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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:birta.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings birta_1.32.0.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.11-bioc/meat/birta.Rcheck'
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'birta/DESCRIPTION' ... OK
* this is package 'birta' version '1.32.0'
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'birta' can be installed ... WARNING
Found the following significant warnings:
Rd warning: C:/Users/biocbuild/bbs-3.11-bioc/tmpdir/Rtmp8gmmRN/R.INSTALLc105ee44acb/birta/man/plotConvergence.Rd:17: file link 'birta' in package 'birta' does not exist and so has been treated as a topic
Warning: Package 'birta' is deprecated and will be removed from Bioconductor
See 'C:/Users/biocbuild/bbs-3.11-bioc/meat/birta.Rcheck/00install.out' for details.
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... NOTE
Packages in Depends field not imported from:
'Biobase' 'methods'
These packages need to be imported from (in the NAMESPACE file)
for when this namespace is loaded but not attached.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
FisherPretest : <anonymous>: no visible global function definition for
'fisher.test'
FisherPretest: no visible global function definition for 'p.adjust'
birtaRun: no visible global function definition for 'median'
birtaRun: no visible global function definition for 'var'
plotConvergence: no visible global function definition for 'abline'
birta,ExpressionSet-ExpressionSet-ExpressionSet: no visible global
function definition for 'exprs'
birta,ExpressionSet-ExpressionSet-ExpressionSet: no visible global
function definition for 'callGeneric'
birta,ExpressionSet-ExpressionSet-missing: no visible global function
definition for 'exprs'
birta,ExpressionSet-ExpressionSet-missing: no visible global function
definition for 'callGeneric'
birta,ExpressionSet-missing-ExpressionSet: no visible global function
definition for 'exprs'
birta,ExpressionSet-missing-ExpressionSet: no visible global function
definition for 'callGeneric'
birta,ExpressionSet-missing-missing: no visible global function
definition for 'exprs'
birta,ExpressionSet-missing-missing: no visible global function
definition for 'callGeneric'
limmaAnalysis,ExpressionSet-matrix-character: no visible global
function definition for 'exprs'
limmaAnalysis,ExpressionSet-matrix-character: no visible global
function definition for 'callGeneric'
Undefined global functions or variables:
abline callGeneric exprs fisher.test median p.adjust var
Consider adding
importFrom("graphics", "abline")
importFrom("methods", "callGeneric")
importFrom("stats", "fisher.test", "median", "p.adjust", "var")
to your NAMESPACE file (and ensure that your DESCRIPTION Imports field
contains 'methods').
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files for i386 is not available
Note: information on .o files for x64 is not available
File 'C:/Users/biocbuild/bbs-3.11-bioc/R/library/birta/libs/i386/birta.dll':
Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
Found 'printf', possibly from 'printf' (C)
Found 'rand', possibly from 'rand' (C)
Found 'srand', possibly from 'srand' (C)
File 'C:/Users/biocbuild/bbs-3.11-bioc/R/library/birta/libs/x64/birta.dll':
Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
Found 'printf', possibly from 'printf' (C)
Found 'rand', possibly from 'rand' (C)
Found 'srand', possibly from 'srand' (C)
Compiled code should not call entry points which might terminate R nor
write to stdout/stderr instead of to the console, nor use Fortran I/O
nor system RNGs. The detected symbols are linked into the code but
might come from libraries and not actually be called.
See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... ERROR
Running examples in 'birta-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: birta
> ### Title: Main interface for Bayesian Inference of Regulation of
> ### Transcriptional Activity.
> ### Aliases: birta
> ### Keywords: htest
>
> ### ** Examples
>
> data(humanSim)
> design = model.matrix(~0+factor(c(rep("control", 5), rep("treated", 5))))
> colnames(design) = c("control", "treated")
> contrasts = "treated - control"
> limmamRNA = limmaAnalysis(sim$dat.mRNA, design, contrasts)
> limmamiRNA = limmaAnalysis(sim$dat.miRNA, design, contrasts)
> sim_result = birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA=limmamRNA,
+ limmamiRNA=limmamiRNA, nrep=c(5,5,5,5), genesets=genesets,
+ model="all-plug-in", niter=50000, nburnin=10000,
+ sample.weights=FALSE, potential_swaps=potential_swaps)
Formatting regulator-target network -> checking overlap between network and measurements.
