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Published 20 hours ago • verified publisher icon Bioconductor

VDJdive

Open kstreet13 opened this issue 3 years ago • 14 comments

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

  • Repository: https://github.com/kstreet13/VDJdive

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

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kstreet13 avatar Jul 20 '22 19:07 kstreet13

Hi @kstreet13

Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.

The DESCRIPTION file for this package is:

Package: VDJdive
Title: Analysis Tools for 10X V(D)J Data 
Version: 0.99.1
Authors@R: c(
    person("Kelly", "Street", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0001-6379-5013")),
    person("Mercedeh", "Movassagh", role = "aut", email = "[email protected]", comment = c(ORCID = "0000-0001-7690-0230")),
    person("Jill", "Lundell", role = "aut", email = "[email protected]", comment = c(ORCID = "0000-0002-6048-4700")),
    person("Jared", "Brown", role = "ctb"),
    person("Linglin", "Huang", role = "ctb"))
Description: This package provides functions for handling and analyzing immune receptor repertoire data, as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots.
Depends:
    R (>= 4.1)
Imports:
    basilisk,
    BiocParallel,
    cowplot,
    ggplot2,
    gridExtra,
    IRanges,
    Matrix,
    methods,
    RColorBrewer,
    reticulate,
    S4Vectors,
    SingleCellExperiment,
    stats,
    SummarizedExperiment,
    utils
Suggests:
    breakaway,
    covr,
    knitr,
    rmarkdown,
    testthat,
    BiocStyle
License: Artistic-2.0
URL: https://github.com/kstreet13/VDJdive
BugReports: https://github.com/kstreet13/VDJdive/issues
biocViews: 
    Software,
    ImmunoOncology, 
    SingleCell,
    Annotation,
    RNASeq,
    TargetedResequencing
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.1
StagedInstall: no
VignetteBuilder: knitr
Collate: 
    'clonoStats_class.R'
    'abundanceVDJ.R'
    'barVDJ.R'
    'basilisk.R'
    'boxVDJ.R'
    'calculateDiversity.R'
    'clonoStats_helpers.R'
    'clonoStats.R'
    'contigs.R'
    'pieVDJ.R'
    'runBreakaway.R'
    'runVDJPCA.R'
    'scatterVDJ.R'
    'setup.R'
    'utils.R'

bioc-issue-bot avatar Jul 20 '22 19:07 bioc-issue-bot

A reviewer has been assigned to your package. Learn what to expect during the review process.

IMPORTANT: Please read this documentation for setting up remotes to push to git.bioconductor.org. It is required to push a version bump to git.bioconductor.org to trigger a new build.

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bioc-issue-bot avatar Jul 22 '22 18:07 bioc-issue-bot

Dear Package contributor,

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

Your package has been built on Linux, Mac, and Windows.

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

Please see the build report for more details. This link will be active for 21 days.

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

bioc-issue-bot avatar Jul 22 '22 18:07 bioc-issue-bot

Package 'VDJdive' Review

Thank you for submition your package to Bioconductor. The package passed check and build. It is in pretty good shape. 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.

The DESCRIPTION file

  • [ ] At least 3 sentences in Description field is required.
  • [ ] 'LazyData:' in the 'DESCRIPTION' should be set to false or removed
  • [ ] R version should be no less than 4.2

The NAMESPACE file

  • [ ] Selective imports using importFrom instead of import all with import.
    • in line 43 import(IRanges)
    • in line 44 import(S4Vectors)

General package development

  • [ ] NOTE: Consider adding a NEWS file, so your package news will be included in Bioconductor release announcements.

