dreamlet submission

[GabrielHoffman]

Update the following URL to point to the GitHub repository of the package you wish to submit to Bioconductor

• Repository: https://github.com/GabrielHoffman/dreamlet

Confirm the following by editing each check box to '[x]'

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• [x] I have read the Bioconductor Package Submission instructions. My package is consistent with the Bioconductor Package Guidelines.

• [x] I understand Bioconductor Package Naming Policy and acknowledge Bioconductor may retain use of package name.

• [x] I understand that a minimum requirement for package acceptance is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS. Passing these checks does not result in automatic acceptance. The package will then undergo a formal review and recommendations for acceptance regarding other Bioconductor standards will be addressed.

• [x] My package addresses statistical or bioinformatic issues related to the analysis and comprehension of high throughput genomic data.

• [x] I am committed to the long-term maintenance of my package. This includes monitoring the support site for issues that users may have, subscribing to the bioc-devel mailing list to stay aware of developments in the Bioconductor community, responding promptly to requests for updates from the Core team in response to changes in R or underlying software.

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[GabrielHoffman]

@jianhong, Can you advise about the current warnings? I cannot resolve the warnings that say: unknown macro '\insertAllCited'

Following the directions in section 1 here doesn't work. This seems like it should come up often enough in Bioconductor

Gabriel

[jianhong]

@GabrielHoffman I agree with you that this should be not shown as a warning. And the documents section 4.2 explained about that. @lshep Could you please take a look?

[lshep]

I'm not sure we have encountered this way of doing citations yet. @LiNk-NY could this be a bug in BiocCheck? I think @jianhong it okay to ignore these warnings for now.

[LiNk-NY]

Hi @GabrielHoffman @jianhong and @lshep It is safe to ignore these warnings as noted in section 4.2 of the Rdpack documentation linked above. I will take a look at including the new macro location when tools::parse_Rd is called within BiocCheck.

Update: The warnings have been addressed in BiocCheck version 1.37.9.

Best, Marcel

[jianhong]

Package 'dreamlet' Review

Thank you for submitting your package to Bioconductor. The package passed check and build. It is almost there. However there are several things need to be fixed. Please try to answer the comments line by line when you are ready for a second review. Code: Note: please consider; Important: must be addressed.

General package development

* Important: WARNING: Empty or missing \value sections found in man
  pages.

Found in files: man/cellSpecificityValues-class.Rd, man/dreamlet_mash_result-class.Rd, man/dreamletProcessedData-class.Rd, man/dreamletResult-class.Rd, man/vpDF-class.Rd Please add @return in the R code to avoid the warning.

R code

• [ ] Important: No paste in message(), message, stop. Please move it to outside.

  • In file R/dreamlet.R:
    • at line 477 found ' stop("sort.by must be in: ", paste0(opt, collapse=', '))'
    • at line 575 found ' stop("sort.by must be in: ", paste0(opt, collapse=', '))'
  • In file R/misc.R:
    • at line 43 found ' stop("Variables in formula are not found in data:\n ", paste(v[!found], collapse=", "))'

• [ ] Important: Remove unused code.

  • In file R/aggregateNonCountSignal.R:
    • at line 169 found ' # idx = which(number == 0)'
    • at line 170 found ' # if( length(idx) > 0){'
    • at line 171 found ' # signal[idx] = NA'
    • at line 172 found ' # w[idx] = 0'
    • at line 173 found ' # }'
    • at line 176 found ' # # Want E = signal, w = lowest weight observed value per row'
    • at line 177 found ' # idx = which(number[1,] == 1)'
    • at line 178 found ' # if( length(idx) > 0){'
    • at line 179 found ' # w[,idx] = apply(w, 1, function(b){'
    • at line 180 found ' # b = b[b>0 & is.finite(b)]'
    • at line 181 found ' # ifelse(length(b) > 0, min(b), 1)'
    • at line 182 found ' # })'
    • at line 183 found ' # }'
    • at line 186 found ' # if( any(!is.finite(w)) ){'
    • at line 187 found ' # for(i in seq(nrow(w))){'
    • at line 188 found ' # if( all(!is.finite(w[i,])) ){'
    • at line 189 found ' # w[i,] = 0'
    • at line 190 found ' # }else{ '
    • at line 191 found ' # idx = which(!is.finite(w[i,]))'
    • at line 192 found ' # if( length(idx) > 0){'
    • at line 193 found ' # w[i,idx] = max(w[i, -idx])'
    • at line 194 found ' # }'
    • at line 195 found ' # }'
    • at line 196 found ' # }'
    • at line 197 found ' # }'
  • In file R/aggregateToPseudoBulk.R:
    • at line 31 found ' #message(i)'
    • at line 34 found ' #foo <- matrix(0, nrow(x), length(fill))'
    • at line 266 found ' # values = as.matrix(assay(x,y)[,seq(1,10)])'
    • at line 368 found ' # countsMatrix <- colsum_fast(x, ids, BPPARAM=BPPARAM)'
  • In file R/cellTypeSpecificity.R:
    • at line 100 found ' # cell_fracs = colSums(cellCounts(pb))'
    • at line 101 found ' # cell_fracs = cell_fracs / sum(cell_fracs)'
    • at line 104 found ' # geneExpr.fract = t(apply(geneExpr, 1, function(x) x/ sum(x)))'
    • at line 105 found ' # df = DataFrame(geneExpr.fract)'
    • at line 106 found ' # colnames(df) = colnames(geneExpr.fract)'
  • In file R/colsum2.R:
    • at line 12 found ' # rblock = ceiling(nrow(x)/10)'
    • at line 13 found ' # cblock = getAutoBlockSize() / (rblock*8)'
    • at line 14 found ' # grid = RegularArrayGrid(dim(x), spacings=c(rblock, cblock))'
    • at line 21 found ' # grid <- DelayedArray:::normarg_grid(grid, x)'
    • at line 80 found ' # colsum(block, group2, reorder=FALSE, na.rm=na.rm)'
  • In file R/computeLogCPM.R:
    • at line 63 found ' # t(log2((t(counts(sce)) + pc) /(lib.size/ 1e6)))'
  • In file R/dreamlet.R:
    • at line 670 found ' # stopifnot( is(x, 'dreamletProcessedData'))'
  • In file R/dropRedundantTerms.R:
    • at line 82 found ' # fterms_new = array(sapply(excludeVar, function(x) gsub(x, '1', fterms)))'
  • In file R/fitVarPart.R:
    • at line 68 found ' # stopifnot( is(x, 'dreamletProcessedData'))'
  • In file R/mash.R:
    • at line 45 found ' # A = matrix(NA, length(rnlvl), length(cnlvl), '
    • at line 46 found ' # dimnames = list(rnlvl, cnlvl))'
    • at line 47 found ' # A[i,j] = tab[[col]]'
  • In file R/plotPercentBars.R:
    • at line 82 found ' # legend.position = "bottom",'
    • at line 83 found ' # plot.margin = unit(c(0,.3,0,.8), "cm"),'
  • In file R/processAssays.R:
    • at line 46 found ' # nCells = extract from y'
    • at line 86 found ' # design = model.matrix( subbars(formula), data)'
  • In file R/removeConstantTerms.R:
    • at line 83 found ' # fterms_new = gsub(x, '1', fterms_new)'
    • at line 92 found ' # fterms_new = array(sapply(excludeVar, function(x) gsub(x, '1', fterms)))'
  • In file R/zenith_gsa.R:

• [ ] NOTE: Avoid 'suppressWarnings'/'*Messages' if possible

  • In file R/processAssays.R:
    • at line 75 found ' y = suppressMessages(DGEList(y, remove.zeros = TRUE))'
    • at line 89 found ' keep = suppressWarnings(filterByExpr(y, min.count=min.count, min.prop=min.prop))'