30 DE gene(s) have 69 regulating TFs and 328 regulating miRNAs
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
birta
--- call from context ---
birtaStart(mRNAexpr = dat.mRNA, miRNAexpr = dat.miRNA, genesetsTF = genesetsTFs,
genesetsmiRNA = genesetsmiRNA, alpha_i = alpha_i, alpha_i0 = alpha_i0,
alpha = alpha, beta = beta, b_j = b.mRNA, replicates = nrep,
niter = niter, burnin = nburnin, thin = thin, model = model,
only_switches = only_switches, nomiRNA = is.null(genesetsmiRNA),
noTF = is.null(genesetsTFs), omega_miRNA = omegamiRNA, omega_TF = omegaTF,
potential_swaps = potential_swaps, theta_TF = theta_TF, theta_miRNA = theta_miRNA,
weightSampleMean = weightSampleMean, weightSampleVariance = weightSampleVariance,
weight_samples_per_move = weight_samples_per_move, equal.regulator.weights = one.regulator.weight,
A_sigma = A_Sigma, O_sigma = O_Sigma, lambda_omega = mylambda,
init_S = init_miR, init_T = init_TF, condition.specific.inference = condition.specific.inference,
TFexpr = TFexpr, alpha_i0TF = alpha_i0TF, alpha_iTF = alpha_iTF,
TF_sigma = TF_Sigma, alphaTF = alphaTF, betaTF = betaTF)
--- call from argument ---
is.null(potential_swaps) || sapply(potential_swaps[["T_potential_swaps"]],
length) != sapply(potential_swaps.orig[["T_potential_swaps"]],
length)
--- R stacktrace ---
where 1: birtaStart(mRNAexpr = dat.mRNA, miRNAexpr = dat.miRNA, genesetsTF = genesetsTFs,
genesetsmiRNA = genesetsmiRNA, alpha_i = alpha_i, alpha_i0 = alpha_i0,
alpha = alpha, beta = beta, b_j = b.mRNA, replicates = nrep,
niter = niter, burnin = nburnin, thin = thin, model = model,
only_switches = only_switches, nomiRNA = is.null(genesetsmiRNA),
noTF = is.null(genesetsTFs), omega_miRNA = omegamiRNA, omega_TF = omegaTF,
potential_swaps = potential_swaps, theta_TF = theta_TF, theta_miRNA = theta_miRNA,
weightSampleMean = weightSampleMean, weightSampleVariance = weightSampleVariance,
weight_samples_per_move = weight_samples_per_move, equal.regulator.weights = one.regulator.weight,
A_sigma = A_Sigma, O_sigma = O_Sigma, lambda_omega = mylambda,
init_S = init_miR, init_T = init_TF, condition.specific.inference = condition.specific.inference,
TFexpr = TFexpr, alpha_i0TF = alpha_i0TF, alpha_iTF = alpha_iTF,
TF_sigma = TF_Sigma, alphaTF = alphaTF, betaTF = betaTF)
where 2: birtaRun(dat.mRNA, dat.miRNA, TFexpr = NULL, limmamRNA, limmamiRNA,
limmaTF, nrep, fdr.mRNA, fdr.miRNA, lfc.mRNA, lfc.miRNA,
genesets, lambda, sample.weights, one.regulator.weight, theta_TF,
theta_miRNA, model, niter, nburnin, thin, potential_swaps,
run.pretest, condition.specific.inference, only_switches,
weightSampleMean, weightSampleVariance)
where 3: birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA = limmamRNA, limmamiRNA = limmamiRNA,
nrep = c(5, 5, 5, 5), genesets = genesets, model = "all-plug-in",
niter = 50000, nburnin = 10000, sample.weights = FALSE, potential_swaps = potential_swaps)
where 4: birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA = limmamRNA, limmamiRNA = limmamiRNA,
nrep = c(5, 5, 5, 5), genesets = genesets, model = "all-plug-in",
niter = 50000, nburnin = 10000, sample.weights = FALSE, potential_swaps = potential_swaps)
--- value of length: 156 type: logical ---
V$AIRE_01 V$AP3_Q6 V$CEBPA_01
FALSE FALSE FALSE
V$EN1_01 V$FOXJ2_02 V$HELIOSA_01
FALSE FALSE FALSE
V$HOXA4_Q2 V$PAX2_02 V$STAT5A_03
FALSE FALSE FALSE
V$STAT5A_04 V$TFIIA_Q6 V$USF_02
FALSE FALSE FALSE
V$ELK1_01 V$HOXA3_01 V$PAX5_02
FALSE FALSE FALSE
V$YY1_01 V$CEBP_Q2_01 V$CEBP_Q2
FALSE FALSE FALSE
V$HNF4_Q6_02 V$HNF4_Q6 V$PAX4_04
FALSE FALSE FALSE
V$PXR_Q2 V$FOXP3_Q4 V$GATA4_Q3
FALSE FALSE FALSE
V$MAF_Q6_01 V$CEBP_Q3 V$CEBP_01
FALSE FALSE FALSE
V$NF1_Q6_01 V$NF1_Q6 V$TBX5_02
FALSE FALSE FALSE
V$TEF1_Q6 V$GFI1_01 V$PR_Q2
FALSE FALSE FALSE
V$GR_Q6_01 V$OCT1_06 V$AREB6_02
FALSE FALSE FALSE
V$STAT4_01 V$GATA6_01 V$ELF1_Q6
FALSE FALSE FALSE
V$STAT6_01 V$NKX62_Q2 V$PAX8_01
FALSE FALSE FALSE
V$PAX8_B V$RFX_Q6 V$P53_DECAMER_Q2
FALSE FALSE FALSE
V$GCM_Q2 V$CRX_Q4 V$SRY_01
FALSE FALSE FALSE
V$PAX5_01 V$AP4_01 V$AP4_Q6
FALSE FALSE FALSE
V$E2F1_Q3 V$ETS2_B V$HES1_Q2
FALSE FALSE FALSE
V$E2F1_Q4 V$GR_Q6 V$E2F1_Q3_01
FALSE FALSE FALSE
V$ATF4_Q2 V$NERF_Q2 V$OLF1_01
FALSE FALSE FALSE
V$P300_01 V$TST1_01 V$USF2_Q6
FALSE FALSE FALSE
V$ZIC1_01 V$AREB6_04 V$MZF1_01
FALSE FALSE FALSE
V$VDR_Q3 V$STAT_Q6 V$PAX4_03
FALSE FALSE FALSE
V$SMAD4_Q6 V$SMAD_Q6 V$ZIC2_01
FALSE FALSE FALSE
V$PBX1_03 V$PBX_Q3 V$CEBP_C
FALSE FALSE FALSE
V$DELTAEF1_01 V$FOXM1_01 V$TCF4_Q5
FALSE FALSE FALSE
V$TTF1_Q6 V$AP4_Q5 V$MYOD_01
FALSE FALSE FALSE
V$SP3_Q3 V$MSX1_01 V$NCX_01
FALSE FALSE FALSE
V$GATA1_06 V$SREBP_Q3 V$EGR_Q6
FALSE FALSE FALSE
V$SREBP1_Q6 V$GATA1_05 V$EFC_Q6
FALSE FALSE FALSE
V$LBP1_Q6 V$ETS1_B V$TAL1_Q6
FALSE FALSE FALSE