R code

  • [ ] NOTE: no direct slot access with @ or slot() - accessors implemented and used.
    • In file R/clonoStats.R:
      • at line 305 found ' names1 = x@names1, names2 = x@names2, group = grpVar,'
    • In file R/runVDJPCA.R:
      • at line 53 found ' runVDJPCA(x@abundance, ...)'
    • In file R/utils.R:
      • at line 20 found ' P2 <- matrix(x[, ind2, drop = FALSE]@x, nrow = 2)'
      • at line 30 found ' counts <- x[, ind3p, drop = FALSE]@x'
      • at line 138 found ' if(all(x@x %% 1 == 0)){ # integer counts'
      • at line 183 found ' return(summarizeClonotypes(metadata(x)[[clonoStats]]@assignment,'
      • at line 201 found ' if(is.null(x@assignment)){'
      • at line 205 found ' summarizeClonotypes(x@assignment, by, ...)'
      • at line 301 found ' return(splitClonotypes(metadata(x)[[clonoStats]]@assignment, '
      • at line 310 found ' if(is.null(x@assignment)){'
      • at line 314 found ' splitClonotypes(x@assignment, by)'
      • at line 327 found ' cat('clonotypes:', length(object@names1), '\n')'
      • at line 328 found ' cat('cells:', length(object@group), '\n')'
      • at line 329 found ' groups <- levels(object@group)'
      • at line 331 found ' cat('has assignment:', !is.null(object@assignment))'
      • at line 348 found ' paste(object@names1, object@names2)'
      • at line 374 found ' object@abundance'
      • at line 399 found ' object@frequency'
      • at line 425 found ' object@assignment'
      • at line 451 found ' object@group'
  • [ ] NOTE: :: is not suggested in source code unless you can make sure all the packages are imported. Please note that you need to manully double check the import items when you make any change in the DESCRIPTION file during development.
  • [ ] NOTE: Vectorize: for loops present, try to replace them by *apply funcitons.
    • In file R/abundanceVDJ.R:
      • at line 48 found ' for (i in seq_len(ncol(a))) {'
    • In file R/barVDJ.R:
      • at line 44 found ' for (i in seq_len(ncol(x))) {'
    • In file R/calculateDiversity.R:
      • at line 91 found ' for (i in seq_len(t)[-1]){'
      • at line 98 found ' for (i in seq_len(t)){'
      • at line 99 found ' for (j in seq_len(t)){'
      • at line 110 found ' for (i in seq_len(t)){'
      • at line 111 found ' for (j in seq_len(t)){'
    • In file R/clonoStats_helpers.R:
      • at line 114 found ' for(i in seq_along(temp)){'
      • at line 130 found ' for(ii in l1.idx){'
      • at line 156 found ' for(i in seq_along(temp)){'
      • at line 319 found ' for(i in seq_along(contigs)){'
      • at line 379 found ' for(i in ind.multiple){'
    • In file R/clonoStats.R:
      • at line 219 found ' for(i in ind){'
    • In file R/pieVDJ.R:
      • at line 45 found ' for (i in seq_len(ncol(x))) {'
    • In file R/setup.R:
      • at line 51 found ' for (ii in seq_along(samples)) {'
      • at line 226 found ' for(samp in unique(contigs$sample)){'
    • In file R/utils.R:
      • at line 47 found ' for(p in probs){'
  • [ ] Important: Remove unused code.
    • In file R/calculateDiversity.R:
      • at line 67 found ' #B2 <- B1 - f[1]'
      • at line 68 found ' #C2 <- sum(seq_len(t) * (seq_len(t)-1) * f[seq_len(t)])'
      • at line 123 found ' # CI0 <- matrix(c(lb,ub),1,2)'
      • at line 124 found ' # colnames(CI0) <- c("lb","ub")'
      • at line 125 found ' # return(list(est=chao4, CI=CI0))'
    • In file R/clonoStats.R:
      • at line 290 found ' # grpVar <- clonoGroup(x)'
    • In file R/pieVDJ.R:
      • at line 81 found ' # datL <- data.frame(values = seq_len(100))'
      • at line 82 found ' # pieL <- ggplot2::ggplot(datL, ggplot2::aes(x = "", y = values, fill = values, weight = values)) +'
      • at line 83 found ' # ggplot2::geom_col(position = "stack") +'
      • at line 84 found ' # ggplot2::scale_fill_continuous("", type = "viridis", breaks = c(1, 100), '
