Question: Phyloseq to DESeq2 for 16s amplicon sequencing data
gravatar for Bioinfonext
22 months ago by
Bioinfonext320 wrote:


I am trying to use DESeq2 for amplicon data analysis, but I am getting some problem.

Details of the analysis:

here is the summary of data, I do have three tissue: soil, root, and shoot sample collected from different villages.

and we have given three treatment to each soil, root and shoot sample in each village: TREATMENT T1, T2 AND T3.

Now, the aim is to find which microbial species or genus are more abundant in Treatment T1 and Treatment T3 in comparison to T2 and how T3 microbial abundance is different from T1 Treatment.

I tried to pass the phyloseq object into DESeq2

# Phyloseq to Deseq

psfdds<-phyloseqtodeseq2(ps0, ~1) # I just pass 1 as design here, because at later step I am going to pass again group as design.

#Group Tissue and Tremant variable into one varibale

psfdds$group <- factor(paste0(psfdds$Tissue,psfdds$Treatment))
#  group used as design again
design(psfdds) <- ~ group

    psfdds <- DESeq(psfdds, test="Wald", fitType="parametric")
    estimating size factors
    estimating dispersions
    gene-wise dispersion estimates
    mean-dispersion relationship
    final dispersion estimates
    fitting model and testing
    -- replacing outliers and refitting for 10886 genes
    -- DESeq argument 'minReplicatesForReplace' = 7
    -- original counts are preserved in counts(dds)
    estimating dispersions
    fitting model and testing

[1] "Intercept" "group_LeafT2_vs_LeafT1" "group_LeafT3_vs_LeafT1"
[4] "group_RootT1_vs_LeafT1" "group_RootT2_vs_LeafT1" "group_RootT3_vs_LeafT1"
[7] "group_SoilT1_vs_LeafT1" "group_SoilT2_vs_LeafT1" "group_SoilT3_vs_LeafT1"

# Now extracting result for soil in Treatment1_vs_Treatment2

 res_soil.T1_soil.T2_new <- results(psfdds, contrasts=c("group","TissueSoil.TreatmentT1","TissueSoil.TreatmentT2"))

# but when I look at summary of result of any comparison it give same value as shown below
out of 59297 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 58684, 99%
LFC < 0 (down) : 3, 0.0051%
outliers [1] : 0, 0%
low counts [2] : 3004, 5.1%
(mean count < 0)

But it seems like there is some problem as it is showing the same result after passing anything.

Could anyone please suggest how can I resolve it.

I will be very thankful to you.

bioconducter R • 1.5k views
ADD COMMENTlink modified 22 months ago • written 22 months ago by Bioinfonext320

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ADD REPLYlink written 22 months ago by h.mon32k
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