Tag: DESeq

Difference in number of DEGs from Deseq2 and limma-voom

Difference in number of DEGs from Deseq2 and limma-voom 0 Hello, I have RNA-seq data from two different treatment groups (F and NF ) at 2 different time points (T1 and T2). The mapping was done with STAR aligner and the quantification was done with FeatureCounts. I run differential expression…

Continue Reading Difference in number of DEGs from Deseq2 and limma-voom

Multiple disease condition vs Normal

DESeq2: Multiple disease condition vs Normal 0 Hello! I have an mRNA dataset with one cell type and 3 different conditions (Metastatic, Primary Tumor, and Solid Tissue Normal). I would like to compare the two diseased conditions with the normal. I am using the following code but getting the understated…

Continue Reading Multiple disease condition vs Normal

Multifactor analysis

Multifactor analysis 1 @66dd6104 Last seen 1 day ago United States HI every one I am new to RNA seq analyisis using R. I Am usig DESeq 2 for my analysis. I have four different Samples with three replicates each and I want to compare all of them with control…

Continue Reading Multifactor analysis

Running DESeq2 from rpy2 – Askdevz

If you open R and type: you will see that the assay function is not actually coming from DESeq2, but from its dependency which is called SummarizedExperiment: > assay standardGeneric for “assay” defined from package “SummarizedExperiment” function (x, i, withDimnames = TRUE, …) standardGeneric(“assay”) <bytecode: 0x5586a354db90> <environment: 0x5586a3535e20> Methods may…

Continue Reading Running DESeq2 from rpy2 – Askdevz

rna seq – R – [DESeq2] – How use TMM normalized counts (from EdgeR) in inputs for DESeq2?

I have several RNAseq samples, from different experimental conditions. After sequencing, and alignment to reference genome, I merged the raw counts to get a dataframe that looks like this: > df_merge T0 DJ21 DJ24 DJ29 DJ32 Rec2 Rec6 Rec9 G10 421 200 350 288 284 198 314 165 G1000 17208…

Continue Reading rna seq – R – [DESeq2] – How use TMM normalized counts (from EdgeR) in inputs for DESeq2?

DESeq2 and WGCNA

DESeq2 and WGCNA 0 I am currently performing an RNAseq analysis with a dataset from a GeneAtlas where I’ve identified DEGs from different comparisons. I want to now do a co-expression analysis with these comparisons and was wondering if anybody had suggestions of tutorials I could be directed to. I…

Continue Reading DESeq2 and WGCNA

Deseq2 Multifactor Design – Design Forum

Deseq2 multifactor design – In fact, deseq2 can analyze any possible experimental design that can be expressed with fixed effects terms (multiple factors, designs with interactions, designs with continuous variables, splines, and so on are all possible). We have searched different posts in different forums and i can’t figure out…

Continue Reading Deseq2 Multifactor Design – Design Forum

InteractiveComplexHeatmap on DESeq2 object with more than 2 groups

InteractiveComplexHeatmap on DESeq2 object with more than 2 groups 1 Hello all, I’m writing with the hope someone can clarify a doubt I have about the differential heatmap generated by the package InteractiveComplexHeatmap via the simple command interactivate(dds). I read the package documentation at bioconductor.org/packages/release/bioc/html/InteractiveComplexHeatmap.html, but I couldn’t find the…

Continue Reading InteractiveComplexHeatmap on DESeq2 object with more than 2 groups

tReasure: R-based GUI package analyzing tRNA expression profiles from small RNA sequencing data | BMC Bioinformatics

tReasure (tRNA Expression Analysis Software Utilizing R for Easy use) is a graphical user interface (GUI) tool for the analysis of tRNA expression profiles from deep-sequencing data of small RNAs (small RNA-seq) using R packages. The whole analysis workflow, including the uploading of FASTQ files of small RNA-seq, quantification of…

Continue Reading tReasure: R-based GUI package analyzing tRNA expression profiles from small RNA sequencing data | BMC Bioinformatics

DESeq2 how to specify contrast to test difference of differences

I am trying to take the “difference of differences” in contrasts from two factors (sex and group). We have male and female animals (sex factor) that were untrained or trained for 1, 2, 4, or 8 weeks (group factor, i.e., “control”, “1w”, “2w”, “4w”, “8w”). I want to know the…

Continue Reading DESeq2 how to specify contrast to test difference of differences

Error in SummarizedExperiment

I have installed DESeq2 version 1.36.0 samples <- colnames(txi$counts) group <- as.factor(c(“control”,”control”,”control”,”control”,”control”,”diet”,”diet”,”diet”,”diet”,”diet”, “control”,”control”,”control”,”control”,”control”,”diet”,”diet”,”diet”,”diet”,”diet”,”diet”)) coldata <- data.frame(samples, group, stringsAsFactors = F) coldata <- coldata[,c(“samples”,”group”)] coldata$samples <- factor(coldata$samples) coldata$group <- factor(coldata$group) rownames(coldata) <- sub(“fb”, “”, rownames(coldata)) all(rownames(coldata$samples) %in% colnames(txi)) all(rownames(coldata) == colnames(txi)) TRUE library(DESeq2) ddsTxi <- DESeqDataSetFromTximport(txi, colData = coldata, design =…

