distances: Internal functions for estimating pair-wise transcriptome...

In jingwyang/AnceTran: the R package for analyzing TF-binding evolution based on ChIP-seq data

Description

Several published methods to estimate pair-wise transcriptome distances.

Distance methods used to analyze expression values include Pearson distance, Euclidean distance, angular-cosine distance, the square root of Jensen-Shannon divergence, the Brownian distance, and so on.

Stationary OU method is applied to estimate pair-wise TF binding distances

Usage

Expdist.nbdln(reads.count = NULL, gene_length = NULL, omega = NULL)

Expdist.u(reads.count = NULL, gene_length = NULL)

Expdist.jsd(expMat = NULL)

Expdist.pea(expMat = NULL)

Expdist.spe(expMat = NULL)

Expdist.euc(expMat = NULL)

Expdist.cos(expMat = NULL)

Expdist.tani(expMat = NULL)

Expdist.jac(expMat = NULL)

Expdist.ced(expMat = NULL)

Expdist.sou(expMat = NULL)

TFdist.sou(bsMat = NULL)

Arguments

expMat	a expression value matrix: colum correspnds to expression values; row correspnds to othologous genes
bsMat	a TF-binding score matrix: column corresponds to binding score value; row corresponds to othologous genes

Value

A distance matrix shows the transcriptome distance for each paried species.

References

Chen H, He X. 2016. The Convergent Cancer Evolution toward a Single Cellular Destination. Mol Biol Evol 33:4-12

Gu X, Su Z. 2007. Tissue-driven hypothesis of genomic evolution and sequence-expression correlations. Proc Natl Acad Sci USA 104:2779-2784.

Pereira V, Waxman D, Eyre-Walker A. 2009. A problem with the correlation coefficient as a measure of gene expression divergence. Genetics 183:1597-1600.

Sudmant PH, Alexis MS, Burge CB. 2015. Meta-analysis of RNA-seq expression data across species, tissues and studies. Genome Biol 16:287.

Distance based on negative bio distribution and log normal model

jingwyang/AnceTran documentation built on May 19, 2019, 2:57 a.m.