Tag: DEG

Hi all, I have 4 datasets (Brain region A (Control & Disease), Brain region B (Control & Disease)) and performed scRNA-seq by integrating all data. And I want to compare Disease and Control in each brain region using pseudo-bulk DEG analysis. (ex. Region A Disease vs Region A Control) Even…

The heterogeneity of GABAergic neurons in human, macaque, mouse, and pig To perform a cross-species comparative study of the GABAergic neurons, we collected the snRNA-seq datasets of the cerebral cortex for human10,11, macaque12,13, mouse14,15, and pig16. After cell-type annotation and filtering out the excitatory neurons and non-neurons, the GABAergic neurons…

A recent study in Nature demonstrated transcriptomic dysregulation in the cerebral cortex in autism spectrum disorder (ASD). Study: Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Image Credit: Ukrolenochka/Shutterstock Background The risk factors for ASD include a significant genetic component with hundreds of risk genes involved. Molecular profiling…

doi: 10.3389/fgene.2022.958641. eCollection 2022. Affiliations Expand Affiliations 1 Laboratório de Ecofisiologia e Bioquímica de Plantas, Núcleo de Conservação da Biodiversidade, Instituto de Pesquisas Ambientais, São Paulo, SP, Brasil. 2 Programa de Pós-Graduação em Biologia Celular e Estrutural, Universidade Estadual de Campinas, Campinas, SP, Brasil. Item in Clipboard Camilla Alves Santos et…

Methylation is one of the most classical epigenetic modifications in eukaryotes. DNA methylation regulates gene expression and has important implications in both growth and disease-related research. DNA methylation affects the maturation of germ cells or embryonic cells subject to specific gene expression. It has also been widely studied and applied…

Hi, I have found that my selected gene, probe I.D 201667_at is differentially expressed between WDLPS and DDLPS tumour tissue samples after performing microarray DEG analysis. Instead of just a p value in a table format: Probe I.D “201667_at” logFC 10.8205874181535 AveExpr 10.6925705768407 t 82.8808890739766 P.Value 3.10189446528995e-88 adj.P Val 3.10189446528995e-88…

I am working on a datasets and after some discussion with my group, we doubt that maybe one or more of our controls are different than the other controls. The motivation is to see if one or more controls have been effected differently by the solvent they were kept in….

Introduction Multiple myeloma is a B-cell malignancy characterized by the malignant proliferation of clonal plasma cells in bone marrow,1 It is the second most common hematological malignancy after lymphoma.2 The age-standardised rate (ASR) of multiple myeloma incidence was 1·78 (95% UI 1·69-1·87) per 100 000 people globally and mortality was…

Introduction Stroke is one of the leading causes of death and disability worldwide, which causes substantial economic and social burdens.1 Ischemic stroke is caused by insufficient blood and oxygen supply to the brain,2 accounting for about 85% of the casualties of stroke patients.3 The concept of treatment for ischemic injury…

Cell-type annotation scRNA-seq data were filtered to discard low-quality cells and doublets (Supplementary Fig. 1, Extended Data Fig. 1 and Methods). Supervised clustering (Reference Component Analysis v2 (RCA2)) at low resolution grouped cells into 11 major cell types (Extended Data Fig. 1). To identify epithelial cell subtypes, we initially analyzed…

This might be a bit long, please bare with me. I’m conducting a differential expression analysis using limma – voom. My comparison is regarding response vs non-response to a cancer drug. However, I’m not getting any DE genes, absolute zeros. Someone here once recommended not to use contrast matrix for…

Problem in getting SNAI1 gene in prostate DEG table 0 Hello everyone, would you please help me I have got prostate DEGs by TCGAbiolinks package but I couldn’t get SNAI1 gene in my DEG table. I wonder why it does happen. First I normalized it and then I filtered it,…