      • at line 85 found ' # labels = c("Singletons", "Most expanded")) +'
      • at line 86 found ' # ggplot2::theme_bw() +'
      • at line 87 found ' # ggplot2::theme(legend.position = "bottom") '
    • In file R/setup.R:
      • at line 55 found ' #tcr_ii$type <- "TCR"'
      • at line 56 found ' #tcr_ii$clonotype_tcr <- paste0(sample_ids[ii], "-", '
      • at line 57 found ' #tcr_ii$raw_clonotype_id)'
  • [ ] NOTE: Functional programming: code repetition.
    • repetition in .CHN_sample , .MC_sample and .UNIQ_sample
      • in .CHN_sample
        • line 6: if (length(all.alphas) == 0) {
        • line 7: clono <- Matrix(0, nrow = length(contigs), ncol = 0)
        • line 8: rownames(clono) <- names(contigs)
        • line 9: return(clono)
        • line 10: }
        • line 11: clono <- Matrix(0, nrow = length(contigs), ncol = length(all.alphas))
      • in .MC_sample
        • line 17: if (length(all.alphas) == 0) {
        • line 18: clono <- Matrix(0, nrow = length(contigs), ncol = 0)
        • line 19: rownames(clono) <- names(contigs)
        • line 20: return(clono)
        • line 21: }
        • line 24: if (length(all.betas) == 0) {
        • line 25: clono <- Matrix(0, nrow = length(contigs), ncol = 0)
        • line 26: rownames(clono) <- names(contigs)
        • line 27: return(clono)
        • line 28: }
        • line 29: counts <- Matrix(0, nrow = length(all.alphas), ncol = length(all.betas),
      • in .UNIQ_sample
        • line 15: type1]))
        • line 16: if (length(all.alphas) == 0) {
        • line 17: clono <- Matrix(0, nrow = length(contigs), ncol = 0)
        • line 18: rownames(clono) <- names(contigs)
        • line 19: return(clono)
        • line 20: }
        • line 23: if (length(all.betas) == 0) {
        • line 24: clono <- Matrix(0, nrow = length(contigs), ncol = 0)
        • line 25: rownames(clono) <- names(contigs)
        • line 26: return(clono)
        • line 27: }
        • line 28: counts <- Matrix(0, nrow = length(all.alphas), ncol = length(all.betas),
    • repetition in .CR_sample , .MC_sample and .UNIQ_sample
      • in .CR_sample
        • line 11: poss.indices <- match(clonoID, all_clonotypes)
        • line 12: clonoJ <- as.integer(unlist(poss.indices) - 1)
        • line 13: clonoP <- as.integer(c(0, cumsum(lengths(poss.indices))))
        • line 14: clonoX <- rep(1, sum(lengths(poss.indices)))
        • line 15: clono <- new("dgRMatrix", j = clonoJ, p = clonoP, x = clonoX,
        • line 16: Dim = as.integer(c(length(contigs), length(all_clonotypes))))
      • in .MC_sample
        • line 57: counts <- as.numeric(counts)
        • line 58: clonoJ <- as.integer(unlist(poss.indices) - 1)
        • line 59: clonoP <- as.integer(c(0, cumsum(lengths(poss.indices))))
        • line 60: clonoX <- rep(1, sum(lengths(poss.indices)))
        • line 61: clono <- new("dgRMatrix", j = clonoJ, p = clonoP, x = clonoX,
        • line 62: Dim = as.integer(c(length(contigs), length(counts))))
      • in .UNIQ_sample
        • line 47: counts <- as.numeric(counts)
        • line 48: clonoJ <- as.integer(unlist(poss.indices) - 1)
        • line 49: clonoP <- as.integer(c(0, cumsum(lengths(poss.indices))))
        • line 50: clonoX <- rep(1, sum(lengths(poss.indices)))
        • line 51: clono <- new("dgRMatrix", j = clonoJ, p = clonoP, x = clonoX,
        • line 52: Dim = as.integer(c(length(contigs), length(counts))))
    • repetition in .EM_sample , .MC_sample and .UNIQ_sample
      • in .EM_sample
        • line 3: contigs <- .prepContigs(contigs, type)
        • line 4: if (type == "TCR") {
        • line 5: type1 <- "TRA"
        • line 6: type2 <- "TRB"
        • line 7: }
        • line 8: if (type == "BCR") {
        • line 9: type1 <- "IGH"
        • line 10: type2 <- c("IGL", "IGK")
        • line 11: }
