Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • DESeq2 DESeqDataSetFromMatrix problem

    Dear experts,

    I encountered a strange problem when using the DESeqDataSetFromMatrix command from DESeq2. I have set up the countData and colData in the required format the same as the sample data described in the tutorial. The column names of the countdata refer to samples, while the row names refer to genes. The row number of colData is the same as the samples. What causes the problem and how to fix it?

    Any input would be very appreciated! Thank you and Merry Christmas!

    Thanks,

    Xiayu


    > colData
    condition
    HOSC1-cellline HPVpos
    UD-SCC2 HPVpos
    UM-SCC104 HPVpos
    UM-SCC47 HPVpos
    UPCI_SCC090 HPVpos
    UPCI_SCC152 HPVpos
    UPCI_SCC154 HPVpos
    UT-SCC45 HPVpos
    VU-147T HPVpos
    1483 HPVneg
    183 HPVneg
    ....


    > head(countdata[,1:11])
    HOSC1-cellline UD-SCC2 UM-SCC104 UM-SCC47 UPCI_SCC090 UPCI_SCC152
    A1BG 2 0 1 0 80 65
    A1CF 0 0 0 0 0 0
    A2M 3 0 0 0 1 0
    A2ML1 157 1 190 21 759 747
    A4GALT 625 0 780 590 346 1134
    A4GNT 0 0 0 0 1 0
    UPCI_SCC154 UT-SCC45 VU-147T 1483 183
    A1BG 6 9 3 53 30
    A1CF 0 0 0 1 5
    A2M 1 0 1 2 1
    A2ML1 1 32 4 14 2280
    A4GALT 630 235 1414 107 2072
    A4GNT 2 0 0 1 1


    > dds <- DESeqDataSetFromMatrix(countData = countdata, colData = colData, design = ~ condition)
    Error in validObject(.Object) :
    invalid class RangesList?object: number of rows in DataTable 'mcols(x)' must match length of 'x'


    > sessionInfo()
    R version 3.1.0 (2014-04-10)
    Platform: x86_64-unknown-linux-gnu (64-bit)

    locale:
    [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
    [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
    [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
    [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
    [9] LC_ADDRESS=C LC_TELEPHONE=C
    [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

    other attached packages:
    [1] DESeq2_1.6.3 RcppArmadillo_0.4.320.0 Rcpp_0.11.3
    [4] GenomicRanges_1.16.4 GenomeInfoDb_1.0.2 IRanges_1.22.10
    [7] S4Vectors_0.4.0 BiocGenerics_0.12.1

    loaded via a namespace (and not attached):
    [1] acepack_1.3-3.3 annotate_1.42.1 AnnotationDbi_1.26.1
    [4] base64enc_0.1-2 BatchJobs_1.3 BBmisc_1.8
    [7] Biobase_2.24.0 BiocParallel_0.6.1 brew_1.0-6
    [10] checkmate_1.5.1 cluster_1.15.3 codetools_0.2-9
    [13] colorspace_1.2-4 DBI_0.3.1 digest_0.6.4
    [16] fail_1.2 foreach_1.4.2 foreign_0.8-61
    [19] Formula_1.1-2 genefilter_1.46.1 geneplotter_1.42.0
    [22] ggplot2_1.0.0 grid_3.1.0 gtable_0.1.2
    [25] Hmisc_3.14-6 iterators_1.0.7 lattice_0.20-29
    [28] latticeExtra_0.6-26 locfit_1.5-9.1 MASS_7.3-35
    [31] munsell_0.4.2 nnet_7.3-8 plyr_1.8.1
    [34] proto_0.3-10 RColorBrewer_1.1-2 reshape2_1.4.1
    [37] rpart_4.1-8 RSQLite_0.11.4 scales_0.2.4
    [40] sendmailR_1.2-1 splines_3.1.0 stringr_0.6.2
    [43] survival_2.37-7 tools_3.1.0 XML_3.98-1.1
    [46] xtable_1.7-4 XVector_0.4.0

  • #2
    What's the output of:
    Code:
    dim(countdata)
    and
    Code:
    dim(colData)

    Comment


    • #3
      Originally posted by dpryan View Post
      What's the output of:
      Code:
      dim(countdata)
      and
      Code:
      dim(colData)
      > dim(countdata)
      [1] 20587 69
      > dim(colData)
      [1] 69 1

      Thank you for your reply!

      Comment


      • #4
        Problem solved. It turns out to be the library issue in the linux system. The code should be fine. Thank you all and Merry Christmas!

        Comment


        • #5
          We try to discourage cross posting the same question on different forums because it duplicates the effort for answerers and splits answers.

          Comment

          Latest Articles

          Collapse

          • seqadmin
            Essential Discoveries and Tools in Epitranscriptomics
            by seqadmin


            The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist on Modified Bases...
            Yesterday, 07:01 AM
          • seqadmin
            Current Approaches to Protein Sequencing
            by seqadmin


            Proteins are often described as the workhorses of the cell, and identifying their sequences is key to understanding their role in biological processes and disease. Currently, the most common technique used to determine protein sequences is mass spectrometry. While still a valuable tool, mass spectrometry faces several limitations and requires a highly experienced scientist familiar with the equipment to operate it. Additionally, other proteomic methods, like affinity assays, are constrained...
            04-04-2024, 04:25 PM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by seqadmin, 04-11-2024, 12:08 PM
          0 responses
          39 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 04-10-2024, 10:19 PM
          0 responses
          41 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 04-10-2024, 09:21 AM
          0 responses
          35 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 04-04-2024, 09:00 AM
          0 responses
          55 views
          0 likes
          Last Post seqadmin  
          Working...
          X