PS.Est.Depth: estimate the sequencing depths

In PoissonSeq: Significance analysis of sequencing data based on a Poisson log linear model

Description

Given a data matrix, this function will estimate the sequencing depths on the basis of the Poisson goodness-of-fit statistic. This estimate is applicable to data with any types of outcome, as it estimates under the null hypothesis.
A more detailed instruction as well as sample data is available at
http://www.stanford.edu/~junli07/research.html.

Usage

 PS.Est.Depth(n, iter=5, ct.sum=5, ct.mean=0.5)

Arguments

n	The data matrix. The rows are counts for a gene, and the columns are counts from an experiment.
iter	Number of iterations used. Default value: 5. The default value is usually a good choice.
ct.sum	if the total number of reads of a gene across all experiments <= ct.sum, this gene will not be considered for estimating sequencing depth. Default value: 5.
ct.mean	if the mean number of reads of a gene across all experiments <= ct.mean, this gene will not be considered for estimating sequencing depth. Default value: 0.5.

Details

The value in the data matrix does not need to be integers.

Value

estimated sequencing depth. a vector. their product is 1.

Author(s)

Jun Li.

References

Li J, Witten DM, Johnstone I, Tibshirani R (2011). Normalization, testing, and false discovery rate estimation for RNA-sequencing data. To appear, Biostatistics.

Examples

 data(dat)
 seq.depth <- PS.Est.Depth(dat$n)

PoissonSeq documentation built on May 1, 2019, 7:33 p.m.