        • line 12: nAlpha <- sum(contigs[, "chain"] %in% type1)
        • line 13: nBeta <- sum(contigs[, "chain"] %in% type2)
        • line 14: all.alphas <- unique(unlist(contigs[, "cdr3"][contigs[, "chain"] %in%
        • line 15: type1]))
        • line 20: counts <- Matrix(0, nrow = length(all.alphas), ncol = length(all.betas),
        • line 21: sparse = TRUE)
        • line 22: rownames(counts) <- all.alphas
        • line 23: colnames(counts) <- all.betas
        • line 24: ind.unique <- which(nAlpha == 1 & nBeta == 1)
        • line 25: ind.multiple <- which(nAlpha > 0 & nBeta > 0 & nAlpha + nBeta >
        • line 26: 2)
        • line 30: wa <- match(contigs[, "cdr3"][contigs[, "chain"] %in% type1],
        • line 31: all.alphas)
        • line 32: wb <- match(contigs[, "cdr3"][contigs[, "chain"] %in% type2],
        • line 33: all.betas)
        • line 34: poss.indices <- as.list(length(all.alphas) * (wb - 1L) +
        • line 35: wa)
        • line 55: })
        • line 56: temp <- contigs[ind.unique]
        • line 57: uniquecounts <- unclass(table(unlist(temp[temp[, "chain"] %in%
        • line 58: type1, "cdr3"]), unlist(temp[temp[, "chain"] %in% type2,
        • line 59: "cdr3"])))
        • line 60: if (length(temp) > 0) {
        • line 61: counts[rownames(uniquecounts), colnames(uniquecounts)] <- uniquecounts
        • line 62: }
        • line 63: uniquecounts <- counts <- as.numeric(counts)
        • line 117: }))
        • line 118: clono <- new("dgRMatrix", j = clonoJ, p = clonoP, x = clonoX,
        • line 119: Dim = as.integer(c(length(contigs), length(counts))))
        • line 120: colnames(clono) <- as.character(outer(all.alphas, all.betas,
        • line 121: FUN = paste))
        • line 122: clono <- clono[, which(colSums(clono) > 0), drop = FALSE]
        • line 123: rownames(clono) <- names(contigs)
        • line 124: return(clono)
      • in .MC_sample
        • line 4: contigs <- .prepContigs(contigs, type)
        • line 5: if (type == "TCR") {
        • line 6: type1 <- "TRA"
        • line 7: type2 <- "TRB"
        • line 8: }
        • line 9: if (type == "BCR") {
        • line 10: type1 <- "IGH"
        • line 11: type2 <- c("IGL", "IGK")
        • line 12: }
        • line 13: nAlpha <- sum(contigs[, "chain"] %in% type1)
        • line 14: nBeta <- sum(contigs[, "chain"] %in% type2)
        • line 15: all.alphas <- unique(unlist(contigs[, "cdr3"][contigs[, "chain"] %in%
        • line 16: type1]))
        • line 29: counts <- Matrix(0, nrow = length(all.alphas), ncol = length(all.betas),
        • line 30: sparse = TRUE)
        • line 31: rownames(counts) <- all.alphas
        • line 32: colnames(counts) <- all.betas
        • line 33: ind.unique <- which(nAlpha == 1 & nBeta == 1)
        • line 34: ind.multiple <- which(nAlpha > 0 & nBeta > 0 & nAlpha + nBeta >
        • line 35: 2)
        • line 43: wa <- match(contigs[, "cdr3"][contigs[, "chain"] %in% type1],
        • line 44: all.alphas)
        • line 45: wb <- match(contigs[, "cdr3"][contigs[, "chain"] %in% type2],
        • line 46: all.betas)
        • line 47: poss.indices <- as.list(length(all.alphas) * (wb - 1L) +
        • line 48: wa)
        • line 50: temp <- contigs[ind.unique]
        • line 51: uniquecounts <- unclass(table(unlist(temp[temp[, "chain"] %in%
        • line 52: type1, "cdr3"]), unlist(temp[temp[, "chain"] %in% type2,
        • line 53: "cdr3"])))
        • line 54: if (length(temp) > 0) {
        • line 55: counts[rownames(uniquecounts), colnames(uniquecounts)] <- uniquecounts
        • line 56: }
        • line 57: counts <- as.numeric(counts)
        • line 61: clono <- new("dgRMatrix", j = clonoJ, p = clonoP, x = clonoX,
        • line 62: Dim = as.integer(c(length(contigs), length(counts))))
        • line 63: colnames(clono) <- as.character(outer(all.alphas, all.betas,