• [ ] NOTE: Avoid using '=' for assignment and use '<-' instead Found in files: R/aggregateNonCountSignal.R (line 54, column 25) R/aggregateNonCountSignal.R (line 91, column 23) R/aggregateNonCountSignal.R (line 94, column 17) R/aggregateNonCountSignal.R (line 97, column 24) R/aggregateNonCountSignal.R (line 100, column 19) R/aggregateNonCountSignal.R (line 103, column 24) R/aggregateNonCountSignal.R (line 106, column 21) R/aggregateNonCountSignal.R (line 109, column 25) R/aggregateNonCountSignal.R (line 110, column 21) R/aggregateNonCountSignal.R (line 113, column 22) R/aggregateNonCountSignal.R (line 116, column 21) R/aggregateNonCountSignal.R (line 122, column 45) R/aggregateNonCountSignal.R (line 124, column 68) R/aggregateNonCountSignal.R (line 137, column 24) R/aggregateNonCountSignal.R (line 140, column 17) R/aggregateToPseudoBulk.R (line 19, column 5) R/aggregateToPseudoBulk.R (line 129, column 23) R/aggregateToPseudoBulk.R (line 135, column 16) R/aggregateToPseudoBulk.R (line 138, column 8) R/aggregateToPseudoBulk.R (line 164, column 17) R/aggregateToPseudoBulk.R (line 165, column 20) R/aggregateToPseudoBulk.R (line 216, column 10) R/aggregateToPseudoBulk.R (line 217, column 10) R/aggregateToPseudoBulk.R (line 219, column 10) R/aggregateToPseudoBulk.R (line 220, column 10) R/aggregateToPseudoBulk.R (line 224, column 29) R/aggregateToPseudoBulk.R (line 251, column 18) R/aggregateToPseudoBulk.R (line 267, column 11) R/aggregateToPseudoBulk.R (line 268, column 14) R/aggregateToPseudoBulk.R (line 269, column 14) R/aggregateToPseudoBulk.R (line 270, column 16) R/aggregateToPseudoBulk.R (line 271, column 16) R/aggregateToPseudoBulk.R (line 274, column 17) R/aggregateToPseudoBulk.R (line 279, column 21) R/aggregateToPseudoBulk.R (line 282, column 17) R/aggregateToPseudoBulk.R (line 290, column 21) R/aggregateToPseudoBulk.R (line 311, column 16) R/aggregateToPseudoBulk.R (line 359, column 22) R/aggregateToPseudoBulk.R (line 361, column 19) R/aggregateToPseudoBulk.R (line 363, column 22) R/aggregateToPseudoBulk.R (line 365, column 19) R/aggregateToPseudoBulk.R (line 369, column 17) R/aggregateToPseudoBulk.R (line 370, column 29) R/aggregateToPseudoBulk.R (line 373, column 19) R/aggregateToPseudoBulk.R (line 375, column 19) R/aggregateToPseudoBulk.R (line 387, column 13) R/aggregateToPseudoBulk.R (line 390, column 10) R/aggregateToPseudoBulk.R (line 396, column 20) R/aggregateToPseudoBulk.R (line 401, column 20) R/aggregateToPseudoBulk.R (line 405, column 34) R/aggregateToPseudoBulk.R (line 409, column 33) R/aggregateToPseudoBulk.R (line 413, column 42) R/aggregateToPseudoBulk.R (line 417, column 43) R/aggregateToPseudoBulk.R (line 421, column 36) R/aggregateToPseudoBulk.R (line 426, column 33) R/aggregateToPseudoBulk.R (line 431, column 36) R/aggregateToPseudoBulk.R (line 441, column 20) R/aggregateToPseudoBulk.R (line 443, column 20) R/aggregateToPseudoBulk.R (line 447, column 34) R/aggregateToPseudoBulk.R (line 451, column 33) R/aggregateToPseudoBulk.R (line 455, column 42) R/aggregateToPseudoBulk.R (line 459, column 43) R/aggregateToPseudoBulk.R (line 463, column 36) R/aggregateToPseudoBulk.R (line 468, column 33) R/aggregateToPseudoBulk.R (line 473, column 36) R/aggregateToPseudoBulk.R (line 480, column 20) R/aggregateToPseudoBulk.R (line 482, column 20) R/aggregateToPseudoBulk.R (line 486, column 34) R/aggregateToPseudoBulk.R (line 490, column 33) R/aggregateToPseudoBulk.R (line 494, column 42) R/aggregateToPseudoBulk.R (line 498, column 43) R/aggregateToPseudoBulk.R (line 502, column 36) R/aggregateToPseudoBulk.R (line 507, column 33) R/aggregateToPseudoBulk.R (line 512, column 36) R/aggregateToPseudoBulk.R (line 520, column 15) R/aggregateToPseudoBulk.R (line 521, column 13) R/aggregateToPseudoBulk.R (line 524, column 16) R/aggregateToPseudoBulk.R (line 525, column 26) R/aggregateToPseudoBulk.R (line 528, column 22) R/aggregateToPseudoBulk.R (line 579, column 15) R/aggregateVar.R (line 45, column 14) R/aggregateVar.R (line 70, column 19) R/aggregateVar.R (line 73, column 13) R/aggregateVar.R (line 76, column 16) R/aggregateVar.R (line 79, column 13) R/aggregateVar.R (line 82, column 11) R/aggregateVar.R (line 85, column 13) R/aggregateVar.R (line 88, column 17) R/aggregateVar.R (line 89, column 13) R/aggregateVar.R (line 92, column 14) R/aggregateVar.R (line 95, column 13) R/aggregateVar.R (line 101, column 25) R/aggregateVar.R (line 103, column 44) R/aggregateVar.R (line 116, column 20) R/aggregateVar.R (line 119, column 13) R/buildClusterTreeFromPB.R (line 37, column 24) R/buildClusterTreeFromPB.R (line 39, column 16) R/buildClusterTreeFromPB.R (line 40, column 21) R/buildClusterTreeFromPB.R (line 43, column 20) R/buildClusterTreeFromPB.R (line 46, column 27) R/buildClusterTreeFromPB.R (line 47, column 20) R/buildClusterTreeFromPB.R (line 50, column 14) R/buildClusterTreeFromPB.R (line 51, column 13) R/buildClusterTreeFromPB.R (line 52, column 13) R/buildClusterTreeFromPB.R (line 53, column 18) R/buildClusterTreeFromPB.R (line 56, column 11) R/cellCounts.R (line 30, column 12) R/cellCounts.R (line 62, column 19) R/cellCounts.R (line 65, column 12) R/cellCounts.R (line 72, column 34) R/cellCounts.R (line 75, column 33) R/cellCounts.R (line 79, column 11) R/cellCounts.R (line 83, column 21) R/cellCounts.R (line 84, column 21) R/cellTypeSpecificity.R (line 65, column 21) R/cellTypeSpecificity.R (line 72, column 18) R/cellTypeSpecificity.R (line 77, column 25) R/cellTypeSpecificity.R (line 78, column 18) R/cellTypeSpecificity.R (line 79, column 28) R/cellTypeSpecificity.R (line 82, column 13) R/cellTypeSpecificity.R (line 86, column 26) R/cellTypeSpecificity.R (line 87, column 21) R/cellTypeSpecificity.R (line 93, column 13) R/cellTypeSpecificity.R (line 94, column 13) R/cellTypeSpecificity.R (line 97, column 18) R/cellTypeSpecificity.R (line 108, column 12) R/colsum_fast.R (line 4, column 16) R/colsum_fast.R (line 6, column 13) R/colsum_fast.R (line 8, column 13) R/colsum_fast.R (line 11, column 20) R/colsum_fast.R (line 18, column 20) R/colsum2.R (line 8, column 9) R/colsum2.R (line 22, column 22) R/colsum2.R (line 87, column 17) R/colsum2.R (line 90, column 23) R/colsum2.R (line 93, column 21) R/colsum2.R (line 95, column 43) R/colsum2.R (line 97, column 23) R/colsum2.R (line 100, column 23) R/colsum2.R (line 104, column 21) R/colsum2.R (line 106, column 21) R/colsum2.R (line 109, column 23) R/colsum2.R (line 110, column 23) R/computeLogCPM.R (line 27, column 19) R/computeLogCPM.R (line 29, column 18) R/computeLogCPM.R (line 55, column 15) R/computeLogCPM.R (line 58, column 18) R/computeLogCPM.R (line 62, column 12) R/dreamlet.R (line 42, column 26) R/dreamlet.R (line 43, column 26) R/dreamlet.R (line 178, column 20) R/dreamlet.R (line 185, column 20) R/dreamlet.R (line 252, column 13) R/dreamlet.R (line 254, column 20) R/dreamlet.R (line 309, column 19) R/dreamlet.R (line 327, column 21) R/dreamlet.R (line 329, column 21) R/dreamlet.R (line 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column 16) R/dreamletCompareClusters.R (line 199, column 21) R/dreamletCompareClusters.R (line 200, column 21) R/dreamletCompareClusters.R (line 213, column 21) R/dreamletCompareClusters.R (line 215, column 36) R/dreamletCompareClusters.R (line 219, column 36) R/dreamletCompareClusters.R (line 220, column 46) R/dreamletCompareClusters.R (line 222, column 28) R/dreamletCompareClusters.R (line 223, column 40) R/dreamletCompareClusters.R (line 224, column 35) R/dreamletCompareClusters.R (line 225, column 38) R/dreamletCompareClusters.R (line 230, column 30) R/dreamletCompareClusters.R (line 231, column 22) R/dreamletCompareClusters.R (line 232, column 32) R/dreamletCompareClusters.R (line 235, column 25) R/dreamletCompareClusters.R (line 240, column 21) R/dreamletCompareClusters.R (line 241, column 36) R/dreamletCompareClusters.R (line 242, column 46) R/dreamletCompareClusters.R (line 244, column 28) R/dreamletCompareClusters.R (line 245, column 40) R/dreamletCompareClusters.R (line 246, column 35) R/dreamletCompareClusters.R (line 247, column 38) R/dreamletCompareClusters.R (line 252, column 30) R/dreamletCompareClusters.R (line 253, column 22) R/dreamletCompareClusters.R (line 254, column 32) R/dreamletCompareClusters.R (line 257, column 25) R/dreamletCompareClusters.R (line 261, column 14) R/dreamletCompareClusters.R (line 266, column 19) R/dreamletCompareClusters.R (line 267, column 23) R/dreamletCompareClusters.R (line 270, column 14) R/dreamletCompareClusters.R (line 288, column 21) R/dreamletCompareClusters.R (line 298, column 21) R/dreamletCompareClusters.R (line 309, column 13) R/dreamletCompareClusters.R (line 