type") dds <- DESeq(dds) res_lfc <- lfcShrink(dds = dds, type = "ashr", coef = "type_Fx600_vs_Fx593") plotMA(res_lfc) as_tibble(res_lfc) %>% mutate(padj = case_when( is.na(padj) ~ 1, TRUE ~ padj )) %>% ggplot

Hi everyone, I am trying to carry out some analysis for an experiment which has three dimensions (treatment, genotype and time) and while I have managed to extract the pairwise comparisons I am interested in, I would like to go further and look at the genes which behave differently upon my drug treatment depending on either the genotype or the time factor (for a given genotype) - I believe that …

the column “stat” , which seems to be lost after the lfcshrink calculation using either apeglm or ashr. My question therefore is: Q1: How can I still apply the fdrtool in a correct way? Or am I doing something wrong? I do not think

for LFC shrinkage, the Normal prior from Love et al (2014). Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'. See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette

I have RNA seq data an experiment consisting of an untreated and treated samples in triplicates; a total of 6 samples. The culturing of the used tissue can be challenging so a batch effect is expected. I would like to apply apeglm shrinkage and use adjusted p-values as a cutoff to explain effects between conditions, but am encountering some problems. My questions are mainly related to the lfc sh…

I got this message every time: **Error: package or namespace load failed for ‘DESeq2’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): there is no package called ‘Biostrings’ In addition: Warning message: package ‘DESeq2’ was built under R version 4.1.1** ```r > sessionInfo() R version 4.1.0 (2021-05-18) Platform: x86_64-w64-mingw32/x64 (64-bit) Ru…

lt;- if(l2FCShrink){ as.data.frame(lfcShrink(dds, contrast=rn, type = "ashr")); } else { as.data.frame(results(dds, contrast=rn)); } results.df$log2FoldChange <- round(results.df$log2FoldChange, 2); results.df

We have RNAseq data with 4 cell lines with different knockout genotypes (WT, A, G, GA), and for each cell lines, we have two treatment types: treated (H) and untreated (C) with 5 replicates for each condition. we are interested in the treatment effect on each of these cell lines: WT.H_C, A.H_C, G.H_C, or GA.H_C (H_C means treatment vs untreat comparison). These are easily to be done in DESeq2 bas…

me 1618. This only happens if I use lfcShrink with "apeglm". It doesn't happen with "normal" or "ashr" or if I just use "results". Attached is an example of my code and also my ColData for dds1. Due to sequencing issues, I have some