Continue Reading Error in SummarizedExperiment

Genes looking differential abundant are not accoring to DESeq2

Genes looking differential abundant are not accoring to DESeq2 0 I have a metagenomic dataset crossing three time points from which I have mined CAZymes and am using DESeq2 to identify differentially abundant CAZymes from using trimmed mean depth generated my CoverM (very similar to Q2Q3 contig coverage). From this…

Continue Reading Genes looking differential abundant are not accoring to DESeq2

Extremely different results for both EdgeR and DESeq2 analysis

Extremely different results for both EdgeR and DESeq2 analysis 1 @373f98d7 Last seen 23 hours ago Singapore Dear all, Upon comparing my results for the analysis between DESeq2 and EdgeR, I have realized that the 2 results obtained after DEG analysis are extremely different from each other. The thresholds I…

Continue Reading Extremely different results for both EdgeR and DESeq2 analysis

RNA interference triggers that target SARS-CoV-2 genome

Coronavirus disease 2019 (COVID-19) vaccines have played a critical role in reducing transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. However, with emerging reports of waning vaccine efficacy, there remains an urgent need to develop prophylactic measures against COVID-19. In a recent study published on the bioRxiv*…

Continue Reading RNA interference triggers that target SARS-CoV-2 genome

Can Differential Isoform expression analysis can be performed using DESeq2 package

Can Differential Isoform expression analysis can be performed using DESeq2 package 0 @03ddb485 Last seen 9 hours ago India Hello, I am want to perform differential isoform expression (DIE) analysis for RNAseq data from human. Can I use DESeq2 for this by inputting the transcript level abundance and getting differential…

Continue Reading Can Differential Isoform expression analysis can be performed using DESeq2 package

Counts from recount3 for DESeq2 analysis

Hi, As the topic title suggests, I want to use RSE objects from recount3 for differential expression analysis with DESeq2. This is an ongoing project that I took over. Looking at what has already been done, there are two points I wonder about : 1 – I found that the…

Continue Reading Counts from recount3 for DESeq2 analysis

deseq2 problem

deseq2 problem 0 Hi I am trying to draw a PCA plot with DESeq2 but somehow I cannot use DESeq2 functions. It is a really simple code i wil be pasting below. > transform <- DESeq2::rlog(eliminated_data, blind = TRUE) Error in (function (classes, fdef, mtable) : unable to find an…

Continue Reading deseq2 problem

scrnaseq – Normalization methods to combine scRNA-seq experiments with different sequencing depths

I don’t think you need to complicate the idea of normalisation by introducing machine learning classifiers as a necessary component. Normalisation is common when comparing different datasets for all differential analysis. If you have single cell data, have a look at integration techniques in the Seurat workflows: satijalab.org/seurat/articles/integration_rpca.html If you’ve…

Continue Reading scrnaseq – Normalization methods to combine scRNA-seq experiments with different sequencing depths

Condition shown as ‘Untreated vs. Treated’ in output of results() in DESeq2

Condition shown as ‘Untreated vs. Treated’ in output of results() in DESeq2 1 @bffcbc5f Last seen 16 hours ago United States of America I am trying to find differentially expressed genes using DESeq2 on some RNA-seq data. In the pheno data, there is a column named ‘condition’ with factored values…

Continue Reading Condition shown as ‘Untreated vs. Treated’ in output of results() in DESeq2

DESeq2 on allelic reads

DESeq2 on allelic reads 1 @ea088d93 Last seen 26 minutes ago Canada Hello, Can I use DESeq2 to perform differential gene expression on allelic reads? I have allelic reads quantified for each parental allele/copy I have 2 treatments (control vs ethanol-exposed) I want to perform differential gene expression to see…

Continue Reading DESeq2 on allelic reads

RNAseq data DEG analysis – DESeq2 normalized data

RNAseq data DEG analysis – DESeq2 normalized data 1 1) You can’t use because those data are already normalized and log-transformed. 3) RSEM expected_count is best to start off with for differential expression. Login before adding your answer. Traffic: 2089 users visited in the last hour Read more here: Source…

Continue Reading RNAseq data DEG analysis – DESeq2 normalized data

Separate exogenous from endogenous transcripts using Salmon RNAseq DTU

Dear friends, We are trying to use Salmon for DTU analysis. We want to separate exogenous from endogenous transcripts by following this post www.biostars.org/p/443701/ and this paper f1000research.com/articles/7-952 We are focusing on a gene called ASCL1 (endo-ASCL1). We transduced cells with lentiviral vector containing ASCL1 ORF only (Lenti-ASCL1). There should…

Continue Reading Separate exogenous from endogenous transcripts using Salmon RNAseq DTU

DESeq2 comparisons with multiple experimental variables?

Summary: I’m trying to get a series of pairwise comparisons of specific experimental variables while holding the other variables constant, and can’t figure out how to do it in DESeq2 I am running an circadian experiment with multiple disease states and experimental interventions, let’s say condition: high fat (HFC), normal…

Continue Reading DESeq2 comparisons with multiple experimental variables?

