columns). Rownames must be in the names of the features and in the same format as the names of the featureLength vector
featureLength	Named numeric vector with the length of each gene/feature. Names must be the names of the features and in the same format as the rownames of the data frame counts.
top_genes	Percentage of genes (left after filtering) to be selected as references, default is 0.5%.

Details

After transforming the counts into TPM values, the tpm data frame is used as input for DAFS function.

We then select the genes with lowest covariance, among those considered as expressed according to DAFS (average expression higher than the cutoff), as references.

Value

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

Examples

data("sample_counts"); data("ath_featureLength")
genes <- customReferences(counts = sample_counts, featureLength = ath_featureLength, top_genes = 0.5)

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