Outcome of DNA sequencing, assembly, and validation In this study, initially total DNA was isolated from the finely chopped, full-grown pupa of Blepharipa sp. The NanoDrop spectrophotometer (1294 ng/μl) and the Qubit fluorometer (732.8 ng/μl) both found that the concentration of total DNA in the sample at an optimum level for mitochondrial DNA enrichment. The Tape Station profile showed…

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…

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…

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…

I am trying to run the Monocle3 function find_gene_modules() on a cell_data_set (cds) but am getting a variety of errors in this. I have not had any other issues before this. I am working with an imported Seurat object. My first error came back stating that the number of rows…

DataViz question for DEG and scRNA-seq 1 Maybe a dumb question, but I recently came across a single-cell paper that showed “volcano” plots for all clusters in one image by filtering for all genes that were FDR <0.05, the x-axis being represented by different colors characterizing different clusters, the y-axis…

With long reproductive timescales, large complex genomes, and a lack of reliable reference genomes, understanding gene function in conifers is extremely challenging. Consequently, our understanding of which genetic factors influence the development of reproductive structures (cones) in monoecious conifers remains limited. Genes with inferred roles in conifer reproduction have mostly…

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches. Welcome to the protocol of high-throughput transcriptome analysis for investigating host-pathogen interactions. This protocol is divided in the following steps….

Hello, I’m newbie in RNA-Seq analysis process. When I processed a RNA-Seq analysis, there are some questions. If you guys have a time, please let me know. In RNA-Seq data analysis (i.e DEG analysis), glmfit function in R used raw RNA-Seq count data for modelling? I TMM normalized for correct…

Hi, I was trying to implement TIP4P/2005f force field in CP2K. I have setup the relevant forcefield and geometry section in the following manner: &FORCE_EVAL  METHOD FIST  &MM    &FORCEFIELD      &SPLINE        EMAX_SPLINE 1.0E+128        RCUT_NB 12.0      &END SPLINE      &BEND   …

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…

Introduction Thyroid carcinoma (THCA) is the most common type of endocrine malignancy and its incidence is increasing.1 Based on its histopathological characteristics, thyroid carcinoma can be classified into multiple subtypes, such as papillary thyroid carcinoma (PTC), follicular thyroid carcinoma, and anaplastic thyroid carcinoma.2 PTC is the most common subtype of…

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…

Defeng Pan,1,&ast; Yufei Zhou,2,&ast; Shengjue Xiao,1,&ast; Yue Hu,3,&ast; Chunyan Huan,1 Qi Wu,1 Xiaotong Wang,1 Qinyuan Pan,1 Jie Liu,1 Hong Zhu1 1Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of…

Description: trial size (200 assays), AccuClear Ultra High Sensitivity dsDNA Quantitation Kit with DNA Standard Category: Nucleic acid quantitation Shipping temperature: 15&deg, C, C to 30&deg Shipping conditions: n/a Storage: 2&deg, C, C to 8&deg Gene target: AccuClear Sensitivity dsDNA Quantitation Kit with DNA trial size (200 assays) Short name:…

after run estimate_size_factors, data with active.assay = ‘integrated’ works too, but no deg in the result. > [email protected] = ‘integrated’ > cds_raw <- as.cell_data_set(seurat_object) Warning: Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. Please run ‘cluster_cells’ on your cell_data_set object > cds <- cluster_cells(cds_raw) > pr_graph_test_res <-…

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…

How to generate a Venn diagram from edgeR DEG results? 0 Dear All, I have done DEG analysis of genes from 4 samples using edgeR. I want to plot them (number of DEGs) using a Venn diagram. I have results from pairwise comparisons as UP and DOWN gene list with…

Problem for analysing GSEA and ORA 0 I analysed two different GEO dataset individually and found DEG for each dataset .After that I compare the two dataset DEG and found some common DEG.Now i want to analyse GSEA and ORA for that common DEG.As far i know for analysing GSEA…

Introduction Alzheimer’s disease (AD) ranks first among the common dementia type of the world. According to epidemiological investigation from the International Alzheimer’s disease association, about 45 million people has been suffered from AD, and the number is expected to increase to 131 million in 2050.1 Despite the widespread prevalence of…

