lncDIFF {lncDIFF}

lncRNA Differential Expression (DE) analysis

Description

lncDIFF returns DE analysis results based on lncRNA counts and grouping variables.

Usage

lncDIFF(
  edata,
  group,
  covariate = NULL,
  link.function = "log",
  CompareGroups = NULL,
  simulated.pvalue = FALSE,
  permutation = 100
)

Arguments

edata

Normalized counts matrix with genes in rows and samples in columns.

group

Primary factor of interest in DE analysis, e.g., treatment groups, tissue types, other phenotypes.

covariate

Other variables (or covariates) associated with expression level. Input must be a matrix or data frame with each column being a covariate matching to group

link.function

Link function for the generalized linear model, either 'log' or 'identity', default as 'log'.

CompareGroups

Labels of treatment groups or phenotypes of interest to be compared in DE analysis. Input must be a vector of group labels without duplicates.

simulated.pvalue

If empirical p-values are computed, simulated.pvalue=TRUE. The default is FALSE.

permutation

The number of permutations used in simulating pvalues. The default value is 100.

Value

DE.results

Likelihood ratio test results with test statistics, p-value, FDR, DE genes, groupwise mean expression, fold change (if two groups are compared). If simulated.pvalue=TRUE, test.results also includes simulated p-value and FDR.

full.model.fit

Generalized linear model with zero-inflated Exponential likelihood function, estimating group effect compared to a reference group.

References

Li, Q., Yu, X., Chaudhary, R. et al.'lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA'. BMC Genomics (2019) 20: 539.

Examples

data('hnsc.edata','tissue','cov')  

# DE analysis comparing two groups (normal vs tumor) for 100 genes
result=lncDIFF(edata=hnsc.edata[1:100,],group=tissue,covariate=cov) 

# Recommend at least 50 permutations if simulated.pvalue=TRUE