GDCquery_Maf error

GDCquery_Maf error 0 @76e1237b Last seen 1 day ago Singapore Hi all, I really need some help. I am trying to run GDCquery_Maf which worked fine until yesterday. Now I get the following error: Error in GDCquery(paste0(“TCGA-“, tumor), data.category = “Simple Nucleotide Variation”, : Please set a valid workflow.type argument…

Continue Reading GDCquery_Maf error

Subsetting DESEQ2 VST Microbiome Data

Subsetting DESEQ2 VST Microbiome Data 1 @f8d5630d Last seen 16 hours ago Germany Hello there, the question is a bit off topic I am currently using DESEQ2 to normalize 16S microbiome data as advised several times in the recent literature. Currently I am facing the problem that I have 16S…

Continue Reading Subsetting DESEQ2 VST Microbiome Data

A comparison of transcriptome analysis methods with reference genome

Background: The application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field. Methods: In our study, six popular analytical procedures/pipeline were compared using four RNA-seq…

Continue Reading A comparison of transcriptome analysis methods with reference genome

How to reduce the impact of one varaible in Deseq2 or edgeR for multivariate value analysis?

Hello, everyone, I’m recently meeting this problem with my analysis, which i’ve done a lots of research and asked people around but their answers are quite confusing, so if I can get more opinions, that’d be terrific and thanks at advance. So I’m doing an analysis of DEGs using Deseq2…

Continue Reading How to reduce the impact of one varaible in Deseq2 or edgeR for multivariate value analysis?

Identification of potentially functional circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as prognostic factors of hepatocellular carcinoma by integrated bioinformatics analysis

Rawla, P., Sunkara, T., Muralidharan, P. & Raj, J. P. Update in global trends and aetiology of hepatocellular carcinoma. Contemp. Oncol. (Poznan, Poland) 22, 141–150 (2018). CAS  Google Scholar  Kong, D. et al. Current statuses of molecular targeted and immune checkpoint therapies in hepatocellular carcinoma. Am. J. Cancer Res. 10,…

Continue Reading Identification of potentially functional circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as prognostic factors of hepatocellular carcinoma by integrated bioinformatics analysis

How to pool phyloseq data?

How to pool phyloseq data? 0 @688ee615 Last seen 1 day ago United Kingdom I hope someone can help. I am trying to carry out some differential abundance analysis on some microbiome data that has come from a metabarcoding experiment using 16S illumina sequencing. I have processed my data using…

Continue Reading How to pool phyloseq data?

Error on Rstudio cloud when installing DESeq2 package : rprogramming

Hi! I am using the free account on R studio cloud. And I am getting this error: * installing *source* package ‘DESeq2’ … ** using staged installation ** libs g++ -std=gnu++14 -I”/opt/R/4.1.2/lib/R/include” -DNDEBUG -I’/cloud/lib/x86_64-pc-linux-gnu-library/4.1/Rcpp/include’ -I’/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppArmadillo/include’ -I/usr/local/include -fpic -g -O2 -c DESeq2.cpp -o DESeq2.o g++: fatal error: Killed signal terminated program…

Continue Reading Error on Rstudio cloud when installing DESeq2 package : rprogramming

A genome-scale screen for synthetic drivers of T cell proliferation

Abramson, J. S. et al. Transcend NHL 001: immunotherapy with the CD19-directed CAR T-cell product JCAR017 results in high complete response rates in relapsed or refractory B-cell non-Hodgkin lymphoma. Blood 128, 4192–4192 (2016). Google Scholar  Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators…

Continue Reading A genome-scale screen for synthetic drivers of T cell proliferation

r DESeq2 how to add coldata code example

Example: deseq2 output explained Column Description 1 Gene Identifiers 2 mean normalised counts, averaged over all samples from both conditions 3 the logarithm (to basis 2) of the fold change (See the note in inputs section) 4 standard error estimate for the log2 fold change estimate 5 Wald statistic 6…

Continue Reading r DESeq2 how to add coldata code example

Pathway analysis of RNAseq data using goseq package

Hello, I have finished the RNA seq analysis and I am trying to perform some pathway analysis. I have used the gage package and I was looking online about another package called goseq that takes into account length bias. However, when I run the code I get an error. How…

Continue Reading Pathway analysis of RNAseq data using goseq package

Loop through columns to generate PCA from DESeq2 data

I’d like to generate a PCA of my bulk RNAseq data, coloured by each of my variables in the DESeq2 object “vsd”. My current code looks like this (to generate a single plot): pcaData <- plotPCA(vsd, intgroup=c(“Age”, “BlastRate”), returnData=TRUE) percentVar <- round(100 * attr(pcaData, “percentVar”)) ggplot(pcaData, aes(PC1, PC2, color=Age, shape=BlastRate))…

Continue Reading Loop through columns to generate PCA from DESeq2 data

DESeq2 pseudotime series design?

DESeq2 pseudotime series design? 1 @jordiplanells-19865 Last seen 22 hours ago Sweden Hi all. First things first, sorry for posting one more question about experimental design and time series in DESeq2.We have performed RNA-seq with two different treatments (control and protein over-expression) in two different time points (t=0 and t=8h)….

Continue Reading DESeq2 pseudotime series design?