        • line 64: FUN = paste))
        • line 65: clono <- clono[, which(colSums(clono) > 0), drop = FALSE]
        • line 66: rownames(clono) <- names(contigs)
        • line 67: return(clono)
        • in .UNIQ_sample
          • line 2:{
          • line 3: contigs <- .prepContigs(contigs, type)
          • line 4: if (type == "TCR") {
          • line 5: type1 <- "TRA"
          • line 6: type2 <- "TRB"
          • line 7: }
          • line 8: if (type == "BCR") {
          • line 9: type1 <- "IGH"
          • line 10: type2 <- c("IGL", "IGK")
          • line 11: }
          • line 12: nAlpha <- sum(contigs[, "chain"] %in% type1)
          • line 13: nBeta <- sum(contigs[, "chain"] %in% type2)
          • line 14: all.alphas <- unique(unlist(contigs[, "cdr3"][contigs[, "chain"] %in%
          • line 15: type1]))
          • line 28: counts <- Matrix(0, nrow = length(all.alphas), ncol = length(all.betas),
          • line 29: sparse = TRUE)
          • line 30: rownames(counts) <- all.alphas
          • line 31: colnames(counts) <- all.betas
          • line 32: ind.unique <- which(nAlpha == 1 & nBeta == 1)
          • line 33: wa <- match(contigs[, "cdr3"][contigs[, "chain"] %in% type1],
          • line 34: all.alphas)
          • line 35: wb <- match(contigs[, "cdr3"][contigs[, "chain"] %in% type2],
          • line 36: all.betas)
          • line 37: poss.indices <- as.list(length(all.alphas) * (wb - 1L) +
          • line 38: wa)
          • line 40: temp <- contigs[ind.unique]
          • line 41: uniquecounts <- unclass(table(unlist(temp[temp[, "chain"] %in%
          • line 42: type1, "cdr3"]), unlist(temp[temp[, "chain"] %in% type2,
          • line 43: "cdr3"])))
          • line 44: if (length(temp) > 0) {
          • line 45: counts[rownames(uniquecounts), colnames(uniquecounts)] <- uniquecounts
          • line 46: }
          • line 47: counts <- as.numeric(counts)
          • line 51: clono <- new("dgRMatrix", j = clonoJ, p = clonoP, x = clonoX,
          • line 52: Dim = as.integer(c(length(contigs), length(counts))))
          • line 53: colnames(clono) <- as.character(outer(all.alphas, all.betas,
          • line 54: FUN = paste))
          • line 55: clono <- clono[, which(colSums(clono) > 0), drop = FALSE]
          • line 56: rownames(clono) <- names(contigs)
          • line 57: return(clono)
    • repetition in .EM_sample and .prepContigs
      • in .EM_sample
        • line 4: if (type == "TCR") {
        • line 5: type1 <- "TRA"
        • line 6: type2 <- "TRB"
        • line 7: }
        • line 8: if (type == "BCR") {
        • line 9: type1 <- "IGH"
        • line 10: type2 <- c("IGL", "IGK")
        • line 11: }
      • in .prepContigs
        • line 4: if (type == "TCR") {
        • line 5: type1 <- "TRA"
        • line 6: type2 <- "TRB"
        • line 7: }
        • line 8: if (type == "BCR") {
        • line 9: type1 <- "IGH"
        • line 10: type2 <- c("IGL", "IGK")
    • repetition in .ginisimpson , .invsimpson and .normentropy and .shannon
      • in .ginisimpson
        • line 1:{
        • line 2: p <- p[which(p > 0)]
        • line 3: p <- p/sum(p)
        • line 4: p <- p[which(p > 0)]
        • line 5: return(1 - sum(p^2))
      • in .invsimpson
        • line 1:{
        • line 2: p <- p[which(p > 0)]
        • line 3: p <- p/sum(p)
        • line 4: p <- p[which(p > 0)]
        • line 5: return(1/sum(p^2))
          • in .normentropy
            • line 1:{
            • line 2: p <- p[which(p > 0)]
            • line 3: p <- p/sum(p)
            • line 4: p <- p[which(p > 0)]
          • in .shannon
            • line 1:{
            • line 2: p <- p[which(p > 0)]
            • line 3: p <- p/sum(p)
            • line 4: p <- p[which(p > 0)]
            • line 5: return(-sum(p * log(p)))