310, column 14) R/dreamletCompareClusters.R (line 313, column 20) R/dreamletCompareClusters.R (line 314, column 24) R/dreamletCompareClusters.R (line 335, column 23) R/dreamletCompareClusters.R (line 337, column 29) R/dreamletCompareClusters.R (line 341, column 29) R/dreamletCompareClusters.R (line 342, column 30) R/dreamletCompareClusters.R (line 343, 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column 21) R/dreamletProcessedData.R (line 29, column 28) R/dreamletProcessedData.R (line 109, column 11) R/dreamletProcessedData.R (line 121, column 12) R/dreamletProcessedData.R (line 174, column 18) R/dreamletProcessedData.R (line 175, column 18) R/dreamletProcessedData.R (line 277, column 12) R/dreamletProcessedData.R (line 279, column 21) R/dreamletProcessedData.R (line 282, column 29) R/dreamletProcessedData.R (line 284, column 29) R/dreamletProcessedData.R (line 288, column 12) R/dropRedundantTerms.R (line 21, column 20) R/dropRedundantTerms.R (line 28, column 14) R/dropRedundantTerms.R (line 29, column 14) R/dropRedundantTerms.R (line 35, column 14) R/dropRedundantTerms.R (line 36, column 19) R/dropRedundantTerms.R (line 37, column 16) R/dropRedundantTerms.R (line 40, column 15) R/dropRedundantTerms.R (line 43, column 14) R/dropRedundantTerms.R (line 46, column 13) R/dropRedundantTerms.R (line 47, column 31) R/dropRedundantTerms.R (line 50, column 11) R/dropRedundantTerms.R (line 51, column 21) R/dropRedundantTerms.R (line 52, column 35) R/dropRedundantTerms.R (line 53, column 12) R/dropRedundantTerms.R (line 54, column 12) R/dropRedundantTerms.R (line 60, column 20) R/dropRedundantTerms.R (line 63, column 11) R/dropRedundantTerms.R (line 64, column 19) R/dropRedundantTerms.R (line 70, column 28) R/dropRedundantTerms.R (line 76, column 36) R/dropRedundantTerms.R (line 77, column 36) R/dropRedundantTerms.R (line 80, column 28) R/dropRedundantTerms.R (line 83, column 28) R/dropRedundantTerms.R (line 84, column 28) R/dropRedundantTerms.R (line 86, column 28) R/dropRedundantTerms.R (line 90, column 17) R/dropRedundantTerms.R (line 93, column 28) R/dropRedundantTerms.R (line 97, column 18) R/dropRedundantTerms.R (line 98, column 51) R/dropRedundantTerms.R (line 102, column 26) R/dropRedundantTerms.R (line 104, column 26) R/dropRedundantTerms.R (line 109, column 26) R/fitVarPart.R (line 73, column 21) R/fitVarPart.R (line 74, column 21) R/fitVarPart.R (line 79, column 23) R/fitVarPart.R (line 82, column 13) R/fitVarPart.R (line 85, column 9) R/fitVarPart.R (line 88, column 27) R/fitVarPart.R (line 92, column 17) R/fitVarPart.R (line 95, column 27) R/fitVarPart.R (line 97, column 26) R/fitVarPart.R (line 101, column 21) R/fitVarPart.R (line 102, column 26) R/fitVarPart.R (line 106, column 23) R/fitVarPart.R (line 109, column 26) R/fitVarPart.R (line 112, column 26) R/fitVarPart.R (line 118, column 29) R/fitVarPart.R (line 120, column 29) R/fitVarPart.R (line 128, column 24) R/fitVarPart.R (line 133, column 15) R/fitVarPart.R (line 136, column 20) R/fitVarPart.R (line 139, column 29) R/fitVarPart.R (line 143, column 29) R/fitVarPart.R (line 147, column 22) R/fitVarPart.R (line 150, column 12) R/fitVarPart.R (line 152, column 26) R/fitVarPart.R (line 156, column 20) R/fitVarPart.R (line 163, column 20) R/fitVarPart.R (line 165, column 15) R/mash.R (line 27, column 13) R/mash.R (line 34, column 48) R/mash.R (line 35, column 48) R/mash.R (line 38, column 15) R/mash.R (line 39, column 15) R/mash.R (line 42, column 11) R/mash.R (line 43, column 11) R/mash.R (line 51, column 11) R/mash.R (line 56, column 14) R/mash.R (line 60, column 25) R/mash.R (line 162, column 10) R/mash.R (line 172, column 13) R/mash.R (line 175, column 21) R/mash.R (line 176, column 26) R/mash.R (line 179, column 13) R/mash.R (line 182, column 27) R/mash.R (line 183, column 22) R/mash.R (line 185, column 22) R/mash.R (line 189, column 16) R/mash.R (line 192, column 19) R/mash.R (line 195, column 11) R/mash.R (line 196, column 11) R/mash.R (line 203, column 14) R/mash.R (line 206, column 13) R/mash.R (line 209, column 14) R/mash.R (line 212, column 15) R/mash.R (line 216, column 13) R/mash.R (line 217, column 41) R/mash.R (line 218, column 39) R/mash.R (line 219, column 40) R/mash.R (line 220, column 32) R/misc.R (line 34, column 14) R/misc.R (line 39, column 11) R/misc.R (line 40, column 15) R/misc.R (line 70, column 15) R/misc.R (line 73, column 11) R/misc.R (line 74, column 16) R/misc.R (line 75, column 13) R/misc.R (line 78, column 11) R/misc.R (line 79, column 16) R/misc.R (line 80, column 13) R/misc.R (line 127, column 9) R/misc.R (line 130, column 16) R/outlier.R (line 23, column 9) R/outlier.R (line 26, column 9) R/outlier.R (line 28, column 8) R/outlier.R (line 29, column 7) R/outlier.R (line 32, column 8) R/outlier.R (line 34, column 7) R/plotCellComposition.R (line 60, column 16) R/plotCellComposition.R (line 87, column 22) R/plotCellComposition.R (line 90, column 11) R/plotCellComposition.R (line 92, column 6) R/plotCellComposition.R (line 95, column 13) R/plotCellComposition.R (line 97, column 13) R/plotForest.R (line 59, column 15) R/plotForest.R (line 59, column 23) R/plotForest.R (line 59, column 28) R/plotForest.R (line 59, column 40) R/plotForest.R (line 61, column 12) R/plotForest.R (line 62, column 15) R/plotForest.R (line 63, column 12) R/plotForest.R (line 64, column 12) R/plotForest.R (line 65, column 18) R/plotForest.R (line 91, column 12) R/plotForest.R (line 91, column 20) R/plotForest.R (line 91, column 27) R/plotForest.R (line 91, column 32) R/plotForest.R (line 94, column 18) R/plotForest.R (line 95, column 28) R/plotForest.R (line 96, column 22) R/plotForest.R (line 98, column 17) R/plotForest.R (line 99, column 27) R/plotForest.R (line 100, column 21) R/plotForest.R (line 102, column 15) R/plotForest.R (line 103, column 25) R/plotForest.R (line 104, column 19) R/plotForest.R (line 107, column 12) R/plotForest.R (line 108, column 12) R/plotForest.R (line 110, column 12) R/plotForest.R (line 111, column 15) R/plotForest.R (line 114, column 12) R/plotGeneHeatmap.R (line 76, column 13) R/plotGeneHeatmap.R (line 77, column 13) R/plotGeneHeatmap.R (line 82, column 13) R/plotGeneHeatmap.R (line 83, column 13) R/plotGeneHeatmap.R (line 84, column 19) R/plotGeneHeatmap.R (line 89, column 15) R/plotGeneHeatmap.R (line 89, column 20) R/plotGeneHeatmap.R (line 89, column 28) R/plotGeneHeatmap.R (line 89, column 40) R/plotGeneHeatmap.R (line 92, column 13) R/plotGeneHeatmap.R (line 95, column 22) R/plotGeneHeatmap.R (line 102, column 14) R/plotGeneHeatmap.R (line 103, column 14) R/plotGeneHeatmap.R (line 104, column 22) R/plotGeneHeatmap.R (line 106, column 13) R/plotGeneHeatmap.R (line 115, column 15) R/plotGeneHeatmap.R (line 118, column 15) R/plotHeatmap.R (line 54, column 16) R/plotHeatmap.R (line 57, column 11) R/plotHeatmap.R (line 60, column 11) R/plotHeatmap.R (line 63, column 15) R/plotHeatmap.R (line 63, column 26) R/plotHeatmap.R (line 63, column 33) R/plotHeatmap.R (line 66, column 11) R/plotHeatmap.R (line 67, column 30) R/plotHeatmap.R (line 69, column 6) R/plotHeatmap.R (line 71, column 17) R/plotHeatmap.R (line 73, column 22) R/plotHeatmap.R (line 74, column 26) R/plotHeatmap.R (line 76, column 15) R/plotHeatmap.R (line 100, column 16) R/plotHeatmap.R (line 103, column 11) R/plotHeatmap.R (line 106, column 11) R/plotHeatmap.R (line 109, column 15) R/plotHeatmap.R (line 109, column 26) R/plotHeatmap.R (line 109, column 33) R/plotHeatmap.R (line 111, column 6) R/plotHeatmap.R (line 113, column 17) R/plotHeatmap.R (line 115, column 22) R/plotHeatmap.R (line 116, column 26) R/plotHeatmap.R (line 118, column 15) R/plotPercentBars.R (line 49, column 24) R/plotPercentBars.R (line 51, column 24) R/plotPercentBars.R (line 55, column 11) R/plotPercentBars.R (line 58, column 11) R/plotPercentBars.R (line 61, column 12) R/plotPercentBars.R (line 63, column 17) R/plotPercentBars.R (line 64, column 18) R/plotPercentBars.R (line 67, column 14) R/plotPercentBars.R (line 67, column 22) R/plotPercentBars.R (line 67, column 33) R/plotPercentBars.R (line 69, column 13) R/plotPercentBars.R (line 75, column 13) R/plotPercentBars.R (line 91, column 13) R/plotPercentBars.R (line 112, column 24) R/plotPercentBars.R (line 114, column 24) R/plotPercentBars.R (line 115, column 24) R/plotPercentBars.R (line 118, column 14) R/plotPercentBars.R (line 119, column 13) R/plotPercentBars.R (line 120, column 13) R/plotPercentBars.R (line 127, column 21) R/plotPercentBars.R (line 132, column 11) R/plotPercentBars.R (line 133, column 29) R/plotPercentBars.R (line 135, column 12) R/plotPercentBars.R (line 136, column 17) R/plotPercentBars.R (line 137, column 