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@dequattroconcetta-21510
Last seen 3 hours ago
Italy

Hi!

I am performing a differential expression analysis comparing cell lines responders to my treatment against cell lines not responders to the treatment (responders vs not resonders).

Here the sampleTable:

Name    Cell_line   condition
cell_line1_rep1 Cell_line_1 RESPONDERS
cell_line2_rep1 Cell_line_2 NOT_responders
cell_line3_rep1 Cell_line_3 NOT_responders
cell_line4_rep1 Cell_line_4 RESPONDERS
cell_line1_rep2 Cell_line_1 RESPONDERS
cell_line2_rep2 Cell_line_2 NOT_responders
cell_line3_rep2 Cell_line_3 NOT_responders
cell_line4_rep2 Cell_line_4 RESPONDERS
cell_line1_rep3 Cell_line_1 RESPONDERS
cell_line2_rep3 Cell_line_2 NOT_responders
cell_line3_rep3 Cell_line_3 NOT_responders
cell_line4_rep3 Cell_line_4 RESPONDERS


In my design I have two cell lines responders and two cell lines not responders. For each cell-line I have three biological replicates.

I construct the dds model considering as covariate both cell_line and condition but I got the error message that the variable are linear.

ddsTxi <- DESeqDataSetFromTximport(txi,
colData = samples,
design = ~ Cell_line + condition)
Error in checkFullRank(modelMatrix) :
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.



I was wondering how I can inform the DESeq function that my replicates come from different cell lines?

Concetta

DESeq2 • 79 views
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@mikelove
Last seen 1 day ago
United States

I'd recommend to collaborate with a statistician to pick an appropriate design, and to help with interpretation of results. We have information in the vignette section above, but beyond that, it would be a good idea to discuss with a statistician.

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swbarnes2 • 650
@swbarnes2-14086
Last seen 32 minutes ago

http://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#group-specific-condition-effects-individuals-nested-within-groups