PAprocess: APA-related gene expression data preprocessing
In BMILAB/PASCCA: Cluster analysis of replicated alternative polyadenylation data using shrinkage canonical correlation analysis

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/PASCCA.R

It鈥檚 to do preliminary processing of APA-related gene expression data.User can choose whether to perform log-transformation by parameter log (default: TRUE).

1	PAprocess(data, log = TRUE)

data

Either a numeric matrix or a data frame.The expressions of RNA-seq data at exon levels or position levels. The first column is poly(A) or exon names, the second column is gene names, and the remaining column is sample names under different biological conditions such as different tissues, cell types and developmental stages. If the set of samples have repeated measurements, the order of the samples in data must be arranged in from big to small according to the number of replicates.

log

Choose Whether or not to perform log2 transformation for data.

This function provides two steps taken to pre-process data. 1) Data cleaning: To filter out genes with one poly(A) site from gene expression data. 2) Data transformation: To transform the count data to the log2 scale by setting the parameter log=TRUE (default: TRUE).

An object of expression matrix or data frame the same as the input data.

If the set of samples have repeated measurements, the order of the samples in data must be arranged in from big to small according to the number of replicates.

Yuqi Long, Wenbin Ye

Thomas, P. E., X. Wu, M. Liu, B. Gaffney, G. Ji et al., 2012 Genome-Wide Control of Polyadenylation Site Choice by CPSF30 in Arabidopsis. Plant Cell 24: 4376-4388.

 ##Loading example data
 data(polyA_example_data2)
 dim(data2)
 str(data2)
 data2[1:5,1:5]

 ##Data preprocessing
 pre_data <- PAprocess(data2,log="T")
 dim(pre_data)
 pre_data[1:5,1:5]