17) R/plotPercentBars.R (line 139, column 22) R/plotPercentBars.R (line 140, column 26) R/plotPercentBars.R (line 143, column 14) R/plotPercentBars.R (line 143, column 22) R/plotPercentBars.R (line 143, column 33) R/plotProjection.R (line 33, column 16) R/plotProjection.R (line 40, column 18) R/plotProjection.R (line 40, column 23) R/plotProjection.R (line 40, column 28) R/plotProjection.R (line 40, column 32) R/plotProjection.R (line 40, column 36) R/plotProjection.R (line 43, column 12) R/plotProjection.R (line 44, column 27) R/plotProjection.R (line 47, column 29) R/plotProjection.R (line 51, column 16) R/plotProjection.R (line 55, column 13) R/plotProjection.R (line 63, column 21) R/plotVarPart.R (line 53, column 24) R/plotVarPart.R (line 55, column 24) R/plotVarPart.R (line 58, column 12) R/plotVarPart.R (line 60, column 18) R/plotVarPart.R (line 63, column 18) R/plotVarPart.R (line 63, column 26) R/plotVarPart.R (line 65, column 13) R/plotVarPart.R (line 85, column 21) R/plotViolin.R (line 50, column 14) R/plotViolin.R (line 50, column 22) R/plotViolin.R (line 50, column 33) R/plotViolin.R (line 53, column 11) R/plotViolin.R (line 54, column 30) R/plotViolin.R (line 57, column 16) R/plotViolin.R (line 59, column 11) R/plotViolin.R (line 61, column 12) R/plotViolin.R (line 63, column 17) R/plotVolcano.R (line 68, column 10) R/plotVolcano.R (line 71, column 14) R/plotVolcano.R (line 72, column 14) R/plotVolcano.R (line 73, column 7) R/plotVolcano.R (line 74, column 14) R/plotVolcano.R (line 77, column 7) R/plotVolcano.R (line 77, column 15) R/plotVolcano.R (line 77, column 25) R/plotVolcano.R (line 77, column 36) R/plotVolcano.R (line 77, column 43) R/plotVolcano.R (line 77, column 48) R/plotVolcano.R (line 79, column 8) R/plotVolcano.R (line 80, column 8) R/plotVolcano.R (line 84, column 11) R/plotVolcano.R (line 85, column 9) R/plotVolcano.R (line 89, column 23) R/plotVolcano.R (line 90, column 22) R/plotVolcano.R (line 93, column 20) R/plotVolcano.R (line 95, column 7) R/plotVolcano.R (line 96, column 14) R/plotVolcano.R (line 99, column 7) R/plotVolcano.R (line 102, column 7) R/plotVolcano.R (line 114, column 12) R/plotVolcano.R (line 115, column 7) R/plotVolcano.R (line 117, column 9) R/plotVolcano.R (line 133, column 7) R/plotVolcano.R (line 135, column 16) R/plotVolcano.R (line 137, column 16) R/plotVolcano.R (line 140, column 8) R/plotVolcano.R (line 141, column 8) R/plotVolcano.R (line 144, column 7) R/plotVolcano.R (line 144, column 15) R/plotVolcano.R (line 144, column 25) R/plotVolcano.R (line 144, column 36) R/plotVolcano.R (line 144, column 41) R/plotVolcano.R (line 148, column 11) R/plotVolcano.R (line 149, column 9) R/plotVolcano.R (line 153, column 23) R/plotVolcano.R (line 154, column 22) R/plotVolcano.R (line 157, column 7) R/plotVolcano.R (line 184, column 10) R/plotVolcano.R (line 187, column 12) R/plotVolcano.R (line 188, column 22) R/plotVolcano.R (line 189, column 16) R/plotVolcano.R (line 191, column 11) R/plotVolcano.R (line 192, column 21) R/plotVolcano.R (line 193, column 15) R/plotVolcano.R (line 195, column 6) R/plotVolcano.R (line 196, column 6) R/plotVolcano.R (line 199, column 6) R/plotVolcano.R (line 202, column 11) R/plotVolcano.R (line 205, column 7) R/plotVolcano.R (line 205, column 15) R/plotVolcano.R (line 205, column 25) R/plotVolcano.R (line 205, column 36) R/plotVolcano.R (line 205, column 45) R/plotVolcano.R (line 205, column 52) R/plotVolcano.R (line 207, column 15) R/plotVolcano.R (line 208, column 11) R/plotVolcano.R (line 211, column 6) R/plotVolcano.R (line 212, column 11) R/plotVolcano.R (line 214, column 8) R/plotVolcano.R (line 215, column 8) R/plotVolcano.R (line 218, column 7) R/plotVolcano.R (line 221, column 7) R/plotVolcano.R (line 222, column 6) R/plotVolcano.R (line 224, column 7) R/plotVolcano.R (line 236, column 12) R/plotVolcano.R (line 237, column 7) R/plotVolcano.R (line 239, column 9) R/plotVoom.R (line 57, column 11) R/plotVoom.R (line 60, column 16) R/plotVoom.R (line 65, column 18) R/plotVoom.R (line 68, column 29) R/plotVoom.R (line 70, column 29) R/plotVoom.R (line 74, column 18) R/plotVoom.R (line 80, column 14) R/plotVoom.R (line 81, column 14) R/plotVoom.R (line 83, column 14) R/plotVoom.R (line 84, column 14) R/plotVoom.R (line 87, column 21) R/plotVoom.R (line 90, column 17) R/plotVoom.R (line 93, column 19) R/plotVoom.R (line 94, column 22) R/plotVoom.R (line 95, column 19) R/plotVoom.R (line 98, column 17) R/plotVoom.R (line 101, column 18) R/plotVoom.R (line 123, column 11) R/plotVoom.R (line 128, column 18) R/plotVoom.R (line 130, column 14) R/plotVoom.R (line 131, column 14) R/plotVoom.R (line 133, column 14) R/plotVoom.R (line 134, column 14) R/plotVoom.R (line 137, column 19) R/plotVoom.R (line 140, column 18) R/processAssays.R (line 36, column 17) R/processAssays.R (line 43, column 19) R/processAssays.R (line 49, column 17) R/processAssays.R (line 57, column 11) R/processAssays.R (line 58, column 14) R/processAssays.R (line 66, column 17) R/processAssays.R (line 69, column 27) R/processAssays.R (line 75, column 19) R/processAssays.R (line 76, column 19) R/processAssays.R (line 79, column 25) R/processAssays.R (line 82, column 25) R/processAssays.R (line 89, column 22) R/processAssays.R (line 94, column 26) R/processAssays.R (line 99, column 59) R/processAssays.R (line 107, column 25) R/processAssays.R (line 113, column 26) R/processAssays.R (line 115, column 34) R/processAssays.R (line 118, column 52) R/processAssays.R (line 179, column 15) R/processAssays.R (line 191, column 23) R/processAssays.R (line 195, column 21) R/processAssays.R (line 196, column 21) R/processAssays.R (line 201, column 22) R/processAssays.R (line 204, column 21) R/processAssays.R (line 212, column 17) R/processAssays.R (line 215, column 27) R/processAssays.R (line 217, column 19) R/processAssays.R (line 220, column 25) R/processAssays.R (line 224, column 22) R/processAssays.R (line 230, column 21) R/processAssays.R (line 248, column 24) R/processAssays.R (line 250, column 17) R/processAssays.R (line 257, column 17) R/processAssays.R (line 269, column 16) R/processAssays.R (line 272, column 14) R/processAssays.R (line 274, column 14) R/processAssays.R (line 278, column 24) R/processAssays.R (line 279, column 12) R/processAssays.R (line 280, column 14) R/removeConstantTerms.R (line 27, column 21) R/removeConstantTerms.R (line 34, column 14) R/removeConstantTerms.R (line 35, column 14) R/removeConstantTerms.R (line 40, column 14) R/removeConstantTerms.R (line 42, column 19) R/removeConstantTerms.R (line 43, column 16) R/removeConstantTerms.R (line 51, column 11) R/removeConstantTerms.R (line 52, column 19) R/removeConstantTerms.R (line 55, column 13) R/removeConstantTerms.R (line 56, column 14) R/removeConstantTerms.R (line 59, column 28) R/removeConstantTerms.R (line 60, column 19) R/removeConstantTerms.R (line 65, column 23) R/removeConstantTerms.R (line 72, column 20) R/removeConstantTerms.R (line 73, column 20) R/removeConstantTerms.R (line 80, column 28) R/removeConstantTerms.R (line 86, column 36) R/removeConstantTerms.R (line 87, column 36) R/removeConstantTerms.R (line 90, column 28) R/removeConstantTerms.R (line 93, column 28) R/removeConstantTerms.R (line 95, column 28) R/removeConstantTerms.R (line 99, column 17) R/removeConstantTerms.R (line 102, column 28) R/removeConstantTerms.R (line 106, column 18) R/removeConstantTerms.R (line 107, column 51) R/removeConstantTerms.R (line 111, column 26) R/removeConstantTerms.R (line 113, column 26) R/removeConstantTerms.R (line 118, column 26) R/vpDF.R (line 47, column 27) R/vpDF.R (line 99, column 21) R/zenith_gsa.R (line 77, column 19) R/zenith_gsa.R (line 78, column 21) R/zenith_gsa.R (line 83, column 22) R/zenith_gsa.R (line 87, column 19) R/zenith_gsa.R (line 90, column 27) R/zenith_gsa.R (line 93, column 23) R/zenith_gsa.R (line 96, column 23) R/zenith_gsa.R (line 99, column 24) R/zenith_gsa.R (line 103, column 40) R/zenith_gsa.R (line 105, column 36) R/zenith_gsa.R (line 107, column 36) R/zenith_gsa.R (line 113, column 26) R/zenith_gsa.R (line 114, column 19) R/zenith_gsa.R (line 115, column 29) R/zenith_gsa.R (line 118, column 23) R/zenith_gsa.R (line 144, column 27) R/zenith_gsa.R (line 146, column 27) R/zenith_gsa.R (line 149, column 17) R/zenith_gsa.R (line 160, column 22) R/zenith_gsa.R (line 163, column 19) R/zenith_gsa.R (line 165, column 23) R/zenith_gsa.R (line 168, column 23) R/zenith_gsa.R (line 171, column 23) R/zenith_gsa.R (line 174, column 23) R/zenith_gsa.R (line 177, column 24) R/zenith_gsa.R (line 181, column 19) R/zenith_gsa.R (line 182, column 29) R/zenith_gsa.R (line 185, column 23)

