PoissonSeq (version 1.1.2)

PS.Main: detecting differentially expressed genes from RNA-Seq data.

Description

This function is the main function of this package. Given the data matrix and the outcome vector, this function returns the estimated permuation-based p-values, the estimated permutation-based false discovery rates, et al. A more detailed instruction as well as sample data is available at http://www.stanford.edu/~junli07/research.html.

Usage

PS.Main(dat, para=list())

Arguments

dat

The input RNA-Seq data. It must have the following three attributes: (1) n: the data matrix. Rows for genes, columns for experiments (samples). (2) y: the outcome vector (3) type: 'twoclass', 'multiclass' or 'quant' The following attributes are optional. If not specified, the default values will be used. (4) pair: paired data or not. Default value: FALSE. Only take effect for twoclass data. (5) gname: gene names. Default value: 1 : nrow(n). That is, the i'th gene is named "i".

para

A list of parameters. It can have the following attributes: (1) trans: to tranform the data using the order transformation or not to transform it. default value: TRUE (2) npermu: number of permuations. default value: 100 (3) seed: random seed to generate the permutation indexes. default value: 10 (4) ct.sum: if the total number of reads of a gene across all experiments <= ct.sum,="" this="" gene="" will="" not="" be="" considered="" for="" differential="" expression="" detection.="" default value:="" 5.="" (5)="" ct.mean: if the mean number of reads of a gene across all experiments <= ct.mean,="" this="" gene="" will="" not="" be="" considered="" for="" differential="" expression="" detection.="" default value:="" 0.5.<="" p="">

(6) div: the number of divisions of genes for estimating theta. default value: 10 (7) pow.file: the file to store the power transform curve (mean(log(mu)) ~ 1/theta). default value: 'pow.txt' All the above attributes are optional.

Value

a data frame (table) containing the following columns. Each row stands for a gene. The genes are sorted from the most significant to the most insignificant.

nc

number of significant genes called. nc = 1 : (number of genes).

gname

the sorted gene names.

tt

The score statistics of the genes.

pval

Permutation-based p-values of the genes.

fdr

Estimated false discovery rate.

log.fc

Estimated log fold change of the genes. Only available for twoclass outcomes.

References

Li J, Witten DM, Johnstone I, Tibshirani R (2012). Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13(3): 523-38.

Examples

 data(dat)
 res <- PS.Main(dat)