Hi, I cannot make my dds structure ready for DESeq2 analysis. I have used quantMode GeneCounts from STAR mapping to create the *ReadsPerGene.out.tab files read into R at the start.

# List of file names and their corresponding shortened names
file_names <- c(
    "star_3-IgM19neg-3c_S399_L002_R1_001AlignmentJob_ReadsPerGene.out.tab",
    "star_4-IgM19pos-4b_S397_L002_R1_001AlignmentJob_ReadsPerGene.out.tab",
    "star_1-unstain-1a_S385_L002_R1_001AlignmentJob_ReadsPerGene.out.tab")

shortened_names <- c("IgM19neg", "IgM19pos", "unstained_control")

# Create an empty list to store sample data frames
sample_data_list <- list()

# Loop through file names and read the count data
for (i in 1:length(file_names)) {
    file_name <- file_names[i]
    shortened_name <- shortened_names[i]

    # Read the count data for the current sample with header
    count_data <- read.table(file_name, header = TRUE, sep = "\t")

    # Rename columns as needed
    colnames(count_data) <- c("gene ID", "unstranded_RNA_seq", "R1", "R2")

    # Add the sample name as a column (using the shortened name)
    count_data$Sample <- shortened_name

    # Append the sample data to the list
    sample_data_list[[shortened_name]] <- count_data
}

# Access the data for each sample by name (e.g., sample_data_list[["IgM19neg"]])

# Combine the sample data frames into a single data frame
combined_data <- do.call(rbind, sample_data_list)
head(combined_data)

# Create a function to remove the top N rows from each sample
remove_top_n_rows <- function(dataframe, n) {
  # Split the dataframe by "Sample" column
  sample_splits <- split(dataframe, dataframe$Sample)

  # Remove the top N rows from each sample
  for (sample_name in names(sample_splits)) {
    sample_df <- sample_splits[[sample_name]]
    sample_splits[[sample_name]] <- sample_df[-(1:n), ]
  }

  # Combine the modified samples back into a single dataframe
  modified_data <- do.call(rbind, sample_splits)
  return(modified_data)
}

# Remove the top 3 rows from each sample
combined_data <- remove_top_n_rows(combined_data, 3)
# Change row names to sequential integers
rownames(combined_data) <- seq(nrow(combined_data))

head(combined_data)

   gene ID unstranded_RNA_seq R1 R2   Sample
1 LOC115544811                 10  7  3 IgM19neg
2 LOC115544869                  0  0  0 IgM19neg
3 LOC115536824                  2  1  1 IgM19neg
4 LOC115536896                  0  0  0 IgM19neg
5 LOC115539754                  0  0  0 IgM19neg
6 LOC115539762                  0  0  0 IgM19neg

# Transpose the data and use 'unstranded_RNA_seq' as gene counts
transposed_data <- data.frame(gene_id = combined_data$`gene ID`, 
                              IgM19neg = combined_data$unstranded_RNA_seq[combined_data$Sample == "IgM19neg"],
                              IgM19pos = combined_data$unstranded_RNA_seq[combined_data$Sample == "IgM19pos"],
                              unstained_control = combined_data$unstranded_RNA_seq[combined_data$Sample == "unstained_control"])

head(transposed_data)

   gene_id IgM19neg IgM19pos unstained_control
1 LOC115544811       10        0                 0
2 LOC115544869        0        0                 0
3 LOC115536824        2        1                 0
4 LOC115536896        0        0                 0
5 LOC115539754        0        0                 0
6 LOC115539762        0        0                 0

# Create a DESeqDataSet object from your transposed_data
dds <- DESeqDataSetFromMatrix(
  countData = transposed_data[, -1],  # Remove the first column (gene_id)
  colData = transposed_data[, 1, drop = FALSE],  # Keep all columns for colData
  design = ~Sample  # Design formula specifying your samples
)

Error in DESeqDataSetFromMatrix(countData = transposed_data[, -1], colData = transposed_data[, : ncol(countData) == nrow(colData) is not TRUE

sessionInfo( ) R version 4.1.1 (2021-08-10) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS 13.5.1