• [ ] NOTE: Functional programming: code repetition.

  • repetition in aggregateNonCountSignal and aggregateVar
    • in aggregateNonCountSignal
      • line 2: min.cells = 10, min.signal = 0.01, min.samples = 4, min.prop = 0.4,
      • line 3: verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))
      • line 4: {
      • line 5: pb <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 6: sample_id = sample_id, verbose = FALSE, fun = "mean",
      • line 7: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 8: pb.sem <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 9: sample_id = sample_id, verbose = FALSE, fun = "sem",
      • line 10: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 11: pb.number <- aggregateToPseudoBulk(sce, assay = assay,
      • line 12: cluster_id = cluster_id, sample_id = sample_id, verbose = FALSE,
      • line 13: fun = "number", BPPARAM = BPPARAM, checkValues = FALSE)
      • line 14: data_constant = droplevels(as.data.frame(colData(pb)))
      • line 15: resList = lapply(assayNames(pb), function(CT) {
      • line 18: number = assay(pb.number, CT)
      • line 19: obj = new("EList", list(E = as.matrix(signal), weights = as.matrix(w)))
      • line 20: include = (number[1, ] >= min.cells)
      • line 21: obj = obj[, include, drop = FALSE]
      • line 22: keep = apply(obj$E, 1, function(x) {
      • line 23: sum(x >= min.signal) >= min.prop * length(x)
      • line 24: })
      • line 25: obj = obj[keep, , drop = FALSE]
      • line 26: if (any(!is.finite(obj$weights))) {
      • line 27: for (i in seq(nrow(obj$weights))) {
      • line 28: if (any(!is.finite(obj$weights[i, ]))) {
      • line 29: idx = which(!is.finite(obj$weights[i, ]))
      • line 30: if (length(idx) > 0) {
      • line 31: obj$weights[i, idx] = max(obj$weights[i,
      • line 32: -idx])
      • line 33: }
      • line 34: }
      • line 35: }
      • line 36: }
      • line 37: if (ncol(obj) < min.samples) {
      • line 38: return(NULL)
      • line 39: }
      • line 40: obj
      • line 41: })
      • line 42: names(resList) = assayNames(pb)
      • line 43: resList = resList[!vapply(resList, is.null, FUN.VALUE = logical(1))]
      • line 44: new("dreamletProcessedData", resList, data = data_constant,
      • line 45: metadata = metadata(pb)$aggr_means, by = metadata(pb)$agg_pars$by)
    • in aggregateVar
      • line 2: min.var = 0.01, min.samples = 4, min.prop = 0.4, verbose = TRUE,
      • line 3: BPPARAM = SerialParam(progressbar = verbose))
      • line 4:{
      • line 5: pb.sem <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 6: sample_id = sample_id, verbose = FALSE, fun = "sem",
      • line 7: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 8: pb.number <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 9: sample_id = sample_id, verbose = FALSE, fun = "number",
      • line 10: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 11: data_constant = droplevels(as.data.frame(colData(pb.sem)))
      • line 12: resList = lapply(assayNames(pb.sem), function(CT) {
      • line 15: w = 1/assay(pb.sem, CT)^2
      • line 16: obj = new("EList", list(E = as.matrix(var), weights = as.matrix(w)))
      • line 17: include = (number[1, ] >= min.cells)
      • line 18: obj = obj[, include, drop = FALSE]
      • line 19: keep = apply(obj$E, 1, function(x) {
      • line 20: sum(x >= min.var) >= min.prop * length(x)
      • line 21: })
      • line 22: obj = obj[keep, , drop = FALSE]
      • line 23: if (any(!is.finite(obj$weights))) {
      • line 24: for (i in seq(nrow(obj$weights))) {
      • line 25: if (any(!is.finite(obj$weights[i, ]))) {
      • line 26: idx = which(!is.finite(obj$weights[i, ]))
      • line 27: if (length(idx) > 0) {
      • line 28: obj$weights[i, idx] = max(obj$weights[i,
      • line 29: -idx])
      • line 30: }
      • line 31: }
      • line 32: }
      • line 33: }
      • line 34: if (ncol(obj) < min.samples) {
      • line 35: return(NULL)
      • line 36: }
      • line 37: obj
      • line 38: })
      • line 39: names(resList) = assayNames(pb.sem)
      • line 40: resList = resList[!vapply(resList, is.null, FUN.VALUE = logical(1))]
      • line 41: new("dreamletProcessedData", resList, data = data_constant,
      • line 42: metadata = metadata(pb.sem)$aggr_means, by = c(cluster_id,
  • repetition in checkFormula and dropRedundantTerms and removeConstantTerms
    • in checkFormula
      • line 1: data)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
    • in dropRedundantTerms
      • line 1: data, tol = 0.001)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
    • in removeConstantTerms
      • line 1: data)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
  • repetition in dropRedundantTerms and removeConstantTerms
    • in dropRedundantTerms
      • line 1: data, tol = 0.001)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
      • line 5: data = data[, colnames(data) %in% unique(all.vars(formula)),
      • line 6: drop = FALSE]
      • line 7: data = droplevels(data)
      • line 8: checkFormula(formula, data)
      • line 9: trmf = terms(formula)
      • line 10: intercept = attr(trmf, "intercept")
      • line 11: fterms = attr(trmf, "term.labels")
      • line 28: if (length(excludeVar) > 0) {
      • line 29: fterms_new = fterms
      • line 30: for (x in excludeVar) {
      • line 31: fterms_new = gsub(paste0(x, "$"), "1", fterms_new)
      • line 32: fterms_new = gsub(paste0(x, "\)"), "1)", fterms_new)
      • line 33: }
      • line 34: fterms_new = unique(fterms_new)
      • line 35: fterms_new = fterms_new[fterms_new != ""]
      • line 36: fterms_new = fterms_new[fterms_new != "1"]
      • line 37: }
      • line 38: else {
      • line 39: fterms_new = fterms
      • line 40: }
      • line 41: exclude = grep(":1|1:|\| 1", fterms_new)
      • line 42: if (length(exclude) > 0) {
      • line 43: fterms_new = fterms_new[-exclude]
      • line 44: }
      • line 45: response = all.vars(update.formula(formula, . ~ NULL))
      • line 46: if (attr(trmf, "response") == 0)
      • line 47: response = NULL
      • line 48: if (length(fterms_new) > 0) {
      • line 49: form_new = reformulate(fterms_new, response = response)
      • line 50: }
      • line 51: else {
      • line 52: form_new = ~1
      • line 53: }
      • line 54: if (intercept == 0) {
      • line 55: form_new = update.formula(form_new, ~. - 1)
      • line 56: }
      • line 57: environment(form_new) <- environment(formula)
      • line 58: form_new
    • in removeConstantTerms
      • line 1: data)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
      • line 5: data = data[, colnames(data) %in% unique(all.vars(formula)),
      • line 6: drop = FALSE]
      • line 7: data = droplevels(data)
      • line 8: checkFormula(formula, data)
      • line 9: trmf = terms(formula)
      • line 10: intercept = attr(trmf, "intercept")
      • line 11: fterms = attr(trmf, "term.labels")
      • line 30: if (length(excludeVar) > 0) {
      • line 31: fterms_new = fterms
      • line 32: for (x in excludeVar) {
      • line 33: fterms_new = gsub(paste0(x, "$"), "1", fterms_new)
      • line 34: fterms_new = gsub(paste0(x, "\)"), "1)", fterms_new)
      • line 35: }
      • line 36: fterms_new = unique(fterms_new)
      • line 37: fterms_new = fterms_new[fterms_new != ""]
      • line 38: }
      • line 39: else {
      • line 40: fterms_new = fterms
      • line 41: }
      • line 42: exclude = grep(":1|1:|\| 1", fterms_new)
      • line 43: if (length(exclude) > 0) {
      • line 44: fterms_new = fterms_new[-exclude]
      • line 45: }
      • line 46: response = all.vars(update.formula(formula, . ~ NULL))
      • line 47: if (attr(trmf, "response") == 0)
      • line 48: response = NULL
      • line 49: if (length(fterms_new) > 0) {
      • line 50: form_new = reformulate(fterms_new, response = response)
      • line 51: }
      • line 52: else {
      • line 53: form_new = ~1
      • line 54: }
      • line 55: if (intercept == 0) {
      • line 56: form_new = update.formula(form_new, ~. - 1)
      • line 57: }
      • line 58: environment(form_new) <- environment(formula)
      • line 59: form_new