DESeq2 and high prefiltering cutoff

DESeq2 and high prefiltering cutoff 1 @255004b1 Last seen 3 hours ago United States Hi, I am curious about prefiltering with DESeq2. I understand from this site and reading the DESeq2 vignette that prefiletering is really unnecessary as DESeq2 has a stringent filtering that it does. However, I’m seeing better…

Continue Reading DESeq2 and high prefiltering cutoff

Interactive Shiny App for Bulk Sequencing Data

calculate_condition_mean_sd_per_gene Calculate statistics for each gene of an expression matrix given a grouping crossPanel Generate the cross plot panel of the shiny app crossPanelServer Generate the cross plot panel of the shiny app crossPanelUI Generate the cross plot panel of the shiny app cross_plot Create a cross plot comparing differential…

Continue Reading Interactive Shiny App for Bulk Sequencing Data

rna seq – How does DESeq2 “collapseReplicates()” function work on read counts data?

Comparing read counts from an RNA-seq experiment for two select genes before and after using DESeq2’s collapseReplicates() and plotCounts() functions yields interesting results: Before collapseReplicates() and plotCounts(): Geneid foo1.1 foo1.2 foo2.1 foo2.2 bar1.1 bar1.2 bar2.1 bar2.2 baz1.1 baz1.2 baz2.1 baz2.2 baz3.1 baz3.2 WASH7P 6 5 0 2 1 1 8…

Continue Reading rna seq – How does DESeq2 “collapseReplicates()” function work on read counts data?

No such file or directory)

I have R(version 4.1.2) installed on Fedora35. When I tried to install “DESeq2”, I got the following warning. 1: .inet_warning(msg) : installation of package ‘genefilter’ had non-zero exit status 2: .inet_warning(msg) : installation of package ‘locfit’ had non-zero exit status 3: .inet_warning(msg) : installation of package ‘DESeq2’ had non-zero exit…

Continue Reading No such file or directory)

nf-core/circrna

circRNA quantification, differential expression analysis and miRNA target prediction of RNA-Seq data Introduction nf-core/circrna is a best-practice analysis pipeline for the quantification, miRNA target prediction and differential expression analysis of circular RNAs in paired-end RNA sequencing data. The pipeline is built using Nextflow, a workflow tool to run tasks across…

Continue Reading nf-core/circrna

Butterfly eyespots evolved via cooption of an ancestral gene-regulatory network that also patterns antennae, legs, and wings

Although the hypothesis of gene-regulatory network (GRN) cooption is a plausible model to explain the origin of morphological novelties (1), there has been limited empirical evidence to show that this mechanism led to the origin of any novel trait. Several hypotheses have been proposed for the origin of butterfly eyespots,…

Continue Reading Butterfly eyespots evolved via cooption of an ancestral gene-regulatory network that also patterns antennae, legs, and wings

Fatty infiltration after rotator cuff tear

Introduction Rotator cuff tear (RCT) is a common shoulder disorder causing shoulder pain and disability. The prevalence of full-thickness RCT is 20.7% in the general population, and increased with age.1 Rotator cuff play essential roles in shoulder function and the treatment of proximal humeral fractures.2,3 It is important to repair…

Continue Reading Fatty infiltration after rotator cuff tear

use tcgabiolinks package to download TCGA data

TCGA Data download in terms of ease of use ,RTCGA The bag should be better , And because it’s already downloaded data , The use is relatively stable . But also because of the downloaded data , There is no guarantee that the data is new .TCGAbiolinks The package is…

Continue Reading use tcgabiolinks package to download TCGA data

RNASeq deseq2

RNASeq deseq2 1 Hi friends I have RNASeq data fromTCGA as HT-seq format. I want to do Deseq2. some patient names are duplicated and deseq2 dose not accept them. How would I deal with the duplicated patients? deseq2 RNASeq • 229 views • link 1 day ago by Rob &utrif;…

Continue Reading RNASeq deseq2

[BioC] DESeq2, plotting residuals vs.fitted values

On the scale of the counts, the fitted values are: assays(dds)[[“mu”]] to get the fitted values on the common scale, you just need to divideeach column by the size factor: fitted.common.scale = t( t( assays(dds)[[“mu”]] ) / sizeFactors(dds) ) So then the residuals are counts(dds, normalized=TRUE) – fitted.common.scale Dear All,I…

Continue Reading [BioC] DESeq2, plotting residuals vs.fitted values

RNA-Seq HTseq galaxy DE analysis

RNA-Seq HTseq galaxy DE analysis 0 Hi friends I have htseq data from TCGA. it contains patients name in first row and genes in first column : 200 columns and 20000 rows. I dont want deseq2 in R. this needs to be done in galaxy. my question is how to…

Continue Reading RNA-Seq HTseq galaxy DE analysis

Bioconductor – DaMiRseq (development version)

DOI: 10.18129/B9.bioc.DaMiRseq     This is the development version of DaMiRseq; for the stable release version, see DaMiRseq. Data Mining for RNA-seq data: normalization, feature selection and classification Bioconductor version: Development (3.15) The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them…

Continue Reading Bioconductor – DaMiRseq (development version)

How to retrieve the batch corrected data frame when using Deseq in R?

How to retrieve the batch corrected data frame when using Deseq in R? 1 I have several different RNAseq dataframes that I have merged together; they are from different studies and are raw counts. I want to correct the merged dataframe for study batch effects without getting negative values (I…

Continue Reading How to retrieve the batch corrected data frame when using Deseq in R?

