40 results • Page 1 of 1
My dataset has a total of 53 samples (with outliers removed) of breast cancer patients with two conditions. I have read that DESeq2 doesn’t provide automatic shrinkage, and I have to use lfcShrink to shrink the log2FoldChanges. The manual states that apeGLM and ashr perform better than the normal option and that apeGLM is quite strict compared to the others and performs well with small...and I h…
control"),listValues = c(1/2,-1))) resLFC <- lfcShrink(dds, contrast=contrast, res=res, type="ashr") up <- res[which(resLFC$log2FoldChange > 0 & resLFC$padj < 0.01),] down <- res[which(resLFC$log2FoldChange < 0 & resLFC
updated 19 days ago • User000
Hi I have done a DESeq2 DGE analysis and it showed an error when I was using ashr shrinkage ``` using 'ashr' for LFC shrinkage. If used in published research, please cite: Stephens, M. (2016) False discovery rates...contrast = c(-1/12,-1/12,-1/12,-1/12,1,-1/12,-1/12,-1/12,-1/12,-1/12,-1/12,-1/12,-1/12,0), type = "ashr") ``` Edit: Thank you Michael for pointing towards the solution here: htt…
updated 7 weeks ago • Kent
Hi everyone, I'm currently getting some warnings when using lfcshrinkage with "ashr", regarding solve(). Example Code should be placed in three backticks as shown below ``` library(zebrafishRNASeq) data(zfGenes...c("group", "Trt","Ctl")) ashr_res<-lfcShrink(dds, contrast = c("group", "Trt","Ctl"),type="ashr") ``` This leads to the production of the corresponding output:…
updated 11 weeks ago • andrebolerbarros
based on Zhu *et al.*, 2018 but am curious as to what criteria others use to justify `apeglm` vs `ashr` for this sort of analysis. My current dataset is 160 samples, but I'm narrowing focus to 4M + 4F per experimental condition...I have an FDR < 0.05 for ~ 200 genes, but `apeglm` shrinks all my fold changes towards zero, and `ashr` gives LFCs between 1 and 28 for all 200 genes. Base mean…
updated 11 weeks ago • Allison
which is why I don't want to run the model for each coefficient separately. I also had a look at ashr, but testing against a threshold is not implemented there. Best, Frederik
updated 3 months ago • Frederik Ziebell
Edit": ```r ddsShrink_t <- lfcShrink(ddsObj2, contras=c("cell_group","DC3_TLR","DC2_TLR"), type="ashr") ``` It now worked with ashr. But is this correct
updated 3 months ago • LHA_trash
equivalent to: ``` res1 <- lfcShrink(dds, contrast=c("condition","treated1","control1"), type="ashr") res2 <- lfcShrink(dds, contrast=c("condition","treated2","control2"), type="ashr") ``` Given the statistical benefit on the DESeq2
updated 6 months ago • jeremymsimon
design = ~ sex+month+method+cat) I've used the default wald test and am applying ashr for LFCshrink which I think is the correct method, however I have previously used IHW for multiple testing correction...of using both please? Or what is used for multiple testing correction otherwise when running with ashr? Many thanks, B
updated 7 months ago • Bex
3 UP 843 > res_tableOE_shrunk <- lfcShrink(dds, contrast = contrast_oe, type = "ashr") using 'ashr' for LFC shrinkage. If used in published research, please cite: Stephens, M. (2016) False discovery rates: a new deal
updated 9 months ago • Deevanshu
RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10), glmGamPoi, BiocManager License: LGPL (>= 3) MD5sum
updated 9 months ago • ladypurrsia
Hi all, I am using DESeq2 v1.32 for RNAseq analysis. I am using the following code to apply a threshold/ FC cutoff of 1.25 in the wald stat. However, I am seeing some significant genes in res_sig have |LFC| < 1.25. From an old discussion thread in this forum, it seems to be normal if lfcShrink type = "normal". Is it okay in ashr also? Or am I supposed to add a post hoc threshold also? Wil…
Hi, Dr love. I post a question about [weird MAplot or volcano plot of DESeq2 diff result](https://support.bioconductor.org/p/9139060/) and also in [biostar](https://www.biostars.org/p/9484986/#9485059). @atpoint give a useful answer about too many 0 count genes and prefiltering. It seems that too many 0 count genes makes lfc shrink have a probelm. And I find the `apeglm` and `ashr` result i…
updated 12 months ago • Guandong Shang
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
updated 12 months ago • Guandong Shang
instead of p-adjusted values in results. Is there a way I can get the nominal P-values by using the ashr shrinkage estimator? I only see the s-values. If not, which P-values is it base on? How can I examine my test by P-value histogram
updated 13 months ago • haasroni
Hi, I'm using Deseq2 (v1.26.0) and ashr (v2.2-47) to shrink the log2FoldChange of the genes resulting from interaction term. DEseq2 returns the different shrunken...among Mut.T/UT vs WT.T/UT using the following **design = ~cell+trt+cell:trt** followed by ashr fold-change shrinkage. I get different results with ashr with reference levels compared to the ones without defining...for example**).…
updated 15 months ago • sofiagreen72211
I obtained negative svalues from the lfcShrink function of DESeq2 using the ashr shrinkage estimator. The values are very small and close to 0: the smallest is `-1.110223e-16`, the largest negative is `-1.199592e...I obtained negative svalues from the lfcShrink function of DESeq2 using the ashr shrinkage estimator. The values are very small and close to 0: the smallest is `-1.110223e-16`, t…
updated 17 months ago • Enrico
Risso et al. 2014) to estimate > variables that capture the unwanted variation. In addition, the ashr > developers have a specific method for accounting for unwanted > variation in combination with ashr (Gerard and Stephens
updated 21 months ago • igor
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