        Documentation

• [ ] Important: Typo in section title of Introduction in rmd file vignettes/mashr.Rmd

• [ ] Important: Vignette includes sessionInfo().

  • rmd file vignettes/mashr.Rmd

[jianhong]

Could I expect the error fix soon? If not, I will close this issues.

[jianhong]

Hi @GabrielHoffman,

Because I did not see any progress here. I closed this issue now. You are welcome to reopen the issue once you are ready to resubmit.

Jianhong.

[GabrielHoffman]

Can you reopen this issue, I just submitted an update

[bioc-issue-bot]

Dear @GabrielHoffman ,

We have reopened the issue to continue the review process. Please remember to push a version bump to git.bioconductor.org to trigger a new build.

[vjcitn]

BTW I just noticed all the "=" to "<-" comments above. I was pleased to learn that the styler package can perform the replacements.

[jianhong]

@vjcitn Thanks for sharing. Next time I will just mention the solution instead of list all the positions.

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: 8c34eee86d8b512ecfd7ccbeb5374bff61bb3393

[bioc-issue-bot]

Dear Package contributor,

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Your package has been built on the Bioconductor Build System.

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The following are build products from R CMD build on the Bioconductor Build System: ERROR before build products produced.

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Received a valid push on git.bioconductor.org; starting a build for commit id: 9571bf5e08048a93dccecc24ee085410abf5ac4c

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

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Received a valid push on git.bioconductor.org; starting a build for commit id: e7bf5d9c7880c12ab8ed3f4c0c6613c48dd52536

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

On one or more platforms, the build results were: "WARNINGS". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: macOS 12.6.5 Monterey: dreamlet_0.99.25.tar.gz Linux (Ubuntu 22.04.2 LTS): dreamlet_0.99.25.tar.gz

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[GabrielHoffman]

I fixed all the issues I could. Can you check?

Note, I could not add sessionInfo() to vignettes/mashr.Rmd since this vignette is pre-processed in my machine since it takes too link to run on the BioC machines

Gabriel

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: 71c3f7fec32e926270ada9aea40013cfe3c9a910

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

On one or more platforms, the build results were: "WARNINGS". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): dreamlet_0.99.26.tar.gz macOS 12.6.5 Monterey: dreamlet_0.99.26.tar.gz

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[GabrielHoffman]

My package passes most checks except for:

* Checking man page documentation...
    * WARNING: Empty or missing \value sections found in man pages.

but it doesn't tell me which function this is in. And I don't get this warning locally.

How can I resolve this?

[lshep]

If you run BiocCheck locally there will be a directory dreamlet.BiocCheck that will have a file that has a more detailed 00BiocCheck.log that give the exact file name/line numbers

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: 9c0572b57a124faf3afb17f858a4e4bf4b8a8fa6

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Dear Package contributor,

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Your package has been built on the Bioconductor Build System.

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[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: 8acb0eb005aa6df971cbfd5320c766938336df59

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: macOS 12.6.5 Monterey: dreamlet_0.99.28.tar.gz Linux (Ubuntu 22.04.2 LTS): dreamlet_0.99.28.tar.gz

Links above active for 21 days.

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[GabrielHoffman]

Can you push this package to the next stage of Bioconductor approval?

Best, Gabriel

[jianhong]

Package 'dreamlet' Review

Thank you for your patience during the review process. The package is intricate, and I truly appreciate the substantial effort you've invested in it. Please try to answer the comments line by line when you are ready for a second review. Code: Note: please consider; Important: must be addressed.

General package development

• [ ] Important: Package files exceed the 5MB size limit. The docs fold need to be removed, otherwise the package size fill exceed the 5MB size limit.

R code

• [ ] Important: No paste in message(), message, stop
  • In file R/misc.R:
    • at line 43 found ' stop("Variables in formula are not found in data:\n ", paste(v[!found], collapse = ", "))'
• [ ] NOTE: Vectorize: for loops present, try to replace them by *apply funcitons.
  • In file R/aggregateNonCountSignal.R:
    • at line 122 found ' for (i in seq(nrow(obj$weights))) {'
  • In file R/aggregateToPseudoBulk.R:
    • at line 34 found ' for (i in seq_along(ys)) {'
    • at line 163 found ' for (i in by) {'
    • at line 197 found ' for (i in names(cd)) {'
    • at line 349 found ' for (i in seq_along(mat.out)) {'
  • In file R/aggregateVar.R:
    • at line 100 found ' for (i in seq(nrow(obj$weights))) {'
  • In file R/colsum2.R:
    • at line 60 found ' for (j in seq_len(grid_ncol)) {'
    • at line 98 found ' for (key in names(group)) {'
  • In file R/dreamletCompareClusters.R:
    • at line 394 found ' for (i in seq(1, 100)) {'
  • In file R/dropRedundantTerms.R:
    • at line 68 found ' for (x in excludeVar) {'
  • In file R/removeConstantTerms.R:
    • at line 79 found ' for (x in excludeVar) {'
• [ ] Important: Remove unused code. If you are not confident with the change, please try to add unit test for the changes.
  • In file R/aggregateToPseudoBulk.R:
    • at line 35 found ' # message(i)'
  • In file R/colsum2.R:
    • at line 16 found ' # grid <- DelayedArray:::normarg_grid(grid, x)'
    • at line 86 found ' # colsum(block, group2, reorder=FALSE, na.rm=na.rm)'
  • In file R/dreamlet.R:
    • at line 829 found ' # if (!quiet) message("\n")'
  • In file R/dropRedundantTerms.R:
    • at line 69 found ' # fterms_new = gsub(x, '1', fterms_new)'
    • at line 78 found ' # old: fterms_new = array(sapply(excludeVar, function(x) gsub(x, '1', fterms)))'
  • In file R/plotHeatmap.R:
    • at line 117 found ' # bquote(log[2]~CPM)'
  • In file R/processAssays.R:
    • at line 90 found ' # geneExpr <- tryCatch('
    • at line 91 found ' # voomWithDreamWeights(y[keep, ], formula, data, weights = w_cells, BPPARAM = BPPARAM, ..., save.plot = TRUE, quiet = quiet, span = span),'
    • at line 92 found ' # error = function(e) NULL'
    • at line 93 found ' # )'
  • In file R/removeConstantTerms.R:
    • at line 80 found ' # old: fterms_new = gsub(x, '1', fterms_new)'
    • at line 89 found ' # old: fterms_new = array(sapply(excludeVar, function(x) gsub(x, '1', fterms)))'
  • In file R/zenith_gsa.R:
• [ ] NOTE: Avoid 'suppressWarnings'/'*Messages' if possible
  • In file R/processAssays.R:
    • at line 71 found ' y <- suppressMessages(DGEList(y, remove.zeros = TRUE))'
    • at line 85 found ' keep <- suppressWarnings(filterByExpr(y, min.count = min.count, min.prop = min.prop))'
• [ ] NOTE: Functional programming: code repetition.
  • repetition in aggregateNonCountSignal and aggregateVar
    • in aggregateNonCountSignal
      • line 2: min.cells = 10, min.signal = 0.01, min.samples = 4, min.prop = 0.4,
      • line 3: verbose = TRUE, BPPARAM = SerialParam(progressbar = verbose))
      • line 4: {