V$STAT1_02 V$CETS1P54_01 V$CETS1P54_02
FALSE FALSE FALSE
V$OCT1_05 V$STAT6_02 V$MYB_Q6
FALSE FALSE FALSE
V$T3R_Q6 V$NFAT_Q4_01 V$NFAT_Q6
FALSE FALSE FALSE
V$CP2_02 V$SMAD_Q6_01 V$AP1_Q6_01
FALSE FALSE FALSE
V$DEC_Q1 V$HMGIY_Q3 V$AREB6_01
FALSE FALSE FALSE
V$TBX5_01 V$SOX5_01 V$NKX25_02
FALSE FALSE FALSE
V$HNF1_C V$HELIOSA_02 V$AHR_Q5
FALSE FALSE FALSE
V$GATA1_03 V$CEBPB_02 V$CEBPB_01
FALSE FALSE FALSE
V$EVI1_05 V$EVI1_02 V$CDC5_01
FALSE FALSE FALSE
V$AFP1_Q6 V$E2F1_Q6 V$SOX9_B1
FALSE FALSE FALSE
V$GATA3_03 V$OCT1_02 V$TFE_Q6
FALSE FALSE FALSE
V$AP2ALPHA_02 V$HMGIY_Q6 V$FOX_Q2
FALSE FALSE FALSE
V$LYF1_01 V$MYOD_Q6 V$EBF_Q6
FALSE FALSE FALSE
V$OSF2_Q6 V$FAC1_01 V$ETS_Q6
FALSE FALSE FALSE
V$ETS_Q4 V$TEF_Q6 V$GABP_B
FALSE FALSE FALSE
V$CEBPGAMMA_Q6 V$AP1_Q4_01 V$AML1_Q6
FALSE FALSE FALSE
V$AP2REP_01 V$POU3F2_02 V$CEBPDELTA_Q6
FALSE FALSE FALSE
V$P53_02 V$E47_02 V$IRF_Q6
FALSE FALSE FALSE
V$FOXO4_02 V$COUPTF_Q6 V$CACCCBINDINGFACTOR_Q6
FALSE FALSE FALSE
V$TITF1_Q3 V$ERR1_Q2 V$STAT1_03
FALSE FALSE FALSE
V$CREB_Q3 V$CART1_01 V$HNF3B_01
FALSE FALSE FALSE
--- function from context ---
function (mRNAexpr, miRNAexpr = NULL, mRNA.data.type = c("array",
"RNAseq"), miRNA.data.type = c("array", "RNAseq"), genesetsTF = NULL,
genesetsmiRNA = NULL, replicates = c(5, 5, 5, 5), n0 = 1,
alpha = 1, beta = 0.1, alpha_i = NULL, alpha_i0 = NULL, b_j,
niter = 1e+06, burnin = 5 * 1e+05, thin = 50, model = c("all-plug-in",
"no-plug-in"), only_switches = FALSE, noTF = FALSE, nomiRNA = FALSE,
A_sigma = NULL, O_sigma, omega_miRNA = NULL, omega_TF = NULL,
weightSampleMean = 0, weightSampleVariance = 1, equal.regulator.weights = TRUE,
potential_swaps = NULL, weight_samples_per_move = 10, theta_TF = 0.01,
theta_miRNA = 0.01, lambda_omega = 0, init_S = NULL, init_T = NULL,
condition.specific.inference = TRUE, TFexpr = NULL, alpha_i0TF = NULL,
alpha_iTF = NULL, TF_sigma = NULL, alphaTF = NULL, betaTF = NULL)
{
model = match.arg(model, several.ok = FALSE)
stopifnot(model %in% c("all-plug-in", "no-plug-in"))
model = switch(model, `no-plug-in` = 3, `all-plug-in` = 1)
mRNA.data.type = match.arg(mRNA.data.type, several.ok = FALSE)
miRNA.data.type = match.arg(miRNA.data.type, several.ok = FALSE)
if (!is.null(TFexpr)) {
tfexpr = rownames(TFexpr)
tfnotexpr = names(genesetsTF)[(!(names(genesetsTF) %in%
tfexpr))]
genesetsTF = genesetsTF[c(tfexpr, tfnotexpr)]
omega_TF = omega_TF[c(tfexpr, tfnotexpr)]
}
potential_swaps.orig = potential_swaps
genesetsmiRNA = sapply(genesetsmiRNA, function(s) intersect(s,
rownames(mRNAexpr)))
genesetsTF = sapply(genesetsTF, function(s) intersect(s,
rownames(mRNAexpr)))
genesetsmiRNA = genesetsmiRNA[unique(names(genesetsmiRNA))]
genesetsTF = genesetsTF[unique(names(genesetsTF))]
omega_TF = sapply(omega_TF, function(s) s[intersect(names(s),
rownames(mRNAexpr))])
omega_miRNA = sapply(omega_miRNA, function(s) s[intersect(names(s),
rownames(mRNAexpr))])
if (length(genesetsmiRNA) != length(omega_miRNA) | !all(sapply(genesetsmiRNA,
length) == sapply(omega_miRNA, length)))
stop("dimensions of genesetsmiRNA have to equal the dimensions of omega_miRNA")
if (length(genesetsTF) != length(omega_TF) | !all(sapply(genesetsTF,
length) == sapply(omega_TF, length)))
stop("dimensions of genesetsTF have to equal the dimensions of omega_TF")
if (any(sapply(genesetsmiRNA, length) == 0) | any(sapply(genesetsTF,
length) == 0)) {
warning("Not all genesets have non-zero length --> removing empty genesets")
genesetsmiRNA = genesetsmiRNA[sapply(genesetsmiRNA, length) >
0]
genesetsTF = genesetsTF[sapply(genesetsTF, length) >
0]
omega_miRNA = omega_miRNA[sapply(omega_miRNA, length) >
0]
omega_TF = omega_TF[sapply(omega_TF, length) > 0]
}
if (!nomiRNA & NROW(miRNAexpr) > 0) {
common.miRNAs = intersect(names(genesetsmiRNA), rownames(miRNAexpr))
genesetsmiRNA = genesetsmiRNA[common.miRNAs]
omega_miRNA = omega_miRNA[common.miRNAs]
miRNAexpr = miRNAexpr[common.miRNAs, ]
A_sigma = A_sigma[common.miRNAs]
alpha_i = alpha_i[common.miRNAs]
alpha_i0 = alpha_i0[common.miRNAs]
if (!is.null(init_S))
init_S = init_S[, common.miRNAs]
}
regulon = union(unlist(genesetsTF), unlist(genesetsmiRNA))
mRNAexpr = mRNAexpr[rownames(mRNAexpr) %in% regulon, ]
O_sigma = O_sigma[names(O_sigma) %in% regulon]
b_j = b_j[names(b_j) %in% regulon]
if ((!only_switches)) {
if (is.null(potential_swaps) || sapply(potential_swaps[["T_potential_swaps"]],
length) != sapply(potential_swaps.orig[["T_potential_swaps"]],
length) || sapply(potential_swaps[["S_potential_swaps"]],
length) != sapply(potential_swaps.orig[["S_potential_swaps"]],
length)) {
potential_swaps = get_potential_swaps(genesetsTF,
genesetsmiRNA)
}
}
if (!is.null(A_sigma) & length(A_sigma) != NROW(miRNAexpr))
stop("length of A_sigma has to equal number of miRNAs!")
