User: Mike

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Mike10
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Posts by Mike

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[DESeq2] Results summary indicates LFC > and < 0 even with changed lfcThreshold
... results1 <- results( dds, contrast=c("combined","B_treated","B_untreated") ) results2 <- results( dds, contrast=c("combined","B_treated","B_untreated"), lfcThreshold=0.58496 ) summary(results1) summary(results2) Output. The results are different but they both say "LFC > 0 (up)" and "LFC ...
deseq2 lfcthreshold written 6 months ago by Mike10 • updated 6 months ago by Michael Love19k
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[DESeq2] Odd patterns in MA plot
... I have 4 RNA-seq samples A,B,C,D in triplicate. They're aligned with STAR and counted with summarizeOverlaps, following the Bioconductor RNA-seq workflow. I'm comparing A vs B and C vs D (other comparisons later but these for now). I'm getting different and odd patterns in the MA-plot depending on w ...
deseq2 ma-plot written 6 months ago by Mike10 • updated 6 months ago by Ryan C. Thompson6.8k
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Comment: C: DESeq2 experimental design, 2 controls and 2 treated
... I checked the distribution of pvalues and the results for 'contrast' don't look right. hist(results.A$pvalue[results.A$baseMean > 1], breaks = 0:20/20, col = "grey50", border = "white") hist(results.B$pvalue[results.B$baseMean > 1], breaks = 0:20/20, col = "grey50", border = "white") hist(re ...
written 7 months ago by Mike10
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Comment: C: DESeq2 experimental design, 2 controls and 2 treated
... Thanks I used the modified command and it worked. I get very few genes changed in the contrast which may be correct (PCA shows the samples are similar) but just want to ensure I'm doing this right: I first had to relevel treatment: se$treatment <- relevel(se$treatment, "untreated") Then analy ...
written 7 months ago by Mike10
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Comment: C: DESeq2 experimental design, 2 controls and 2 treated
... Thank you for the reply. When I use design of ~celltype:treatment dds <- DESeqDataSet(se, design = ~cell_type:treatment) I get a matrix is not full rank error. I looked at the "Model matrix not full rank" in the vignette, should I follow the instructions under "Group-specific condition effects ...
written 7 months ago by Mike10
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DESeq2 experimental design, 2 controls and 2 treated
... I have cell type A (untreated vs treated) and cell type B (untreated vs treated), 3 replicates for each: sample cell_type treatment combined 1 A untreated A_untreated 2 A untreated A_untreated 3 A untreated A_untreated ...
deseq2 written 8 months ago by Mike10 • updated 8 months ago by Michael Love19k
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Comment: C: Use interactions to get the difference in the effect of two treatments
... One control and two treatments, I think that's the same situation as in this post: https://support.bioconductor.org/p/104783/ ...
written 8 months ago by Mike10
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Comment: C: Will large differences in the number of reads between samples decrease reliabili
... Thanks that's good to know. >This looks like a reasonable, expected variation in total read counts. We sequenced on a NextSeq and expected the reads evenly distributed between the 8 samples and I was surprised by the variability, I expected some but not this much. I was planning to follow up wi ...
written 10 months ago by Mike10
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Will large differences in the number of reads between samples decrease reliability of DESeq2 analysis?
... I have some RNA-seq samples from mouse, 2 conditions with 4 replicates each, read quality is good and for each sample 85-90% of reads align. The number of aligned reads in millions are: Condition 1 replicate 1: 100 Condition 1 replicate 2: 79 Condition 1 replicate 3: 52 Condition 1 replicate 4: 37 ...
deseq2 written 10 months ago by Mike10 • updated 10 months ago by Ryan C. Thompson6.8k
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Comment: C: DESeq2 experimental design
... Thank you for your reply, I have some additional questions about the design formula. These are my 12 samples and let's assume I'm analyzing them all together: Sex Genotype BioRep Group female control 1 female.control female control 2 ...
written 11 months ago by Mike10

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