      • line 5: pb <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 6: sample_id = sample_id, verbose = FALSE, fun = "mean",
      • line 7: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 8: pb.sem <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 9: sample_id = sample_id, verbose = FALSE, fun = "sem",
      • line 10: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 11: pb.number <- aggregateToPseudoBulk(sce, assay = assay,
      • line 12: cluster_id = cluster_id, sample_id = sample_id, verbose = FALSE,
      • line 13: fun = "number", BPPARAM = BPPARAM, checkValues = FALSE)
      • line 14: data_constant <- droplevels(as.data.frame(colData(pb)))
      • line 15: resList <- lapply(assayNames(pb), function(CT) {
      • line 18: number <- assay(pb.number, CT)
      • line 19: obj <- new("EList", list(E = as.matrix(signal), weights = as.matrix(w)))
      • line 20: include <- (number[1, ] >= min.cells)
      • line 21: obj <- obj[, include, drop = FALSE]
      • line 22: keep <- apply(obj$E, 1, function(x) {
      • line 23: sum(x >= min.signal) >= min.prop * length(x)
      • line 24: })
      • line 25: obj <- obj[keep, , drop = FALSE]
      • line 26: if (any(!is.finite(obj$weights))) {
      • line 27: for (i in seq(nrow(obj$weights))) {
      • line 28: if (any(!is.finite(obj$weights[i, ]))) {
      • line 29: idx <- which(!is.finite(obj$weights[i, ]))
      • line 30: if (length(idx) > 0) {
      • line 31: obj$weights[i, idx] <- max(obj$weights[i,
      • line 32: -idx])
      • line 33: }
      • line 34: }
      • line 35: }
      • line 36: }
      • line 37: if (ncol(obj) < min.samples) {
      • line 38: return(NULL)
      • line 39: }
      • line 40: obj
      • line 41: })
      • line 42: names(resList) <- assayNames(pb)
      • line 43: resList <- resList[!vapply(resList, is.null, FUN.VALUE = logical(1))]
      • line 44: new("dreamletProcessedData", resList, data = data_constant,
      • line 45: metadata = metadata(pb)$aggr_means, by = metadata(pb)$agg_pars$by)
    • in aggregateVar
      • line 2: min.var = 0.01, min.samples = 4, min.prop = 0.4, verbose = TRUE,
      • line 3: BPPARAM = SerialParam(progressbar = verbose))
      • line 4:{
      • line 5: pb.sem <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 6: sample_id = sample_id, verbose = FALSE, fun = "sem",
      • line 7: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 8: pb.number <- aggregateToPseudoBulk(sce, assay = assay, cluster_id = cluster_id,
      • line 9: sample_id = sample_id, verbose = FALSE, fun = "number",
      • line 10: BPPARAM = BPPARAM, checkValues = FALSE)
      • line 11: data_constant <- droplevels(as.data.frame(colData(pb.sem)))
      • line 12: resList <- lapply(assayNames(pb.sem), function(CT) {
      • line 15: w <- 1/assay(pb.sem, CT)^2
      • line 16: obj <- new("EList", list(E = as.matrix(var), weights = as.matrix(w)))
      • line 17: include <- (number[1, ] >= min.cells)
      • line 18: obj <- obj[, include, drop = FALSE]
      • line 19: keep <- apply(obj$E, 1, function(x) {
      • line 20: sum(x >= min.var) >= min.prop * length(x)
      • line 21: })
      • line 22: obj <- obj[keep, , drop = FALSE]
      • line 23: if (any(!is.finite(obj$weights))) {
      • line 24: for (i in seq(nrow(obj$weights))) {
      • line 25: if (any(!is.finite(obj$weights[i, ]))) {
      • line 26: idx <- which(!is.finite(obj$weights[i, ]))
      • line 27: if (length(idx) > 0) {
      • line 28: obj$weights[i, idx] <- max(obj$weights[i,
      • line 29: -idx])
      • line 30: }
      • line 31: }
      • line 32: }
      • line 33: }
      • line 34: if (ncol(obj) < min.samples) {
      • line 35: return(NULL)
      • line 36: }
      • line 37: obj
      • line 38: })
      • line 39: names(resList) <- assayNames(pb.sem)
      • line 40: resList <- resList[!vapply(resList, is.null, FUN.VALUE = logical(1))]
      • line 41: new("dreamletProcessedData", resList, data = data_constant,
      • line 42: metadata = metadata(pb.sem)$aggr_means, by = c(cluster_id,
  • repetition in dropRedundantTerms and removeConstantTerms
    • in dropRedundantTerms
      • line 1: data, tol = 0.001)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
      • line 5: data <- data[, colnames(data) %in% unique(all.vars(formula)),
      • line 6: drop = FALSE]
      • line 7: data <- droplevels(data)
      • line 8: checkFormula(formula, data)
      • line 9: trmf <- terms(formula)
      • line 10: intercept <- attr(trmf, "intercept")
      • line 11: fterms <- attr(trmf, "term.labels")
      • line 28: if (length(excludeVar) > 0) {
      • line 29: fterms_new <- fterms
      • line 30: for (x in excludeVar) {
      • line 31: fterms_new <- gsub(paste0(x, "$"), "1", fterms_new)
      • line 32: fterms_new <- gsub(paste0(x, "\)"), "1)", fterms_new)
      • line 33: }
      • line 34: fterms_new <- unique(fterms_new)
      • line 35: fterms_new <- fterms_new[fterms_new != ""]
      • line 36: fterms_new <- fterms_new[fterms_new != "1"]
      • line 37: }
      • line 38: else {
      • line 39: fterms_new <- fterms
      • line 40: }
      • line 41: exclude <- grep(":1|1:|\| 1", fterms_new)
      • line 42: if (length(exclude) > 0) {
      • line 43: fterms_new <- fterms_new[-exclude]
      • line 44: }
      • line 45: response <- all.vars(update.formula(formula, . ~ NULL))
      • line 46: if (attr(trmf, "response") == 0)
      • line 47: response <- NULL
      • line 48: if (length(fterms_new) > 0) {
      • line 49: form_new <- reformulate(fterms_new, response = response)
      • line 50: }
      • line 51: else {
      • line 52: form_new <- ~1
      • line 53: }
      • line 54: if (intercept == 0) {
      • line 55: form_new <- update.formula(form_new, ~. - 1)
      • line 56: }
      • line 57: environment(form_new) <- environment(formula)
      • line 58: form_new
    • in removeConstantTerms
      • line 1: data)
      • line 2:{
      • line 3: stopifnot(is(formula, "formula"))
      • line 4: stopifnot(is(data, "data.frame"))
      • line 5: data <- data[, colnames(data) %in% unique(all.vars(formula)),
      • line 6: drop = FALSE]
      • line 7: data <- droplevels(data)
      • line 8: checkFormula(formula, data)
      • line 9: trmf <- terms(formula)
      • line 10: intercept <- attr(trmf, "intercept")
      • line 11: fterms <- attr(trmf, "term.labels")
      • line 30: if (length(excludeVar) > 0) {
      • line 31: fterms_new <- fterms
      • line 32: for (x in excludeVar) {
      • line 33: fterms_new <- gsub(paste0(x, "$"), "1", fterms_new)
      • line 34: fterms_new <- gsub(paste0(x, "\)"), "1)", fterms_new)
      • line 35: }
      • line 36: fterms_new <- unique(fterms_new)
      • line 37: fterms_new <- fterms_new[fterms_new != ""]
      • line 38: }
      • line 39: else {
      • line 40: fterms_new <- fterms
      • line 41: }
      • line 42: exclude <- grep(":1|1:|\| 1", fterms_new)
      • line 43: if (length(exclude) > 0) {
      • line 44: fterms_new <- fterms_new[-exclude]
      • line 45: }
      • line 46: response <- all.vars(update.formula(formula, . ~ NULL))
      • line 47: if (attr(trmf, "response") == 0)
      • line 48: response <- NULL
      • line 49: if (length(fterms_new) > 0) {
      • line 50: form_new <- reformulate(fterms_new, response = response)
      • line 51: }
      • line 52: else {
      • line 53: form_new <- ~1
      • line 54: }
      • line 55: if (intercept == 0) {
      • line 56: form_new <- update.formula(form_new, ~. - 1)
      • line 57: }
      • line 58: environment(form_new) <- environment(formula)
      • line 59: form_new

