ProsCan_DE: The Prostate Cancer Data Example: DE Test Result

In gu-mi/GOglm: Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression

Description

We use this data example to show one of the expected inputs from end-users to prepare an data frame object for GO enrichment analysis using GOglm.

Details

The prostate cancer data consist of seven samples: three from mock treated prostate cancer cells and four from treated cancer cells. The data originally consisted of 49605 genes annotated using Ensembl gene ID (ensGene) and NCBI Build 36.3 (hg18). Descriptions of data preparations can be found in the additional file of Young et al. (2010).

We performed DE tests on the original dataset, so the data frame provided here is an formatted version that the prepare function can further take as the first argument. The Example below shows the format of the required input.

In this illustrative example, we used edgeR (Robinson et al. (2010)) with a common dispersion estimate to obtain DE test p-values, which will be further transformed into significance statistics by the prepare function. Other DE testing methods based on the negative binomial (NB) model for RNA-Seq data can also be adopted, such as the tagwise or trend options in edgeR, the NBPSeq and the DESeq approaches. All of these methods use the same exact NB test for assessing DE, but differ in how they estimate the dispersion parameter as a function of the mean frequency. We use this example to illustrate the DE test results expected from the end-users, from whatever DE testing procedures.

Source

http://www.ncbi.nlm.nih.gov/pubmed/19088194

References

Mi G, Di Y, Emerson S, Cumbie JS and Chang JH (2012) "Length bias correction in Gene Ontology enrichment analysis using logistic regression", PLOS ONE, 7(10): e46128.

Li H, Lovci M, Kwon Y, Rosenfeld M, Fu X, et al. (2008) "Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model", Proc Natl Acad Sci U S A 105: 20179-20184.

Young M, Wakefield M, Smyth G, Oshlack A (2010) "Gene ontology analysis for RNA-seq: accounting for selection bias", Genome Biol 11: R14.

Robinson M, McCarthy D, Smyth G (2010) "edgeR: a Bioconductor package for differential expression analysis of digital gene expression data", Bioinformatics 26: 139-140.

Examples

## Load the dataset into R session:
data(ProsCan_DE)
DE.data <- ProsCan_DE

## Another dataset from this package:
data(ProsCan_Length)
Length.data <- ProsCan_Length

## Prepare a data frame to be passed to goglm():
gene_table <- prepare(DE.data, Length.data, trans.p = "d.log", trans.l = TRUE)
## Check first 10 rows of the data frame:
gene_table[1:10,1:2]

## We can call the summary() function:
summary(gene_table)

## We can also call the plot() function:
plot(gene_table)

gu-mi/GOglm documentation built on May 14, 2019, 7:42 a.m.