| Title: | Long Non-Coding RNA Differential Expression Analysis |
|---|---|
| Description: | We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>. |
| Authors: | Qian Li [aut, cre] |
| Maintainer: | Qian Li <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2025-02-17 05:30:19 UTC |
| Source: | https://github.com/cran/lncDIFF |
Batch information for samples in hnsc.edata.
covcov
A matrix of covariate(s) in columns.
Design matrix for samples in hnsc.edata.
designdesign
A model matrix with 80 rows (i.e. samples) and 3 columns of tissue type and batch.
lncRNA Fragments Per Killobase per Million (FPKM) in a head and neck squamous cell carcinomas (hnsc) study.
hnsc.edatahnsc.edata
A data frame of lncRNA FPKM with 1000 rows (i.e. genes) and 80 columns (i.e. samples ).
lncDIFF returns DE analysis results based on lncRNA counts and grouping variables.
lncDIFF( edata, group, covariate = NULL, link.function = "log", CompareGroups = NULL, simulated.pvalue = FALSE, permutation = 100 )lncDIFF( edata, group, covariate = NULL, link.function = "log", CompareGroups = NULL, simulated.pvalue = FALSE, permutation = 100 )
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 |
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 |
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. |
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. |
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.
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=TRUEdata('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
ZIQML.LRT returns the likelihood ratio test statistics and p-value based on the object returned by ZIQML.fit().
LRT(ZIQML.fit, coef = NULL)LRT(ZIQML.fit, coef = NULL)
ZIQML.fit |
Object returned by ZIQML.fit() |
coef |
An integer or vector indicating the coefficient(s) in design matrix to be tested. coef=1 is the intercept (i.e. baseline group effect), and should not be tested. |
LRT.stat |
Likelihood ratio test statistics. |
LRT.pvalue |
Likelihood ratio test p-value. |
data('hnsc.edata','design') # 'hnsc.edata' contains FPKM of 1132 lncRNA genes and 80 samples. # 'design' is the design matrix of tissue type (tumor vs normal). # Fit GLM by ZIQML.fit for the first 100 genes fit.log=ZIQML.fit(edata=hnsc.edata[1:100,],design.matrix=design) # Likelihood ratio test to compare tumor vs normal in gene expression level. LRT.results=LRT(fit.log,coef=2)data('hnsc.edata','design') # 'hnsc.edata' contains FPKM of 1132 lncRNA genes and 80 samples. # 'design' is the design matrix of tissue type (tumor vs normal). # Fit GLM by ZIQML.fit for the first 100 genes fit.log=ZIQML.fit(edata=hnsc.edata[1:100,],design.matrix=design) # Likelihood ratio test to compare tumor vs normal in gene expression level. LRT.results=LRT(fit.log,coef=2)
Tissue type for samples in hnsc.edata.
tissuetissue
A character vector of tissue type.
ZIQML.fit estimates the group effect on gene expression using zero-inflated exponential quasi likelihood.
ZIQML.fit(edata, design.matrix, link = "log")ZIQML.fit(edata, design.matrix, link = "log")
edata |
Normalized counts matrix with genes in rows and samples in columns. |
design.matrix |
Design matrix for groups and covariates, generated by model.matrix(). |
link |
Link function for the generalized linear model and likelihood function,either 'log' or 'identity'. The default is 'log'. |
Estimates |
Estimated group effect on gene expression by zero-inflated exponential quasi maximum likelihood (ZIQML) estimator. |
logLikelihood |
The value of zero-inflated quasi likelihood. |
edata |
lncRNA counts or expression matrix. |
design.matrix |
The design matrix of groups and covariates. |
link |
The specified link function. |
data('hnsc.edata','design') # 'hnsc.edata' contains FPKM of 1000 lncRNA genes and 80 samples # 'design' is the design matrix for tissue and batch. # For the first 100 genes # Fit GLM by ZIQML with logarithmic link function fit.log=ZIQML.fit(edata=hnsc.edata[1:100,],design.matrix=design,link='log') # Fit GLM by ZIQML with identity link function fit.identity=ZIQML.fit(edata=hnsc.edata[1:100,],design.matrix=design,link='identity')data('hnsc.edata','design') # 'hnsc.edata' contains FPKM of 1000 lncRNA genes and 80 samples # 'design' is the design matrix for tissue and batch. # For the first 100 genes # Fit GLM by ZIQML with logarithmic link function fit.log=ZIQML.fit(edata=hnsc.edata[1:100,],design.matrix=design,link='log') # Fit GLM by ZIQML with identity link function fit.identity=ZIQML.fit(edata=hnsc.edata[1:100,],design.matrix=design,link='identity')