Documentation

  • [ ] Important: Vignette should have an Installation section.
    • rmd file vignettes/workflow.Rmd
  • [ ] Important: Please include Bioconductor installation instructions using BiocManager.
    • rmd file vignettes/workflow.Rmd
  • [ ] Important: Vignette includes motivation for submitting to Bioconductor as part of the abstract/intro of the main vignette.
    • rmd file vignettes/workflow.Rmd

jianhong avatar Jul 31 '22 15:07 jianhong

Hi @jianhong,

Thanks very much for reviewing! I have made most of the changes you suggested and pushed a new version (0.99.2), which I thought would trigger a rebuild, but it doesn't seem to have done that. If there is something else I need to do in that regard, please let me know.

The DESCRIPTION file

  • [x] At least 3 sentences in Description field is required.
  • [x] 'LazyData:' in the 'DESCRIPTION' should be set to false or removed
  • [x] R version should be no less than 4.2

The NAMESPACE file

  • [x] Selective imports using importFrom instead of import all with import.
  • [x] NOTE: Consider adding a NEWS file, so your package news will be included in Bioconductor release announcements.

R code

  • [ ] NOTE: no direct slot access with @ or slot() - accessors implemented and used. For this item, I removed all unnecessary direct slot access, but there is still some. Notably, we implemented an S4 class (the clonoStats class) so the accessor functions still need to directly access the corresponding slots. Also, I could not find accessor functions for the dgCMatrix class, but we need (quick) access to the non-zero elements of a large, sparse matrix, and I don't think there is a better way to do that than mat@x.
  • [x] NOTE: :: is not suggested in source code unless you can make sure all the packages are imported.
  • [ ] NOTE: Vectorize: for loops present, try to replace them by *apply funcitons. This has been done wherever possible, but many are still present because the loop is updating an object repeatedly (most notably in the case of the E-M algorithm), so I don't think any of the *apply functions would work.
  • [x] Important: Remove unused code.
  • [ ] NOTE: Functional programming: code repetition. There are several similar but meaningfully different functions that share a few lines of code. I think for the purposes of debugging (especially for users), it's better not to make several more nested functions for things like p <- p[p > 0].

Documentation

  • [x] Important: Vignette should have an Installation section.
  • [x] Important: Please include Bioconductor installation instructions using BiocManager.
  • [x] Important: Vignette includes motivation for submitting to Bioconductor as part of the abstract/intro of the main vignette. I wasn't entirely sure what was meant by this, but I tried to expand on what was already in the Intro section to explain why I think this package would make the analysis of AIRRseq data easier by giving users access to the Bioconductor SingleCellExperiment framework.

kstreet13 avatar Aug 02 '22 22:08 kstreet13

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jianhong avatar Aug 16 '22 12:08 jianhong

Received a valid push on git.bioconductor.org; starting a build for commit id: affe33329f7f2c5329d671f831fb838d650420a8

bioc-issue-bot avatar Aug 17 '22 18:08 bioc-issue-bot

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bioc-issue-bot avatar Aug 17 '22 18:08 bioc-issue-bot

Just to clarify an earlier point, many of the for loops are iteratively modifying a large, sparse matrix object. Most of these could be done with an apply statement which would replace a large section of the matrix, but that would require constructing a dense version of that section, which would substantially increase memory usage.

kstreet13 avatar Aug 25 '22 18:08 kstreet13

Package 'VDJdive' Review

The package passed check and build.It is almost there.

General package development

* NOTE: Please correct the NEWS file name. See https://contributions.bioconductor.org/news.html?q=news#news

R code

  • [ ] Important: Remove unused code.
    • In file R/clonoStats_helpers.R:
      • at line 169 found ' #print(diff[length(diff)])'

jianhong avatar Sep 01 '22 16:09 jianhong

Received a valid push on git.bioconductor.org; starting a build for commit id: 86a5589cb12c160e9e55f216a990bb9902173c29

bioc-issue-bot avatar Sep 01 '22 22:09 bioc-issue-bot

Dear Package contributor,

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bioc-issue-bot avatar Sep 01 '22 22:09 bioc-issue-bot

Thanks very much, @jianhong! I believe that both of these issues have been addressed in the latest update.

kstreet13 avatar Sep 01 '22 22:09 kstreet13

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bioc-issue-bot avatar Sep 02 '22 12:09 bioc-issue-bot

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lshep avatar Sep 09 '22 11:09 lshep