deseq2 nested multifactorial analysis
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Entering edit mode
jd • 0
@jd-23214
Last seen 9 weeks ago
Sweden

I would like to do a meta analysis where I subtract control from treatment, and then compare treatments:

I initially did these pairwise comparisons: Control A v Treatment A Control B v Treatment B Control C v Treatment C Control D v Treatment D

But I would like to do these two meta comparisons:

1. A (Control A - Treatment A) v B (Control B - Treatment B)
2. C (Control C - Treatment C) v D (Control D - Treatment D)

I have tried like this:

deseqFile1 <- "B3_v_B8.csv"
countData1 <- read.table(deseqFile1, header = T, sep = ",", row.names = 1)
sampleNames1 <- colnames(countData1)
sampleCondition1 <- c("Control_1","Control_1","Control_1","Treatment_1", "Treatment_1","Treatment_1", "Control_2", "Control_2", "Control_2", "Treatment_2", "Treatment_2", "Treatment_2")
sampleCondition2<- c("A","A","A","A","A","A","B","B","B","B","B","B")
colData1<- data.frame(condition=sampleCondition1, genotype=sampleCondition2)

colData1$condition<-as.factor(colData1$condition)
colData1$genotype<-as.factor(colData1$genotype)
row.names(colData1) = sampleNames1
all(rownames(colData1) == colnames(countData1))
dds1 <- DESeqDataSetFromMatrix(countData = countData1,
colData = colData1,
design = ~ condition+genotype)


But I get the following error:

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.

vignette('DESeq2')

Is there a way to do design the analysis without getting this error?

Thanks,

James


# include your problematic code here with any corresponding output
# please also include the results of running the following in an R session

sessionInfo( )
R version 4.2.2 (2022-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/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] stats4    grid      stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] EnhancedVolcano_1.16.0      KEGGREST_1.38.0             gage_2.48.0
[4] clusterProfiler_4.6.0       Cairo_1.6-0                 PCAtools_2.10.0
[7] ggrepel_0.9.3               devtools_2.4.5              usethis_2.1.6
[10] viridis_0.6.2               viridisLite_0.4.1           scales_1.2.1
[13] apeglm_1.20.0               DESeq2_1.38.3               SummarizedExperiment_1.28.0
[16] Biobase_2.58.0              MatrixGenerics_1.10.0       matrixStats_0.63.0
[19] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9         IRanges_2.32.0
[22] S4Vectors_0.36.1            BiocGenerics_0.44.0         party_1.3-11
[25] strucchange_1.5-3           sandwich_3.0-2              zoo_1.8-11
[28] modeltools_0.2-23           mvtnorm_1.1-3               randomForest_4.7-1.1
[31] knitr_1.42                  skimr_2.1.5                 forcats_1.0.0
[34] stringr_1.5.0               dplyr_1.1.0                 purrr_1.0.1
[40] ggplot2_3.4.1               tidyverse_1.3.2             wesanderson_0.3.6

DESeq2 multifactorial nested • 122 views
0
Entering edit mode
Basti ▴ 720
@7d45153c
Last seen 11 hours ago
France

The information on genotype is included in the condition : all Control_1 are A, all Control_2 are B The condition variable is sufficient to perform the contrasts you need