Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • DESeq2 design error

    Hi,

    I am trying to run DESeq2 using the "Differential analysis of count data ~ the DESeq2 package provided in the bioconductor website. I want to do an Multi-factor design. I created my count files using HTseq and created a combined table from all the count files.

    However I am running in the following error after this step:

    Code:
    > dds <- DESeqDataSetFromMatrix(countData = countDATA,
                                 colData = coldata,
                                 design = ~ condition + treatment )
    Error in DESeqDataSet(se, design = design, ignoreRank) : 
      the model matrix is not full rank, so the model cannot be fit as specified.
      one or more variables or interaction terms in the design formula
      are linear combinations of the others and must be removed
    If I try to run the function using only design = ~ condition everything works just fine.

    my coldata table:
    Code:
    row.names	condition	type	treatment	autoAntibodies
    HS06	        0	0	0	0
    HS07        	0	0	0	0
    HS10        	0	0	0	0
    HS15	        0	0	0	0
    HS16	        0	0	0	0
    HS17	        0	0	0	0
    RITIS01	1	2	1	1
    RITIS02	1	2	2	1
    RITIS07	1	2	1	1
    RITIS09	1	1	1	2
    RITIS10	1	2	2	1
    RITIS12	1	1	1	2
    RITIS14	1	2	1	1
    RITIS16	1	1	1	1
    I would like to create a full design which takes all the factors into account (condition, type, treatment and autoAntibodies). Could you please explain why the above design does not work.

    Thanks,
    Even

  • #2
    If "type", "treatment", and "autoAntibodies" are factors, then "condition" is dictated by them.

    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...
      04-22-2024, 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, Yesterday, 08:47 AM
    0 responses
    12 views
    0 likes
    Last Post seqadmin  
    Started by seqadmin, 04-11-2024, 12:08 PM
    0 responses
    60 views
    0 likes
    Last Post seqadmin  
    Started by seqadmin, 04-10-2024, 10:19 PM
    0 responses
    59 views
    0 likes
    Last Post seqadmin  
    Started by seqadmin, 04-10-2024, 09:21 AM
    0 responses
    54 views
    0 likes
    Last Post seqadmin  
    Working...
    X