Profiling and functional characterization of maternal mRNA translation during mouse maternal-to-zygotic transition

INTRODUCTION Mammalian life starts with the fusion of two terminally differentiated gametes, sperm and oocyte, resulting in a totipotent zygote. After going through preimplantation development, the zygote reaches blastocyst before implantation. The two most important events taking place during preimplantation development are zygotic genome activation (ZGA) and the first cell…

Continue Reading Profiling and functional characterization of maternal mRNA translation during mouse maternal-to-zygotic transition

Multi-factor paired RNAseq differential analysis with DEseq2

Hello, I’m working with an RNAseq dataset that looks at plants that are either infected with a fungus or have been left uninfected. I have both male and female genotypes, and those have been cloned, with one clone of each genotype getting inoculated while the other clone serves as the…

Continue Reading Multi-factor paired RNAseq differential analysis with DEseq2

DESeq2 contrast multiple treated conditions versus multiple control conditions

I have 4 treated and 2 control samples each 3 reps. I would like to contrast treated 1,2,3 against 2 controls, and treated 4 against 2 controls. (condition <-factor(c(“treated1″,”treated1″,”treated2″,”treated2″,”treated3″,”treated3″,”treated4″,”treated4″,”control1″,”control1″,”control2″,”control2”))) (coldata <- data.frame(row.names=colnames(txi.g), condition)) dds <- DESeqDataSetFromTximport(txi.g, colData=coldata, design=~condition) dds <- DESeq(dds) res <- DESeq2::results(dds, contrast = list(c(“treated1″,”treated2″,”treated3”),c(“control1″,”control2”)), listValues = c(3,-3/5)…

Continue Reading DESeq2 contrast multiple treated conditions versus multiple control conditions

Bioinformatics Scientist Job Opening in Seattle, WA at Alpine Immune Sciences

Job Posting for Bioinformatics Scientist at Alpine Immune Sciences Alpine Immune Sciences is applying our platform discovery technology to bring innovative new therapies to people living with serious or life-threatening illnesses or conditions, such as cancer and autoimmune/inflammatory diseases. Exciting challenges lie ahead—guided by our core values, we’ll…

Continue Reading Bioinformatics Scientist Job Opening in Seattle, WA at Alpine Immune Sciences

Test if a gene is NOT differentially expressed in DESeq2?

Test if a gene is NOT differentially expressed in DESeq2? 1 @pennylane-21859 Last seen 1 day ago United States First, my apologies if this has been covered already. I thought for sure it would have been, but I can’t find the relevant info with my searches. It’s pretty obvious from…

Continue Reading Test if a gene is NOT differentially expressed in DESeq2?

Large DE LogFC range

Large DE LogFC range 1 @3d20f23f Last seen 1 hour ago Italy I’m working with DESeq2 to make a DE analysis between samples in two different conditions. During the analysis, I identified a batch effect due to the sequencing time modelled as a covariate in the design formula. From the…

Continue Reading Large DE LogFC range

Which method works best for analysing ONE sample of scRNA-seq data?

Which method works best for analysing ONE sample of scRNA-seq data? 1 Hello, I currently have a single-cell RNA-seq (scRNA-seq) data of a single person (sample) and i want to perform DE analysis. However, when I run the DESeq() in the DESeq2 package, it shows an error about only one…

Continue Reading Which method works best for analysing ONE sample of scRNA-seq data?

DE analysis model matrix for paired samples

DE analysis model matrix for paired samples 1 @tkapell-14647 Last seen 2 hours ago Helmholtz Center Munich, Germany Hi, I am analyzing a NanoString dataset where the metadata look as below: The factor of interest is the “group” and both groups are found in each mouse (“mouseID”) (paired samples). I…

Continue Reading DE analysis model matrix for paired samples

normalization for unsupervised analysis by DESeq2

normalization for unsupervised analysis by DESeq2 1 Hi friends I want to use DESeq2 to normalize the raw count data to do PCA. I dont have colData. What code should I use? because in the DESeq2 workflow we need colData and design. thanks RNA-Seq • 216 views • link updated…

Continue Reading normalization for unsupervised analysis by DESeq2

rna seq – How does DESeq2 “collapseReplicates” work on read counts data?

Comparing read counts from an RNA-seq experiment for a couple select genes before and after using DESeq2’s collapseReplicates function yields interesting results: Before: Geneid foo1.1 foo1.2 foo2.1 foo2.2 foo3.1 foo3.2 bar1.1 bar1.2 bar2.1 bar2.2 bar3.1 bar3.2 baz1.1 baz1.2 baz2.1 baz2.2 baz3.1 baz3.2 WASH7P 6 5 0 2 7 3 1…

Continue Reading rna seq – How does DESeq2 “collapseReplicates” work on read counts data?

DESeq2 input from GDAC firehose

Hi guys, I hope you are fine. I’m not good in English so if you couldn’t understand my question, please feel free to reply. I’m a beginner of bioinformatics. I want to practice differential expressed gene (DEG) analysis in R. The RNA seq data I used was downloaded from broad…

Continue Reading DESeq2 input from GDAC firehose

Strangely too low P-value and Adjusted P-value(FDR) DESeq2 and edgeR

My data is experimental data that has been overexpressed for a specific gene. Data samples are divided into 3 groups according to the over-expression time and each group has 3 samples. (total 9 samples) I conducted DGE analysis on the control group and one case group with DESeq2. cts <-…

Continue Reading Strangely too low P-value and Adjusted P-value(FDR) DESeq2 and edgeR

How does the DESeq2 work?