if (length(alpha_i) != NROW(miRNAexpr))
stop("length of alpha_i has to equal number of miRNAs!")
if (length(alpha_i0) != NROW(miRNAexpr))
stop("length of alpha_i0 has to equal number of miRNAs!")
if (!is.null(O_sigma) & length(O_sigma) != NROW(mRNAexpr))
stop("length of O_sigma has to equal number of mRNAs!")
if (length(b_j) != NROW(mRNAexpr))
stop("length of b_j has to equal number of mRNAs!")
if (length(replicates) != 4)
stop("Length of replicates has equal 4! Put 0, if miRNA data is missing.")
if (is.null(init_T)) {
init_T = matrix(0, nrow = 2, length(genesetsTF))
colnames(init_T) = names(genesetsTF)
}
if (is.null(init_S) & NROW(miRNAexpr) > 0) {
init_S = matrix(0, nrow = 2, ncol = NROW(miRNAexpr))
colnames(init_S) = rownames(miRNAexpr)
}
if (!is.null(init_T) & NCOL(init_T) != length(genesetsTF))
stop("length of init_T has to equal length of genesetsTF")
if (!is.null(init_S) & NCOL(init_S) != length(genesetsmiRNA))
stop("length of init_S has to equal length of genesetsmiRNA")
if (!is.null(miRNAexpr)) {
if (length(genesetsmiRNA) != NROW(miRNAexpr))
stop("length of genesetsmiRNA and NROW(miRNAexpr) have to be equal!")
}
lambda_omega = abs(lambda_omega)
theta_miRNA = abs(theta_miRNA)
theta_TF = abs(theta_TF)
alpha = abs(alpha)
beta = abs(beta)
n0 = abs(n0)
weight_samples_per_move = abs(weight_samples_per_move)
weightSampleVariance = abs(weightSampleVariance)
burnin = abs(burnin)
thin = abs(thin)
niter = abs(niter)
miRNA = names(genesetsmiRNA)
mRNA = rownames(mRNAexpr)
TF = names(genesetsTF)
A_cnt = as.integer(length(miRNA))
T_cnt = as.integer(length(TF))
if (nomiRNA) {
A_cnt = 0
use_miRNA_expression = 0
}
else {
use_miRNA_expression = (NROW(miRNAexpr) > 0)
}
if (noTF) {
T_cnt = 0
}
init_T = init_T[, TF]
mirTargets = genesetsmiRNA[miRNA]
mirTargets = lapply(mirTargets, function(x) {
which(mRNA %in% x)
})
TFtargets = genesetsTF[TF]
TFtargets = lapply(TFtargets, function(x) {
which(mRNA %in% x)
})
cat("\nBIRTA\n")
cat("Data and network: #mRNAs = ", nrow(mRNAexpr), "#miRNAs = ",
A_cnt, "#TFs = ", T_cnt, "only one weight per regulator = ",
equal.regulator.weights, "\n")
cat("Prior parameters: theta_TF = ", theta_TF, "theta_miRNA = ",
theta_miRNA, "lambda = ", lambda_omega, "\n")
if (model != "all-plug-in")
cat("Hyperparameters: alpha = ", alpha, " beta = ", beta,
" n0 = ", n0, "\n")
cat("MCMC parameters: burnin = ", burnin, "niter = ", niter,
"thin = ", thin, "condition specific inference = ", condition.specific.inference,
"\n\n")
nTFexpr = 0
if (!is.null(TFexpr)) {
nTFexpr = as.integer(dim(TFexpr)[1])
}
result = .Call("getStates", nmRNA = as.integer(length(mRNA)),
mRNA = as.character(mRNA), nmiRNA = as.integer(A_cnt),
miRNA = as.character(miRNA), nTF = as.integer(T_cnt),
TF = as.character(TF), replicates = as.integer(replicates),
mRNA_expression = as.numeric(mRNAexpr), miRNA_expression = as.numeric(miRNAexpr),
mRNADataType = as.integer((mRNA.data.type == "RNAseq") *
1), miRNADataType = as.integer((miRNA.data.type ==
"RNAseq") * 1), use_miRNA_expression = as.integer(use_miRNA_expression),
genesetsmiRNA = mirTargets, genesetsTF = TFtargets, n0 = as.numeric(n0),
alpha = as.numeric(alpha), beta = as.numeric(beta), alpha_i0 = as.numeric(alpha_i0),
alpha_i = as.numeric(alpha_i), b_j = as.numeric(b_j),
omega_miRNA = omega_miRNA, omega_TF = omega_TF, niter = as.integer(niter),
A_sigma = as.numeric(A_sigma), O_sigma = as.numeric(O_sigma),
model = as.integer(model), burnin = as.integer(burnin),
thin = as.integer(thin), only_switches = as.integer(only_switches),
T_potential_swaps = sapply(potential_swaps$T_potential_swaps,
as.integer), S_potential_swaps = sapply(potential_swaps$S_potential_swaps,
as.integer), weightSampleMean = as.numeric(weightSampleMean),
weightSampleVariance = as.numeric(weightSampleVariance),
weight_samples_per_move = as.integer(weight_samples_per_move),
theta_TF = as.numeric(theta_TF), theta_miRNA = as.numeric(theta_miRNA),
lambda_omega = as.numeric(lambda_omega), init_S = as.integer(init_S),
init_T = as.integer(init_T), condition_specific = as.integer(condition.specific.inference),
equal_regulator_weights = as.integer(equal.regulator.weights),
TFexpr = as.numeric(TFexpr), nTFexpr = as.integer(nTFexpr),
alpha_i0TF = as.numeric(alpha_i0TF), alpha_iTF = as.numeric(alpha_iTF),
TF_sigma = as.numeric(TF_sigma), alphaTF = as.numeric(alphaTF),
betaTF = as.numeric(betaTF), PACKAGE = "birta")
if ((!noTF) & (!condition.specific.inference)) {
names(result$TFstates1) = TF
names(result$TFstates2) = TF
result$TFActivitySwitch = result$TFstates2
result = result[-which(names(result) %in% c("TFstates1",
"TFstates2"))]
}
if (!nomiRNA) {
names(result$miRNAstates1) = miRNA
names(result$miRNAstates2) = miRNA
if (!condition.specific.inference) {
result$miRNAactivitySwitch = result$miRNAstates2
result$miRNAactivitySwitch[which(result$miRNAstates1 >
0)] = result$miRNAstates1[which(result$miRNAstates1 >
0)]
result = result[-which(names(result) %in% c("miRNAstates1",
"miRNAstates2"))]
}
}
if ((!is.null(TFexpr)) > 0 & condition.specific.inference) {
names(result$TFstates1) = TF
names(result$TFstates2) = TF
result$TFActivitySwitch = result$TFstates2[(!(names(result$TFstates2) %in%
rownames(TFexpr)))]
result$TFstates1 = result$TFstates1[rownames(TFexpr)]
result$TFstates2 = result$TFstates2[rownames(TFexpr)]
}
if ((is.null(TFexpr)) > 0 & condition.specific.inference) {
names(result$TFstates1) = TF
names(result$TFstates2) = TF
result$TFActivitySwitch = result$TFstates2
result = result[-which(names(result) %in% c("TFstates1",
"TFstates2"))]
}
result$genesetsTF = genesetsTF
result$genesetsmiRNA = genesetsmiRNA
result$mRNAexpr = mRNAexpr
result$miRNAexpr = miRNAexpr
return(result)
}
<bytecode: 0x03eb3258>
<environment: namespace:birta>
--- function search by body ---
Function birtaStart in namespace birta has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
** running examples for arch 'x64' ... ERROR
Running examples in 'birta-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: birta
> ### Title: Main interface for Bayesian Inference of Regulation of
> ### Transcriptional Activity.
