Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

Michael I Love, Wolfgang Huber, Simon Anders

August 2015

Synopsis of Social media discussions

The collective discussions reflect a strong appreciation for DESeq2, exemplified by phrases like 'classic' and 'highly cited,' alongside mentions of educational activities such as workshops and code sharing, which indicate both high interest and engagement. The tone of admiration and curiosity highlights the publication's perceived importance and the community’s active involvement in learning and applying the method.

A
Agreement
 Moderate agreement

Most discussions recognize the significance and utility of the DESeq2 paper, with some describing it as a classic or highly cited work, indicating general agreement on its value.

I
Interest
 High level of interest

Posts show high interest by highlighting the paper's accessibility, referencing workshops, and sharing resources like code on Github, demonstrating enthusiasm and engagement with the content.

E
Engagement
 High engagement

Comments include questions about manuscript choices, summaries of the paper's importance, and mentions of workshops, reflecting deep involvement and discussion beyond surface level.

I
Impact
 Moderate level of impact

The widespread citations, mention of its citation status on bioRxiv, and emphasis on its clarity suggest the publication has a notable influence on RNA-seq analysis and bioinformatics communities.

Social Mentions

YouTube

7 Videos

Facebook

3 Posts

Twitter

10 Posts

Blogs

14 Articles

News

12 Articles

Reddit

2 Posts

Metrics

Video Views

250,358

Total Likes

3,490

Extended Reach

317,055

Social Features

48

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Understanding DESeq2 for Differential Gene Expression Analysis in RNAseq

Understanding DESeq2 for Differential Gene Expression Analysis in RNAseq

This video introduces DESeq2, a key tool for analyzing count data from RNA sequencing to identify differentially expressed genes, explaining its underlying model and steps for accurate results.

Bioinformagician

January 22, 2022

160,637 views

Analyzing Differential Gene Expression with DESeq2 in RNAseq Data

Analyzing Differential Gene Expression with DESeq2 in RNAseq Data

This tutorial demonstrates how to perform differential gene expression analysis using DESeq2. It covers input data preparation, PCA, dispersion QC, and output interpretation, facilitating further investigation in R, Python, or Excel.

Sanbomics

December 12, 2021

62,082 views

Understanding the Use of Negative Binomial in RNA-Seq Data Analysis

Understanding the Use of Negative Binomial in RNA-Seq Data Analysis

DESeq2 analyzes RNA-seq data by estimating gene expression changes, accounting for variability and outliers, leading to more accurate results. The video explains the statistical models and distributions, including how Poisson-based approaches are used.

LiquidBrain Bioinformatics

April 29, 2022

25,553 views

Advanced Statistical Methods for RNA-Seq Data Analysis in Computational Biology

Advanced Statistical Methods for RNA-Seq Data Analysis in Computational Biology

This lecture covers statistical techniques including Negative Binomial, ComBat, DESeq2, and GLM for RNA-seq analysis, focusing on accurate differential gene expression estimation. DESeq2 uses shrinkage estimation to improve results' stability and interpretability.

JSB UCLA

January 27, 2022

639 views

Normalization Techniques for Single-Cell RNA Sequencing Data

Normalization Techniques for Single-Cell RNA Sequencing Data

This lecture discusses the significant cell-to-cell variation in scRNA-seq data and introduces a modeling framework using regularized negative binomial regression. It aims to normalize and stabilize variance to enhance downstream analyses while reducing technical biases.

JSB UCLA

April 10, 2024

564 views

R-Ladies Tunis

April 18, 2021

525 views

Analyzing Differential Gene Expression with DESeq2 in RNA-Seq Data

Analyzing Differential Gene Expression with DESeq2 in RNA-Seq Data

In this episode, Michael Love discusses differential gene expression analysis from bulk RNASeq data, focusing on DESeq2 and related packages. The video covers the tools' history, theory, and practical applications in bioinformatics.

the bioinformatics chat

November 16, 2023

359 views

  • Peter Castaldi
    @peter4244 (Twitter)

    Mike Love et al's DESeq2 paper from the same year is also a great article for those being introduced to RNA-seq data https://t.co/ByvdkobF9i
    view full post

    February 24, 2024

  • Yueqiang Zhang
    @JohnZha02385409 (Twitter)

    "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 | Genome Biology | Full Text" https://t.co/Z5Or7rWFUt
    view full post

    October 23, 2021

  • Steven Ge
    @StevenXGe (Twitter)

    @mikelove @s_anders_m @wolfgangkhuber That's why the 2014 DESeq2 paper is such a classic, easily understood by non-statisticians like me. https://t.co/pQzs7lW7ei
    view full post

    September 13, 2021

    5

  • R Tweets
    @rstats_tweets (Twitter)

    RT @RLadiesTunis: Happy to share with you the materials for our last #R4Bioinfo meetup while we have been hosting @mard113 and @steman_rese…
    view full post

    April 24, 2021

    3

  • Rstats
    @rstatstweet (Twitter)

    RT @RLadiesTunis: Happy to share with you the materials for our last #R4Bioinfo meetup while we have been hosting @mard113 and @steman_rese…
    view full post

    April 24, 2021

    3

  • Mouna Belaid ➖ @mouna_belaid@mastodon.social
    @mounaa_belaid (Twitter)

    RT @RLadiesTunis: Happy to share with you the materials for our last #R4Bioinfo meetup while we have been hosting @mard113 and @steman_rese…
    view full post

    April 24, 2021

    3

  • R-Ladies Tunis
    @RLadiesTunis (Twitter)

    Happy to share with you the materials for our last #R4Bioinfo meetup while we have been hosting @mard113 and @steman_research who took us through an incredible #workshop. ➡️Code on Github: https://t.co/KGwnx9Jxef
    view full post

    April 24, 2021

    9

    3

  • Nadav Yayon
    @YayonNadav (Twitter)

    @slobentanzer @MicrobiomDigest Which manuscript would you choose to claim for yourself? Perhaps this? https://t.co/u5Glh1UR7L
    view full post

    November 18, 2020

  • Michael Love
    @mikelove (Twitter)

    @NM_Reid Check out the DESeq2 paper https://t.co/sAYQTvVTHX https://t.co/aLn45bqUEr
    view full post

    July 14, 2020

    1

  • Scite
    @Scite (Twitter)

    "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" is the most highly cited preprint on @biorxivpreprint. https://t.co/SSUBMSUrjO
    view full post

    June 5, 2020

    2

Abstract Synopsis

  • DESeq2 is a statistical tool designed to analyze RNAseq data by estimating changes in gene expression while accounting for variability and outliers, leading to more accurate and stable results.
  • It uses a technique called shrinkage estimation to improve the interpretation of how much gene expression differs between experimental conditions, focusing on both the significance and magnitude of changes.
  • The DESeq2 package is accessible for researchers via Bioconductor, a platform that provides tools for bioinformatics analysis, making it easier to perform differential expression studies in RNAseq experiments.]