This is not DESeq2 specific but rather deals with modeling in general when comparing two groups. Whether you’re using DESeq2 or a t-test or some other linear model, you have to specify exactly what it is that you’re trying to model. If you assign some patients to a “treatment” group…

Continue Reading How does the DESeq2 work?

downloading RNA seq data

downloading RNA seq data 0 Hi friends I am using the following code to get the data from TCGA. I want to have only one allocate of each person then I will have unique patients ID. Is there any line of code that I should add to this to get…

Continue Reading downloading RNA seq data

Heatmap deseq2

I’m using deseq2 for DEA but when I create a heatmap with only DEGs, it looks very strange: I’m not sure whether there are only overexpressed genes or whether the dataset is not normalized properly. I probably made a mistake somewhere in my coding but I don’t know where to…

Continue Reading Heatmap deseq2

DESeq2 analysis for targeted RNA-seq

Dear all, I am attempting to perform DESeq2 analysis (using the Geneious plugin) for targeted RNA-seq of several hundred genes. Only the target set of genes is sequenced, as the primer that I use for first strand cDNA synthesis is specific to the target set of genes. I have three…

Continue Reading DESeq2 analysis for targeted RNA-seq

Immune-related Prognostic Genes of ccRCC

Introduction Kidney cancer is one of the most commonly diagnosed tumors around the globe.1 According to the statistics from the World Health Organization, annually, there are more than 140,000 RCC-related deaths.2 ccRCC is the most typical subtype of kidney cancer and contributes to the majority of kidney cancer-related deaths.3,4 Until…

Continue Reading Immune-related Prognostic Genes of ccRCC

Bioconductor – derfinder (development version)

DOI: 10.18129/B9.bioc.derfinder     This is the development version of derfinder; for the stable release version, see derfinder. Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Bioconductor version: Development (3.15) This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two…

Continue Reading Bioconductor – derfinder (development version)

RNAseq using galaxy

RNAseq using galaxy 0 @e3a40d42 Last seen 1 hour ago United States I am new to rnaseq, I am using RNA star to map the genes and count reads per gene. After running the feature counts on RNA Star output, I will like to proceed to the deseq2 for differential…

Continue Reading RNAseq using galaxy

Bioconductor – TBSignatureProfiler (development version)

DOI: 10.18129/B9.bioc.TBSignatureProfiler     This is the development version of TBSignatureProfiler; for the stable release version, see TBSignatureProfiler. Profile RNA-Seq Data Using TB Pathway Signatures Bioconductor version: Development (3.15) Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates…

Continue Reading Bioconductor – TBSignatureProfiler (development version)

Comparative de novo transcriptome analysis identifies salinity stress responsive genes and metabolic pathways in sugarcane and its wild relative Erianthus arundinaceus [Retzius] Jeswiet

1. Singh, A. et al. Phytochemical profile of sugarcane and its potential health aspects. Pharmacogn. Rev. 9, 45–54 (2015). CAS  PubMed  PubMed Central  Google Scholar  2. Eggleston, G. Positive aspects of cane sugar and sugar cane derived products in food and nutrition. J. Agric. Food Chem. 66, 4007–4012 (2018). CAS …

Continue Reading Comparative de novo transcriptome analysis identifies salinity stress responsive genes and metabolic pathways in sugarcane and its wild relative Erianthus arundinaceus [Retzius] Jeswiet

Using DESeq2 to analyse multi-variate design resulting in testing the wrong parameter

Enter the body of text here Hi, I am analysing a RNA Seq dataset coming from 3 independent cell isolates (isolate1, isolate2, isolate3), each given 3 different treatments (control, drug1, drug2). We are testing drug 1 against control in the first instance: We also observed that there is some variation…

Continue Reading Using DESeq2 to analyse multi-variate design resulting in testing the wrong parameter

sigh of log2FC values in DESeq2

sigh of log2FC values in DESeq2 0 @194b0276 Last seen 10 hours ago United States Can someone explain to me how is the sign of log2FoldChange is set in the results of DEseq2? I was pretty sure that it is calculated as log2(Counts_treatment/Count_reference), where reference is determined alphabetically (unless specified…

Continue Reading sigh of log2FC values in DESeq2

featureCounts output has letters and +/- sign

featureCounts output has letters and +/- sign 1 Hello, I have created a featureCounts table and 10 of my files had weird outputs. Some values were my organism name and others were a “+” or “-“. I could not find anything about this in the manual. I tried to re-run…

Continue Reading featureCounts output has letters and +/- sign

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data | BMC Bioinformatics

1. Van den Berge K, Hembach KM, Soneson C, Tiberi S, Clement L, Love MI, Patro R, Robinson MD. RNA sequencing data: Hitchhikers guide to expression analysis. Annu Rev Biomed Data Sci. 2019;2(1):139–73. doi.org/10.1146/annurev-biodatasci-072018-021255. Article  Google Scholar  2. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A,…