> ### Aliases: birta
> ### Keywords: htest
>
> ### ** Examples
>
> data(humanSim)
> design = model.matrix(~0+factor(c(rep("control", 5), rep("treated", 5))))
> colnames(design) = c("control", "treated")
> contrasts = "treated - control"
> limmamRNA = limmaAnalysis(sim$dat.mRNA, design, contrasts)
> limmamiRNA = limmaAnalysis(sim$dat.miRNA, design, contrasts)
> sim_result = birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA=limmamRNA,
+ limmamiRNA=limmamiRNA, nrep=c(5,5,5,5), genesets=genesets,
+ model="all-plug-in", niter=50000, nburnin=10000,
+ sample.weights=FALSE, potential_swaps=potential_swaps)
Formatting regulator-target network -> checking overlap between network and measurements.
30 DE gene(s) have 69 regulating TFs and 328 regulating miRNAs
----------- FAILURE REPORT --------------
--- failure: length > 1 in coercion to logical ---
--- srcref ---
:
--- package (from environment) ---
birta
--- call from context ---
birtaStart(mRNAexpr = dat.mRNA, miRNAexpr = dat.miRNA, genesetsTF = genesetsTFs,
genesetsmiRNA = genesetsmiRNA, alpha_i = alpha_i, alpha_i0 = alpha_i0,
alpha = alpha, beta = beta, b_j = b.mRNA, replicates = nrep,
niter = niter, burnin = nburnin, thin = thin, model = model,
only_switches = only_switches, nomiRNA = is.null(genesetsmiRNA),
noTF = is.null(genesetsTFs), omega_miRNA = omegamiRNA, omega_TF = omegaTF,
potential_swaps = potential_swaps, theta_TF = theta_TF, theta_miRNA = theta_miRNA,
weightSampleMean = weightSampleMean, weightSampleVariance = weightSampleVariance,
weight_samples_per_move = weight_samples_per_move, equal.regulator.weights = one.regulator.weight,
A_sigma = A_Sigma, O_sigma = O_Sigma, lambda_omega = mylambda,
init_S = init_miR, init_T = init_TF, condition.specific.inference = condition.specific.inference,
TFexpr = TFexpr, alpha_i0TF = alpha_i0TF, alpha_iTF = alpha_iTF,
TF_sigma = TF_Sigma, alphaTF = alphaTF, betaTF = betaTF)
--- call from argument ---
is.null(potential_swaps) || sapply(potential_swaps[["T_potential_swaps"]],
length) != sapply(potential_swaps.orig[["T_potential_swaps"]],
length)
--- R stacktrace ---
where 1: birtaStart(mRNAexpr = dat.mRNA, miRNAexpr = dat.miRNA, genesetsTF = genesetsTFs,
genesetsmiRNA = genesetsmiRNA, alpha_i = alpha_i, alpha_i0 = alpha_i0,
alpha = alpha, beta = beta, b_j = b.mRNA, replicates = nrep,
niter = niter, burnin = nburnin, thin = thin, model = model,
only_switches = only_switches, nomiRNA = is.null(genesetsmiRNA),
noTF = is.null(genesetsTFs), omega_miRNA = omegamiRNA, omega_TF = omegaTF,
potential_swaps = potential_swaps, theta_TF = theta_TF, theta_miRNA = theta_miRNA,
weightSampleMean = weightSampleMean, weightSampleVariance = weightSampleVariance,
weight_samples_per_move = weight_samples_per_move, equal.regulator.weights = one.regulator.weight,
A_sigma = A_Sigma, O_sigma = O_Sigma, lambda_omega = mylambda,
init_S = init_miR, init_T = init_TF, condition.specific.inference = condition.specific.inference,
TFexpr = TFexpr, alpha_i0TF = alpha_i0TF, alpha_iTF = alpha_iTF,
TF_sigma = TF_Sigma, alphaTF = alphaTF, betaTF = betaTF)
where 2: birtaRun(dat.mRNA, dat.miRNA, TFexpr = NULL, limmamRNA, limmamiRNA,
limmaTF, nrep, fdr.mRNA, fdr.miRNA, lfc.mRNA, lfc.miRNA,
genesets, lambda, sample.weights, one.regulator.weight, theta_TF,
theta_miRNA, model, niter, nburnin, thin, potential_swaps,
run.pretest, condition.specific.inference, only_switches,
weightSampleMean, weightSampleVariance)
where 3: birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA = limmamRNA, limmamiRNA = limmamiRNA,
nrep = c(5, 5, 5, 5), genesets = genesets, model = "all-plug-in",
niter = 50000, nburnin = 10000, sample.weights = FALSE, potential_swaps = potential_swaps)
where 4: birta(sim$dat.mRNA, sim$dat.miRNA, limmamRNA = limmamRNA, limmamiRNA = limmamiRNA,