        Documentation

• [ ] Important: remove unused file vignettes/mashr.Rmd.orig.

  C and Fortran code

• [ ] Important: remove unused code in the colsum_beachmat.cpp. You have test functions to validate the code.

Unit test

• [ ] Important: remove unused code in the inst/unitTests/test\_pseudobulk.R
• [ ] Important: move the library function to outside of the test\_functions. You may want to put all the library load into file tests/runTests.R

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Dear Package contributor,

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Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): dreamlet_0.99.29.tar.gz macOS 12.6.5 Monterey: dreamlet_0.99.29.tar.gz

Links above active for 21 days.

Remember: if you submitted your package after July 7th, 2020, when making changes to your repository push to git@git.bioconductor.org:packages/dreamlet to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

[GabrielHoffman]

@jianhong I have addressed all the important issues. Confirm?

Gabriel

[GabrielHoffman]

@jianhong Following up to finalize this submission

Best, Gabriel

[bioc-issue-bot]

Your package has been accepted. It will be added to the Bioconductor nightly builds.

Thank you for contributing to Bioconductor!

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[lshep]

The default branch of your GitHub repository has been added to Bioconductor's git repository as branch devel.

To use the git.bioconductor.org repository, we need an 'ssh' key to associate with your github user name. If your GitHub account already has ssh public keys (https://github.com/GabrielHoffman.keys is not empty), then no further steps are required. Otherwise, do the following:

1. Add an SSH key to your github account
2. Submit your SSH key to Bioconductor

See further instructions at

https://bioconductor.org/developers/how-to/git/

for working with this repository. See especially

https://bioconductor.org/developers/how-to/git/new-package-workflow/ https://bioconductor.org/developers/how-to/git/sync-existing-repositories/

to keep your GitHub and Bioconductor repositories in sync.

Your package will be included in the next nigthly 'devel' build (check-out from git at about 6 pm Eastern; build completion around 2pm Eastern the next day) at

https://bioconductor.org/checkResults/

(Builds sometimes fail, so ensure that the date stamps on the main landing page are consistent with the addition of your package). Once the package builds successfully, you package will be available for download in the 'Devel' version of Bioconductor using BiocManager::install("dreamlet"). The package 'landing page' will be created at

https://bioconductor.org/packages/dreamlet

If you have any questions, please contact the bioc-devel mailing list (https://stat.ethz.ch/mailman/listinfo/bioc-devel); this issue will not be monitored further.