RNASeq.Data: Standardize the data from RNA-seq experiment

In AMAP.Seq: Compare Gene Expressions from 2-Treatment RNA-Seq Experiments

Description

Collect all necessary input data and standardize them for follow-up analysis

Usage

RNASeq.Data(counts, size = NULL, group, model = "nbinom", dispersion = NULL)

Arguments

counts	the counts of reads mapped to the gene. input as a G X S matrix, where G is the number of genes, and S is the number of samples
size	the normalization factors for the counts. It should be a vector with length S, for example, the total number of reads for each column. The default is Geometric Median of the counts in each column. Users can also input the 'size' as a G X S matrix, so that each cell of the 'counts' matrix has one normalization factor.
group	a vector indicating the design of a 2-treatment assignment, for example group=c(1,1,2,2).
model	specify the discrete probability that model the counts. We allow 'nbinom' and 'poisson' in our test, where 'nbinom' is the default choice that use negative-binomal model.
dispersion	the dispersion parameter for each gene (each row of the counts). users can specify the estimates by their own method, or by default, we will use quasi-likelihood method to estimate a dispersion for each gene

Value

counts	counts of reads
size	Normalization factor of each count
group	treatment group
model	distribution
dispersion	estimated dispersion parameter in the NB model. If model="poisson", dispersion=1e-4 for all genes

Examples

### see examples by typing 'help(test.AMAP)'

AMAP.Seq documentation built on May 2, 2019, 6:45 a.m.