nrep = c(5, 5, 5, 5), genesets = genesets, model = "all-plug-in",
niter = 50000, nburnin = 10000, sample.weights = FALSE, potential_swaps = potential_swaps)
--- value of length: 156 type: logical ---
V$AIRE_01 V$AP3_Q6 V$CEBPA_01
FALSE FALSE FALSE
V$EN1_01 V$FOXJ2_02 V$HELIOSA_01
FALSE FALSE FALSE
V$HOXA4_Q2 V$PAX2_02 V$STAT5A_03
FALSE FALSE FALSE
V$STAT5A_04 V$TFIIA_Q6 V$USF_02
FALSE FALSE FALSE
V$ELK1_01 V$HOXA3_01 V$PAX5_02
FALSE FALSE FALSE
V$YY1_01 V$CEBP_Q2_01 V$CEBP_Q2
FALSE FALSE FALSE
V$HNF4_Q6_02 V$HNF4_Q6 V$PAX4_04
FALSE FALSE FALSE
V$PXR_Q2 V$FOXP3_Q4 V$GATA4_Q3
FALSE FALSE FALSE
V$MAF_Q6_01 V$CEBP_Q3 V$CEBP_01
FALSE FALSE FALSE
V$NF1_Q6_01 V$NF1_Q6 V$TBX5_02
FALSE FALSE FALSE
V$TEF1_Q6 V$GFI1_01 V$PR_Q2
FALSE FALSE FALSE
V$GR_Q6_01 V$OCT1_06 V$AREB6_02
FALSE FALSE FALSE
V$STAT4_01 V$GATA6_01 V$ELF1_Q6
FALSE FALSE FALSE
V$STAT6_01 V$NKX62_Q2 V$PAX8_01
FALSE FALSE FALSE
V$PAX8_B V$RFX_Q6 V$P53_DECAMER_Q2
FALSE FALSE FALSE
V$GCM_Q2 V$CRX_Q4 V$SRY_01
FALSE FALSE FALSE
V$PAX5_01 V$AP4_01 V$AP4_Q6
FALSE FALSE FALSE
V$E2F1_Q3 V$ETS2_B V$HES1_Q2
FALSE FALSE FALSE
V$E2F1_Q4 V$GR_Q6 V$E2F1_Q3_01
FALSE FALSE FALSE
V$ATF4_Q2 V$NERF_Q2 V$OLF1_01
FALSE FALSE FALSE
V$P300_01 V$TST1_01 V$USF2_Q6
FALSE FALSE FALSE
V$ZIC1_01 V$AREB6_04 V$MZF1_01
FALSE FALSE FALSE
V$VDR_Q3 V$STAT_Q6 V$PAX4_03
FALSE FALSE FALSE
V$SMAD4_Q6 V$SMAD_Q6 V$ZIC2_01
FALSE FALSE FALSE
V$PBX1_03 V$PBX_Q3 V$CEBP_C
FALSE FALSE FALSE
V$DELTAEF1_01 V$FOXM1_01 V$TCF4_Q5
FALSE FALSE FALSE
V$TTF1_Q6 V$AP4_Q5 V$MYOD_01
FALSE FALSE FALSE
V$SP3_Q3 V$MSX1_01 V$NCX_01
FALSE FALSE FALSE
V$GATA1_06 V$SREBP_Q3 V$EGR_Q6
FALSE FALSE FALSE
V$SREBP1_Q6 V$GATA1_05 V$EFC_Q6
FALSE FALSE FALSE
V$LBP1_Q6 V$ETS1_B V$TAL1_Q6
FALSE FALSE FALSE
V$STAT1_02 V$CETS1P54_01 V$CETS1P54_02
FALSE FALSE FALSE
V$OCT1_05 V$STAT6_02 V$MYB_Q6
FALSE FALSE FALSE
V$T3R_Q6 V$NFAT_Q4_01 V$NFAT_Q6
FALSE FALSE FALSE
V$CP2_02 V$SMAD_Q6_01 V$AP1_Q6_01
FALSE FALSE FALSE
V$DEC_Q1 V$HMGIY_Q3 V$AREB6_01
FALSE FALSE FALSE
V$TBX5_01 V$SOX5_01 V$NKX25_02
FALSE FALSE FALSE
V$HNF1_C V$HELIOSA_02 V$AHR_Q5
FALSE FALSE FALSE
V$GATA1_03 V$CEBPB_02 V$CEBPB_01
FALSE FALSE FALSE
V$EVI1_05 V$EVI1_02 V$CDC5_01
FALSE FALSE FALSE
V$AFP1_Q6 V$E2F1_Q6 V$SOX9_B1
FALSE FALSE FALSE
V$GATA3_03 V$OCT1_02 V$TFE_Q6
FALSE FALSE FALSE
V$AP2ALPHA_02 V$HMGIY_Q6 V$FOX_Q2
FALSE FALSE FALSE
V$LYF1_01 V$MYOD_Q6 V$EBF_Q6
FALSE FALSE FALSE
V$OSF2_Q6 V$FAC1_01 V$ETS_Q6
FALSE FALSE FALSE
V$ETS_Q4 V$TEF_Q6 V$GABP_B
FALSE FALSE FALSE
V$CEBPGAMMA_Q6 V$AP1_Q4_01 V$AML1_Q6
FALSE FALSE FALSE
V$AP2REP_01 V$POU3F2_02 V$CEBPDELTA_Q6
FALSE FALSE FALSE
V$P53_02 V$E47_02 V$IRF_Q6
FALSE FALSE FALSE
V$FOXO4_02 V$COUPTF_Q6 V$CACCCBINDINGFACTOR_Q6
FALSE FALSE FALSE
V$TITF1_Q3 V$ERR1_Q2 V$STAT1_03
FALSE FALSE FALSE
V$CREB_Q3 V$CART1_01 V$HNF3B_01
FALSE FALSE FALSE
--- function from context ---
function (mRNAexpr, miRNAexpr = NULL, mRNA.data.type = c("array",
"RNAseq"), miRNA.data.type = c("array", "RNAseq"), genesetsTF = NULL,
genesetsmiRNA = NULL, replicates = c(5, 5, 5, 5), n0 = 1,
alpha = 1, beta = 0.1, alpha_i = NULL, alpha_i0 = NULL, b_j,
niter = 1e+06, burnin = 5 * 1e+05, thin = 50, model = c("all-plug-in",
"no-plug-in"), only_switches = FALSE, noTF = FALSE, nomiRNA = FALSE,
A_sigma = NULL, O_sigma, omega_miRNA = NULL, omega_TF = NULL,
weightSampleMean = 0, weightSampleVariance = 1, equal.regulator.weights = TRUE,
potential_swaps = NULL, weight_samples_per_move = 10, theta_TF = 0.01,
theta_miRNA = 0.01, lambda_omega = 0, init_S = NULL, init_T = NULL,
condition.specific.inference = TRUE, TFexpr = NULL, alpha_i0TF = NULL,
alpha_iTF = NULL, TF_sigma = NULL, alphaTF = NULL, betaTF = NULL)
{
model = match.arg(model, several.ok = FALSE)
stopifnot(model %in% c("all-plug-in", "no-plug-in"))
model = switch(model, `no-plug-in` = 3, `all-plug-in` = 1)
