| Type: | Package |
| Title: | Expression RNA-Seq Data Analysis Based on Linear Mixed Model |
| Version: | 0.2.0 |
| Author: | Kyungtaek Park <qkrrudxor147@snu.ac.kr> |
| Maintainer: | Kyungtaek Park <qkrrudxor147@snu.ac.kr> |
| Description: | Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information. |
| Depends: | R (≥ 2.15.0), edgeR, limma, MASS, parallel, stats, methods |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2019-04-24 13:21:01 UTC; KyungtaekPark |
| Repository: | CRAN |
| Date/Publication: | 2019-04-25 10:40:07 UTC |
Class Balli
Class Balli holds results from BALLI
Description
Class Balli
Class Balli holds results from BALLI
Class LargeDataObject
Class LargeDataObject holds large data such as technical variance and results from BALLI fit
Description
Class LargeDataObject
Class LargeDataObject holds large data such as technical variance and results from BALLI fit
Class TecVarList
Class TecVarList holds technical variance
Description
Class TecVarList
Class TecVarList holds technical variance
BALLI
Description
DEG analysis using BALLI algorithm
Usage
balli(object, intV = 2, logcpm = NULL, tecVar = NULL,
design = NULL, numCores = NULL, threshold = 1e-06, maxiter = 200)
Arguments
object |
a TecVarList object |
intV |
numeric vector designating interest variable(s) which is(are) column number(s) of design matrix |
logcpm |
logcpm values for each gene and each sample |
tecVar |
estimated technical variance values for each gene and each sample |
design |
design matrix with samples in row and covariable(s) to be estimated in column |
numCores |
number of cores to be used for multithreding. If NULL, a single core is used |
threshold |
threshold for convergence |
maxiter |
maximum number of iteration to converge of estimated biological variance. If not, biological variance is estimated by using Brent method |
Value
an Balli object including Result and topGenes list. Following components are shown by Result (same order of genes with input data) and topGenes (ordered by pBALLI in Result) :
log2FC |
log2 fold changes of interest variable(s) |
lLLI |
log-likelihoods estimated by LLI |
lBALLI |
log-likelihoods estimated by BALLI |
pLLI |
p-values estimated by LLI |
pBALLI |
p-values estimated by BALLI |
BCF |
Bartlett's correction factor |
expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50)))) group <- c(rep("A",10),rep("B",10)) design <- model.matrix(~group, data = expr) dge <- DGEList(counts=expr, group=group) dge <- calcNormFactors(dge) tV <- tecVarEstim(dge,design) balli(tV,intV=2)
balliFit
Description
Estimates likelihood and Bartlett correction factor using BALLI algorithm of each gene
Usage
balliFit(y_mat, x_mat, tecVar, intVar = 2, full = T, cfault = 0,
miter = 200, conv = 1e-06)
Arguments
y_mat |
numeric vector containing log-cpm values of each gene and each sample |
x_mat |
design matrix with samples in row and covariable(s) to be estimated in column |
tecVar |
numeric vector containing estimated technical variance of a gene of each sample |
intVar |
numeric vector designating interest variable(s) which is(are) column number(s) of x_mat |
full |
logical value designating full model (TRUE) or reduced model (FALSE). |
cfault |
initial value of index showing whether converged (0) or not (1). |
miter |
maximum number of iteration to converge. |
conv |
threshold for convergence |
Value
following components are estimated
ll |
log-likelihoods |
beta |
coefficients of interested variable(s) |
alpha |
coefficients of nuisance variable(s) |
BCF |
Bartlett's correction factor |
cfault |
index whether converged or not |
Examples
expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50))))
group <- c(rep("A",10),rep("B",10))
design <- model.matrix(~group, data = expr)
dge <- DGEList(counts=expr, group=group)
dge <- calcNormFactors(dge)
tV <- tecVarEstim(dge,design)
gtv <- tV$tecVar[1,]
gdat <- data.frame(logcpm=tV$logcpm[1,],design,tecVar=gtv)
gy <- matrix(unlist(gdat[,1]),ncol=1)
gx <- matrix(unlist(gdat[,2:(ncol(gdat)-1)]),ncol=ncol(gdat)-2)
balliFit(y_mat=gy,x_mat=gx,tecVar=gtv,intVar=2,full=TRUE,cfault=0,miter=200,conv=1e-6)
Technical Variance Estimation
Description
Estimate technical variance by using voom-trend. The code is derived from voom function in limma package
Usage
tecVarEstim(counts, design = NULL, lib.size = NULL, span = 0.5, ...)
Arguments
counts |
a DGEList object |
design |
design matrix with samples in row and coefficient(s) to be estimated in column |
lib.size |
numeric vector containing total library sizes for each sample |
span |
width of the lowess smoothing window as a proportion |
... |
other arguments are passed to lmFit. |
Value
an TecVarList object with the following components:
targets |
matrix containing covariables, library sizes and normalization foctors of each sample |
design |
design matrix with samples in row and covariable(s) to be estimated in column |
logcpm |
logcpm values of each gene and each sample |
tecVar |
estimated techical variance of each gene and each sample |
Examples
expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50))))
group <- c(rep("A",10),rep("B",10))
design <- model.matrix(~group, data = expr)
dge <- DGEList(counts=expr, group=group)
dge <- calcNormFactors(dge)
tecVarEstim(dge,design)