updated 21 months ago • helenhuang.math
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
updated 21 months ago • serdarT
did comparisons within each culture condition by grouping condition and genotype and using lfcshrink/ashr. For instance for genotype A: dds <- DESeqDataSetFromMatrix(countData = countdata, colData = coldata, design = ~ Experiment...2D contrast A_2D_v_WT_2D_lfcShrink <- lfcShrink(dds, contrast=c("group"…
updated 22 months ago • Nick F
Dear all, I analyzed RNA-seq data using DESeq2 extracting the results with or without shrinkage without shrinkage: res21 <- results(dds2, contrast=c("combined", "XX.DMSO", "XX.dTAG"), cooksCutoff=FALSE, alpha=0.05) with shrinkage: resLFC1 <- lfcShrink(dds2, contrast=c("combined", "XX.DMSO", "XX.dTAG"), alp…
updated 22 months ago • da.de
ashr" generally performs better than "normal" and that it preserves large LFC, but these LFC are unrealistically large with...ashr. I have 2 questions: 1. Is this expected behavior or does log2FC on this scale imply there is something fundamentally wrong...with my design or samples? 2. If this is expected behavior, should I go with "ashr" or "normal"? I am not using apeglm because I need the …
updated 2.3 years ago • Alex
I have created 12 pairs of DGE for my 3x2 design matrix with deseq2 and 10/12 shrinking by ashr works fine however,for 2 of the pairs, this message appears > Optimization failed to converge. Results may be unreliable...increasing maxiter and rerunning. I saw error in a thread here https://github.com/stephens999/ashr/issues/76 Does installing mixsqp to resolve this issue? Am I m…
updated 2.3 years ago • kavator
Hi, I have pretty simple but probably weird question. I have complex design with interaction terms in my model (see below), I run DESeq2_1.20.0 on rna-seq data. It seems that I can't change my contrasts to end up using only apeglm shrinkage and lfcThreshold, instead I'm stuck with using ashr shrinkage method without being able to incorporate lfcThreshold into calculation of null. What bothe…
updated 2.3 years ago • Iryna
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
updated 2.3 years ago • junli1988
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
updated 2.3 years ago • Paul
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…
updated 2.4 years ago • Paul
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
updated 2.5 years ago • nabiyogesh
1] ``` > resLFC_sh2 <- lfcShrink(dds, contrast=c("Treatment", "Sh2", "Control"), type="ashr") using 'ashr' for LFC shrinkage. If used in published research, please cite: Stephens, M. (2016) False discovery rates: a new deal
updated 2.9 years ago • MatthewP
the `lfcShrink` function, but no matter what type of shrinkage estimator I give (*normal/apeglm/ashr*) I get the same results. Also - I get NA values in the **padj** column. Could this have to do with the number of samples expressing
updated 3.1 years ago • ronif10
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
updated 3.3 years ago • David Eccles (gringer)
logical, should p-values and adjusted p-values be replaced with s-values when using apeglm or ashr. s-values provide the probability of false signs among the tests with equal or smaller s-value than a given given's s-value
updated 3.3 years ago • ATpoint
lfcShrink` does not say much on the topic, except that `contrast` and `coef` are ignored for `type="ashr"` when `res` is provided (and for `ashr` neither `contrast` nor `coef` are required). There is also the error message `one of coef or...reason why `contrast` or `coef` should be required if `res` is given (even for for types other than `ashr`), or am I missing something? In fact, if `res` is…
updated 3.3 years ago • tdanhorn
above for my own dataset. However, I am struggling to perform lfcShrink() using types "apeglm" and "ashr". Does any one have any suggestions on how to perform log fold change shrinkage on this design? I look forward to your replies
updated 3.5 years ago • hannepainter
Hello DESeqers, I have two groups of patients whose gene expression is expected to change differently over time. In one group, the change in gene expression is expected to be greater or at least different, but individual patients are also expected to have different baseline levels of gene expression as well. Therefore, I'm interested the interaction of time, and patient, and not in indi…
updated 3.7 years ago • jurbach
Hello everyone, I am currently working on RNA-Seq data using DESeq2. As it is in the manual, you can perform pre-filtering (e.g.: keep <- rowSums(counts(dds)) >= 10 dds <- dds[keep,] However, it's also said that: _"While it is not necessary to pre-filter low count genes before running the DESeq2 functions..."_. So, from what I gather, using this threshold (1…
updated 3.7 years ago • andrebolerbarros
Hello, I have a small dataset with 3 replicates per condition. One condition is a gene over-expression, the other is a control. I'm used to run DESeq2 with betaPrior = True to have a comparability with the 'old' DESeq2 behavior. I can't apply this old workflow because the expression differences between my conditions it quite small and one gene, namely the over-expressed ![](https://ibb.…
updated 4.0 years ago • mat.lesche
better just use non-shrunken log2foldchange for all? (I'm looking forward to `` apeglm ```` ``and `` ashr ``) Thank you
updated 4.1 years ago • Yuya Liang
setwd("C:/cygwin64/home/Coexpression_Nov2017") getwd() library(DESeq2) library(apeglm) library(ashr) expected_matrix <- read.table(file = "17samples_expected_count.txt", sep = "\t", header = TRUE) condition <- factor(c (rep("control
updated 4.7 years ago • lychen83
40 results • Page 1 of 1
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