mRNA.data.type = match.arg(mRNA.data.type, several.ok = FALSE)
miRNA.data.type = match.arg(miRNA.data.type, several.ok = FALSE)
if (!is.null(TFexpr)) {
tfexpr = rownames(TFexpr)
tfnotexpr = names(genesetsTF)[(!(names(genesetsTF) %in%
tfexpr))]
genesetsTF = genesetsTF[c(tfexpr, tfnotexpr)]
omega_TF = omega_TF[c(tfexpr, tfnotexpr)]
}
potential_swaps.orig = potential_swaps
genesetsmiRNA = sapply(genesetsmiRNA, function(s) intersect(s,
rownames(mRNAexpr)))
genesetsTF = sapply(genesetsTF, function(s) intersect(s,
rownames(mRNAexpr)))
genesetsmiRNA = genesetsmiRNA[unique(names(genesetsmiRNA))]
genesetsTF = genesetsTF[unique(names(genesetsTF))]
omega_TF = sapply(omega_TF, function(s) s[intersect(names(s),
rownames(mRNAexpr))])
omega_miRNA = sapply(omega_miRNA, function(s) s[intersect(names(s),
rownames(mRNAexpr))])
if (length(genesetsmiRNA) != length(omega_miRNA) | !all(sapply(genesetsmiRNA,
length) == sapply(omega_miRNA, length)))
stop("dimensions of genesetsmiRNA have to equal the dimensions of omega_miRNA")
if (length(genesetsTF) != length(omega_TF) | !all(sapply(genesetsTF,
length) == sapply(omega_TF, length)))
stop("dimensions of genesetsTF have to equal the dimensions of omega_TF")
if (any(sapply(genesetsmiRNA, length) == 0) | any(sapply(genesetsTF,
length) == 0)) {
warning("Not all genesets have non-zero length --> removing empty genesets")
genesetsmiRNA = genesetsmiRNA[sapply(genesetsmiRNA, length) >
0]
genesetsTF = genesetsTF[sapply(genesetsTF, length) >
0]
omega_miRNA = omega_miRNA[sapply(omega_miRNA, length) >
0]
omega_TF = omega_TF[sapply(omega_TF, length) > 0]
}
if (!nomiRNA & NROW(miRNAexpr) > 0) {
common.miRNAs = intersect(names(genesetsmiRNA), rownames(miRNAexpr))
genesetsmiRNA = genesetsmiRNA[common.miRNAs]
omega_miRNA = omega_miRNA[common.miRNAs]
miRNAexpr = miRNAexpr[common.miRNAs, ]
A_sigma = A_sigma[common.miRNAs]
alpha_i = alpha_i[common.miRNAs]
alpha_i0 = alpha_i0[common.miRNAs]
if (!is.null(init_S))
init_S = init_S[, common.miRNAs]
}
regulon = union(unlist(genesetsTF), unlist(genesetsmiRNA))
mRNAexpr = mRNAexpr[rownames(mRNAexpr) %in% regulon, ]
O_sigma = O_sigma[names(O_sigma) %in% regulon]
b_j = b_j[names(b_j) %in% regulon]
if ((!only_switches)) {
if (is.null(potential_swaps) || sapply(potential_swaps[["T_potential_swaps"]],
length) != sapply(potential_swaps.orig[["T_potential_swaps"]],
length) || sapply(potential_swaps[["S_potential_swaps"]],
length) != sapply(potential_swaps.orig[["S_potential_swaps"]],
length)) {
potential_swaps = get_potential_swaps(genesetsTF,
genesetsmiRNA)
}
}
if (!is.null(A_sigma) & length(A_sigma) != NROW(miRNAexpr))
stop("length of A_sigma has to equal number of miRNAs!")
if (length(alpha_i) != NROW(miRNAexpr))
stop("length of alpha_i has to equal number of miRNAs!")
if (length(alpha_i0) != NROW(miRNAexpr))
stop("length of alpha_i0 has to equal number of miRNAs!")
if (!is.null(O_sigma) & length(O_sigma) != NROW(mRNAexpr))
stop("length of O_sigma has to equal number of mRNAs!")
if (length(b_j) != NROW(mRNAexpr))
stop("length of b_j has to equal number of mRNAs!")
if (length(replicates) != 4)
stop("Length of replicates has equal 4! Put 0, if miRNA data is missing.")
if (is.null(init_T)) {
init_T = matrix(0, nrow = 2, length(genesetsTF))
colnames(init_T) = names(genesetsTF)
}
if (is.null(init_S) & NROW(miRNAexpr) > 0) {
init_S = matrix(0, nrow = 2, ncol = NROW(miRNAexpr))
colnames(init_S) = rownames(miRNAexpr)
}
if (!is.null(init_T) & NCOL(init_T) != length(genesetsTF))
stop("length of init_T has to equal length of genesetsTF")
if (!is.null(init_S) & NCOL(init_S) != length(genesetsmiRNA))
stop("length of init_S has to equal length of genesetsmiRNA")
if (!is.null(miRNAexpr)) {
if (length(genesetsmiRNA) != NROW(miRNAexpr))
stop("length of genesetsmiRNA and NROW(miRNAexpr) have to be equal!")