Re-analysis of multiple scRNAseq data as a combined new one 0 Hi all, There are so many regions in brain.The cortex itself is of many types.I found many single cell RNA seq studies which focus on different regions eg- gse … somatosensory cortex dropseq year 2015 gse….. primary visual cortex…

Event listing from University of Pittsburgh: Wednesday, September 15 from 10:00 AM to 4:00 PM This workshop will focus on Gene Expression Omnibus (GEO) repository. We will learn how to find high throughput gene expression studies – bulk RNA-seq/ scRNA-seq /microarray from GEO, retrieve gene expression count data and generate…

Event listing from University of Pittsburgh: Wednesday, September 15 from 10:00 AM to 4:00 PM This workshop will focus on Gene Expression Omnibus (GEO) repository. We will learn how to find high throughput gene expression studies – bulk RNA-seq/ scRNA-seq /microarray from GEO, retrieve gene expression count data and generate…

Gene with hundreds of counts very similar, is it possible to use it to get DEGs? 0 Hi, I’m starting in boinfomatics and I’m using Deseq2 for differential expression analysis. I have an RNAseq dataset, where one of the genes I intend to analyze has hundreds of counts ranging from…

GitHub – HaoWuLab-Bioinformatics/wu-group: ECSWalk Files Permalink Failed to load latest commit information. Type Name Latest commit message Commit time ECSWalk: A carcinogenic driver module detection method based on a network model. Data used in this study: In our research, we used data processed by Rafsan Ahmed, et al. and Mark…

create protein database for local Blast 1 Hello friends, how’re you? I wanted to know how I can do a blast against a database that I downloaded. I’ve downloaded a database of protein sequences from DEG (essential genes) and my idea is to do a blast against this database in…

List of human protein coding genes with given name (known function?) 2 Hello, To put it simply, I am doing differential expression analysis on human RNA-seq data and I want to focus my analysis of genes that are: 1) Protein coding, so no SNOR or MIR 2) Genes with a…

Sorry for lateness, I wanted to do something similar. This is what I did for reference: Using a Seurat generated gene list for input into ClusterProfiler to see the GO or KEGG terms per cluster. I’ll keep the meat and potatoes of the Seurat vignette in this tutorial: library(dplyr) library(Seurat)…

DEGs with uninsteresed sample files 1 Hi all, I am working on some bulkRNAseq data from GEO and my sample description looks like this. I am interested in A vs B, A vs C and B vs C but I am not interested in sample D. My question is whether…

How filter genes to construct co-expression network? 1 Hi, I am interested to filter data for constructing co-expression network , Which parameter can i use to filter genes? As i know in WGCNA tutorial, it suggests not to use differential expressed genes(DEG) to filter genes. WGCNA Co-expreesion network DEG Filtering…

FindMarkers for ClusterProfiler 1 Hi, I recently ran FindMarkers to compare DEG between two different clusters in a single-cell RNA-seq analysis This is my code: markers= FindMarkers(obj, ident.1=c(4), ident.2 = c(5)) head(markers) dim(markers) table(markers$avg_log2FC > 0) table(markers4v5$p_val_adj < 0.05 & markers$avg_log2FC > 0) I would like to run ClusterProfiler to…

Power calculation for microarray data 0 I have an initial sample of 228 patients from a microarray study. Recently I have obtained a new set of labels specifying different condition types for only 69 out of the 228 patients. I wanted to run a DEG analysis on this set of…

I wrote a script on how to analyze the microarray-based miRNA expression data. Here is my code: # general config baseDir <- ‘.’ annotfile <- ‘mirbase_genelist.tsv’ setwd(baseDir) options(scipen = 99) require(limma) # read in the data targets <- read.csv(“/media/mdrcubuntu/46B85615B8560439/microarray_text_files/targets.txt”, sep=””) # retain information about background via gIsWellAboveBG project <- read.maimages(targets,source=”agilent.median”,green.only…