Continue Reading GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data | BMC Bioinformatics

weighted regression – Deriving initial weights for IRLS in DESeq2’s GLM model

In the paper introducing DESeq2 they say: As the GLM’s link function is $g(mu) = log (mu)$ and its variance function is $V( mu; alpha) = mu + alpha mu^2$, the elements of the diagonal matrix $W_i$ are given by $$w_{jj} = frac{1}{g^{prime}(mu_j)^2 V(mu_j)} = frac{1}{frac{1}{mu_j}+alpha}$$ While I am not…

Continue Reading weighted regression – Deriving initial weights for IRLS in DESeq2’s GLM model

Unlog transformed data in DESeq2

Unlog transformed data in DESeq2 1 @mohammedtoufiq91-17679 Last seen 13 hours ago Qatar Hi, I am using DESeq2 for analyzing Illumina RNASeq datasets. I follow the below steps; Derived raw counts (from featureCounts) > Imported counts to DESeq2 Normalised the counts via an estimation of size factors (counts(dds, normalized =…

Continue Reading Unlog transformed data in DESeq2

Analyzing RNA-seq data with DESeq2 Tutorial

I am working through the Analyzing RNA-seq data with DESeq2 tutorial, and running it through RMarkdown. Loading the tximeta package in the tutorial code below: coldata <- samples coldata$files <- files coldata$names <- coldata$run library(“tximeta”) # se <- tximeta(coldata) # ddsTxi <- DESeqDataSet(se, design = ~ condition) Is causing the…

Continue Reading Analyzing RNA-seq data with DESeq2 Tutorial

DESeq2 comparisons using contrast

DESeq2 comparisons using contrast 0 Hi all, I recently started analyzing some bacterial RNAseq data. So, I have 4 different strains and for each strain I have 2 conditions. Let’s say strains are A, B, C and D while conditions are X and Y. I have a total of 24…

Continue Reading DESeq2 comparisons using contrast

No differentially expressed genes

Hi, I have a dataset now with 36k lncRNAs and I’m using DESeq2 to find differentially expressed lncRNAs between a healthy group and a disease group, but unfortunately I cannot find any DE lncRNAs with low padj values. However, when I explore my data by taking the log2 fold change…

Continue Reading No differentially expressed genes

batch, condition and tissue information

Hi all, I have the following type of data: 5 batches (5 different data sets) 2 conditions (wt, mutant) 3 different tissues (data 1 and 2 represent tissue 1, data 3 and 4 represent tissue 2, data 5 represents tissue 3) I would like to find DEGs in tissue 1…

Continue Reading batch, condition and tissue information

dispersion of a gene, what does it mean?

dispersion of a gene, what does it mean? 2 In all RNA-seq analysis applications they talk about the dispersion of a gene. As far as I understood, it is not a variance of the normalized counts for a given gene. It is somehow much more complicated. DESeq defines the dispersion…

Continue Reading dispersion of a gene, what does it mean?

get rRNA FASTA file for a particular bacteria

get rRNA FASTA file for a particular bacteria 0 Hey all, I was trying to find a way to get all rRNA (5S, 16S and 23S) FASTA sequences for a particular bacteria (B. thetaiotaomicron VPI-5482, which is the type strain). I wanted this file so that I could use something…

Continue Reading get rRNA FASTA file for a particular bacteria

Virtual environment in R? – Stackify

I’m going to use the comment posted by @cboettig in order to resolve this question. Packrat Packrat is a dependency management system for R. Gives you three important advantages (all of them focused in your portability needs) Isolated : Installing a new or updated package for one project won’t break…

Continue Reading Virtual environment in R? – Stackify

Diffbind3 dba.plotMA error

Hello, I am analyzing some ATAC-seq from flies using Diffbind3.0.8 and EdgeR. I initially ran dba.analyze() with the default peak size of 401 and was able to graph the results using dba.plotMA and dba.plotVolcano when my contrasts were evaluated using both EdgeR and DESEQ2. After resizing the peaks to 100…

Continue Reading Diffbind3 dba.plotMA error

Design formula in DESeq2

Hello, I am using DESeq2 for analysis of RNAseq data. I would like to ask you about the design in the DESEq2 formula. I have tissue from animals treated with a chemical and my animal model is a colorectal cancer model. My variables are gender (male or female), treatment (treated…

Continue Reading Design formula in DESeq2

How to get normalized count table from DESeq?

How to get normalized count table from DESeq? 1 Hi, I’m using Deseq compare differential abundance. Here is my code: ds.all <- phyloseq_to_deseq2(ps0.infant.pbs, ~ sample_type) geoMeans <- apply(counts(ds.all),1,gm_mean) ds.all <- estimateSizeFactors(ds.all,geoMeans = geoMeans) dds.all <- DESeq(ds.all,fitType = “local”) Then as the results I got 8 ASVs that showed significantly different….

Continue Reading How to get normalized count table from DESeq?

Assigning Object to Function Rather than vice-versa?

R Syntax Clarification in DeSeq2 Vignette: Assigning Object to Function Rather than vice-versa? 1 Hi, I have a question about some of the R syntax present in the DeSeq2 vignette. The following comes directly from the section regarding the removal of batch effects: mat <- assay(vsd) mm <- model.matrix(~condition, colData(vsd))…

Continue Reading Assigning Object to Function Rather than vice-versa?