}
lambda_omega = abs(lambda_omega)
theta_miRNA = abs(theta_miRNA)
theta_TF = abs(theta_TF)
alpha = abs(alpha)
beta = abs(beta)
n0 = abs(n0)
weight_samples_per_move = abs(weight_samples_per_move)
weightSampleVariance = abs(weightSampleVariance)
burnin = abs(burnin)
thin = abs(thin)
niter = abs(niter)
miRNA = names(genesetsmiRNA)
mRNA = rownames(mRNAexpr)
TF = names(genesetsTF)
A_cnt = as.integer(length(miRNA))
T_cnt = as.integer(length(TF))
if (nomiRNA) {
A_cnt = 0
use_miRNA_expression = 0
}
else {
use_miRNA_expression = (NROW(miRNAexpr) > 0)
}
if (noTF) {
T_cnt = 0
}
init_T = init_T[, TF]
mirTargets = genesetsmiRNA[miRNA]
mirTargets = lapply(mirTargets, function(x) {
which(mRNA %in% x)
})
TFtargets = genesetsTF[TF]
TFtargets = lapply(TFtargets, function(x) {
which(mRNA %in% x)
})
cat("\nBIRTA\n")
cat("Data and network: #mRNAs = ", nrow(mRNAexpr), "#miRNAs = ",
A_cnt, "#TFs = ", T_cnt, "only one weight per regulator = ",
equal.regulator.weights, "\n")
cat("Prior parameters: theta_TF = ", theta_TF, "theta_miRNA = ",
theta_miRNA, "lambda = ", lambda_omega, "\n")
if (model != "all-plug-in")
cat("Hyperparameters: alpha = ", alpha, " beta = ", beta,
" n0 = ", n0, "\n")
cat("MCMC parameters: burnin = ", burnin, "niter = ", niter,
"thin = ", thin, "condition specific inference = ", condition.specific.inference,
"\n\n")
nTFexpr = 0
if (!is.null(TFexpr)) {
nTFexpr = as.integer(dim(TFexpr)[1])
}
result = .Call("getStates", nmRNA = as.integer(length(mRNA)),
mRNA = as.character(mRNA), nmiRNA = as.integer(A_cnt),
miRNA = as.character(miRNA), nTF = as.integer(T_cnt),
TF = as.character(TF), replicates = as.integer(replicates),
mRNA_expression = as.numeric(mRNAexpr), miRNA_expression = as.numeric(miRNAexpr),
mRNADataType = as.integer((mRNA.data.type == "RNAseq") *
1), miRNADataType = as.integer((miRNA.data.type ==
"RNAseq") * 1), use_miRNA_expression = as.integer(use_miRNA_expression),
genesetsmiRNA = mirTargets, genesetsTF = TFtargets, n0 = as.numeric(n0),
alpha = as.numeric(alpha), beta = as.numeric(beta), alpha_i0 = as.numeric(alpha_i0),
alpha_i = as.numeric(alpha_i), b_j = as.numeric(b_j),
omega_miRNA = omega_miRNA, omega_TF = omega_TF, niter = as.integer(niter),
A_sigma = as.numeric(A_sigma), O_sigma = as.numeric(O_sigma),
model = as.integer(model), burnin = as.integer(burnin),
thin = as.integer(thin), only_switches = as.integer(only_switches),
T_potential_swaps = sapply(potential_swaps$T_potential_swaps,
as.integer), S_potential_swaps = sapply(potential_swaps$S_potential_swaps,
as.integer), weightSampleMean = as.numeric(weightSampleMean),
weightSampleVariance = as.numeric(weightSampleVariance),
weight_samples_per_move = as.integer(weight_samples_per_move),
theta_TF = as.numeric(theta_TF), theta_miRNA = as.numeric(theta_miRNA),
lambda_omega = as.numeric(lambda_omega), init_S = as.integer(init_S),
init_T = as.integer(init_T), condition_specific = as.integer(condition.specific.inference),
equal_regulator_weights = as.integer(equal.regulator.weights),
TFexpr = as.numeric(TFexpr), nTFexpr = as.integer(nTFexpr),
alpha_i0TF = as.numeric(alpha_i0TF), alpha_iTF = as.numeric(alpha_iTF),
TF_sigma = as.numeric(TF_sigma), alphaTF = as.numeric(alphaTF),
betaTF = as.numeric(betaTF), PACKAGE = "birta")
if ((!noTF) & (!condition.specific.inference)) {
names(result$TFstates1) = TF
names(result$TFstates2) = TF
result$TFActivitySwitch = result$TFstates2
result = result[-which(names(result) %in% c("TFstates1",
"TFstates2"))]
}
if (!nomiRNA) {
names(result$miRNAstates1) = miRNA
names(result$miRNAstates2) = miRNA
if (!condition.specific.inference) {
result$miRNAactivitySwitch = result$miRNAstates2
result$miRNAactivitySwitch[which(result$miRNAstates1 >
0)] = result$miRNAstates1[which(result$miRNAstates1 >
0)]
result = result[-which(names(result) %in% c("miRNAstates1",
"miRNAstates2"))]
}
}
if ((!is.null(TFexpr)) > 0 & condition.specific.inference) {
names(result$TFstates1) = TF
names(result$TFstates2) = TF
result$TFActivitySwitch = result$TFstates2[(!(names(result$TFstates2) %in%
rownames(TFexpr)))]
result$TFstates1 = result$TFstates1[rownames(TFexpr)]
result$TFstates2 = result$TFstates2[rownames(TFexpr)]
}
if ((is.null(TFexpr)) > 0 & condition.specific.inference) {
names(result$TFstates1) = TF
names(result$TFstates2) = TF
result$TFActivitySwitch = result$TFstates2
result = result[-which(names(result) %in% c("TFstates1",
"TFstates2"))]
}
result$genesetsTF = genesetsTF
result$genesetsmiRNA = genesetsmiRNA
result$mRNAexpr = mRNAexpr
result$miRNAexpr = miRNAexpr
return(result)
}
<bytecode: 0x0000000008102c38>
<environment: namespace:birta>
--- function search by body ---
Function birtaStart in namespace birta has this body.
----------- END OF FAILURE REPORT --------------
Fatal error: length > 1 in coercion to logical
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 ERRORs, 1 WARNING, 3 NOTEs
See
'C:/Users/biocbuild/bbs-3.11-bioc/meat/birta.Rcheck/00check.log'
for details.