Matrix products: default LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages: [1] parallel stats4 stats graphics grDevices utils datasets methods base

other attached packages: [1] edgeR_3.34.1 limma_3.48.3 DESeq2_1.32.0 SummarizedExperiment_1.22.0 Biobase_2.52.0 MatrixGenerics_1.4.3
[7] matrixStats_0.61.0 GenomicRanges_1.44.0 GenomeInfoDb_1.28.4 IRanges_2.26.0 S4Vectors_0.30.2 BiocGenerics_0.38.0
[13] RColorBrewer_1.1-2 umap_0.2.7.0 forcats_0.5.1 stringr_1.4.0 purrr_0.3.4 readr_2.1.0
[19] tidyr_1.1.4 tibble_3.1.6 tidyverse_1.3.1 splitstackshape_1.4.8 plyr_1.8.6 cowplot_1.1.1
[25] ggplot2_3.3.5 magrittr_2.0.1 Matrix_1.3-4 SeuratObject_4.0.2 Seurat_4.0.5 dplyr_1.0.7

loaded via a namespace (and not attached): [1] readxl_1.3.1 backports_1.4.0 igraph_1.2.8 lazyeval_0.2.2 splines_4.1.1 BiocParallel_1.26.2 listenv_0.8.0
[8] scattermore_0.7 digest_0.6.28 htmltools_0.5.2 fansi_0.5.0 memoise_2.0.0 tensor_1.5 cluster_2.1.2
[15] ROCR_1.0-11 tzdb_0.2.0 Biostrings_2.60.2 annotate_1.70.0 globals_0.14.0 modelr_0.1.8 askpass_1.1
[22] spatstat.sparse_2.0-0 colorspace_2.0-2 blob_1.2.2 rvest_1.0.2 ggrepel_0.9.1 haven_2.4.3 xfun_0.28
[29] crayon_1.4.2 RCurl_1.98-1.5 jsonlite_1.7.2 genefilter_1.74.1 spatstat.data_2.1-0 survival_3.2-13 zoo_1.8-9
[36] glue_1.5.0 polyclip_1.10-0 gtable_0.3.0 zlibbioc_1.38.0 XVector_0.32.0 leiden_0.3.9 DelayedArray_0.18.0
[43] future.apply_1.8.1 abind_1.4-5 scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1 Rcpp_1.0.7 viridisLite_0.4.0
[50] xtable_1.8-4 reticulate_1.22 spatstat.core_2.3-1 bit_4.0.4 htmlwidgets_1.5.4 httr_1.4.2 ellipsis_0.3.2
[57] ica_1.0-2 XML_3.99-0.8 pkgconfig_2.0.3 uwot_0.1.10 dbplyr_2.1.1 deldir_1.0-6 locfit_1.5-9.4
[64] utf8_1.2.2 AnnotationDbi_1.54.1 tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4 later_1.3.0 cachem_1.0.6
[71] munsell_0.5.0 cellranger_1.1.0 tools_4.1.1 cli_3.1.0 RSQLite_2.2.8 generics_0.1.1 broom_0.7.10
[78] ggridges_0.5.3 evaluate_0.14 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-3 bit64_4.0.5 knitr_1.36
[85] fs_1.5.0 fitdistrplus_1.1-6 RANN_2.6.1 KEGGREST_1.32.0 pbapply_1.5-0 future_1.23.0 nlme_3.1-153
[92] mime_0.12 xml2_1.3.2 compiler_4.1.1 rstudioapi_0.13 plotly_4.10.0 png_0.1-7 spatstat.utils_2.2-0
[99] reprex_2.0.1 geneplotter_1.70.0 stringi_1.7.5 RSpectra_0.16-0 lattice_0.20-45 vctrs_0.3.8 pillar_1.6.4
[106] lifecycle_1.0.1 spatstat.geom_2.3-0 lmtest_0.9-39 RcppAnnoy_0.0.19 data.table_1.14.2 bitops_1.0-7 irlba_2.3.3
[113] httpuv_1.6.3 patchwork_1.1.1 R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.29.0
[120] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1 openssl_1.4.5 withr_2.4.2 sctransform_0.3.2 GenomeInfoDbData_1.2.6 [127] mgcv_1.8-38 hms_1.1.1 grid_4.1.1 rpart_4.1-15 rmarkdown_2.11 Rtsne_0.15 shiny_1.7.1
[134] lubridate_1.8.0

dim(transposed_data)

[1] 97344 4

I didn't try to go through all that code. However, I will point out that it would be much easier to use cbind on your input list rather than rbind. There's not much profit in combining things in one long data.frame if what you really want is a data.frame with a column per sample, which is what cbind would give you.

But the take-home message is this:

Error in DESeqDataSetFromMatrix(countData = transposed_data[, -1], colData = transposed_data[, : ncol(countData) == nrow(colData) is not TRUE

Which is telling you that the number of rows in your colData object (which describes the samples) does not match the number of columns in your countData object. Which makes sense, given that the thing you are using as your colData describes the rows of your countData object rather than the columns.

Also, you need to update R and Bioconductor. Both of yours are over 2 years, and we technically do not provide support for anything but the release version.