Combining Microarray and RNA-Seq datasets visualization and comparison

Combining Microarray and RNA-Seq datasets visualization and comparison 0 Hi, I am working with some public transcriptomics datasets (both Microarray and RNA-Seq) to study gene signatures of bacterial “Infected” samples vs. Healthy Control samples. The starting point of analysis of Microarray data is .CEL files for Affymetrix (normalize using GCRMA)…

Continue Reading Combining Microarray and RNA-Seq datasets visualization and comparison

different results between DESeq model with multiple groups or with specific groups

different results between DESeq model with multiple groups or with specific groups 1 @b295d7f1 Last seen 3 hours ago Italy Hi! I am working with DESeq2 to perform a differential expression analysis between different treatments.I have 4 conditions and 4 four biological replicates for each conditions. I’ve performed differential expression…

Continue Reading different results between DESeq model with multiple groups or with specific groups

highlighting specific genes (from a user-supplied list) in a Volcano plot in R

highlighting specific genes (from a user-supplied list) in a Volcano plot in R 1 I’ve generated a volcano plot using DeSeq2 results and would like to specifically highlight a subset of genes by providing a list of gene IDs Dataset$condition <- relevel(Dataset$condition, “Ctrl”) res <- lfcShrink(DatasetProcessed, contrast=c(“condition”,”Treat”,”Ctrl”)) with(res, plot(log2FoldChange, -log10(pvalue),…

Continue Reading highlighting specific genes (from a user-supplied list) in a Volcano plot in R

deseq2 machine sizing best practices for very large data set

deseq2 machine sizing best practices for very large data set 0 @aa611017 Last seen 8 hours ago United States I want to perform differential expression analysis on a data set containing 17,000 samples. The salmon quant.sf files are about 1.5 Tb. based on my naive understanding of R and R…

Continue Reading deseq2 machine sizing best practices for very large data set

Bioconductor – rgsepd

    This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see rgsepd. Gene Set Enrichment / Projection Displays Bioconductor version: 3.4 R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at RefSeq IDs) by automating differential…

Continue Reading Bioconductor – rgsepd

TCGA dataset normalization

TCGA dataset normalization 0 hi. i am new to machine learning. i want to normalize my data which I downloaded from UCSC Xena browser for pancreatic cancer TCGA PAAD is its id. when I try to run this code it is showing error given below library( “DESeq2” ) library(ggplot2) countData…

Continue Reading TCGA dataset normalization

Transposition and duplication of MADS-domain transcription factor genes in annual and perennial Arabis species modulates flowering

Annual and perennial species occur in many plant families. Annual plants and some perennials are monocarpic (flowering once in their life cycle), characterized by a massive flowering and typically produce many seeds before the whole plant senesces. By contrast, most perennials live for many years, show delayed reproduction, and are…

Continue Reading Transposition and duplication of MADS-domain transcription factor genes in annual and perennial Arabis species modulates flowering

Understanding output of Negative Binomial in DESeq2

Enter the body of text here Dear author and seniors, Hope you all are doing great. I am very new to RNASeq and DESeq2. I know that the negative Binomial (Gamma Poisson) is used to fit this RNAseq count data. All genes are assessed/fitted between two conditions and we get…

Continue Reading Understanding output of Negative Binomial in DESeq2

DEseq2 in R studio Cloud – RStudio Cloud

Dear all, I am investigating the possibility of using RStudio Cloud to teach RNA sequencing technologies using the DESeq2 package. Bioconductor DESeq2 Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. My attemps to compile…

Continue Reading DEseq2 in R studio Cloud – RStudio Cloud

Aro Biotherapeutics hiring Investigator, Genetics & Bioinformatics in Philadelphia, Pennsylvania, United States

About Aro BioTx Join the team at Aro Biotherapeutics creating breakthrough biotherapeutics based on Centyrin oligonucleotide conjugates. Centyrins are small protein domains based on the fibronectin domains of human Tenascin C that combine the affinity and specificity properties of antibodies with the stability and tissue penetration properties of small molecules….

Continue Reading Aro Biotherapeutics hiring Investigator, Genetics & Bioinformatics in Philadelphia, Pennsylvania, United States

Bioconductor – ERSSA

DOI: 10.18129/B9.bioc.ERSSA     This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see ERSSA. Empirical RNA-seq Sample Size Analysis Bioconductor version: 3.9 The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate…

Continue Reading Bioconductor – ERSSA

Bioconductor – NBAMSeq

DOI: 10.18129/B9.bioc.NBAMSeq     This package is for version 3.9 of Bioconductor; for the stable, up-to-date release version, see NBAMSeq. Negative Binomial Additive Model for RNA-Seq Data Bioconductor version: 3.9 High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step…

Continue Reading Bioconductor – NBAMSeq

Homer finds same peak multiple times

I am using Homer to identify peaks in RNA-seq data and then determine differential expression by counting reads per peak. Homer has a lovely package that does just this: getDifferentialPeaksReplicates.pl. The issue is that for some reason Homer returns the same peak multiple times in its final output (Bonus question:…

Continue Reading Homer finds same peak multiple times

Understanding the output of Negative Binomial in DESeq2

Are zero is a default option of log2fold change to be considered as up and down? Yes, by default, the null hypothesis is that the log2FoldChange is zero. Does this mean that there are seven genes in total that will have significant adjusted p-value? Yes, you are interpreting your results…

Continue Reading Understanding the output of Negative Binomial in DESeq2