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Several discussions highlight the exaggerated false positives reported in the article, such as one user stating, 'DESeq2’s FDR can increase significantly with large sample sizes,' reflecting the article's critical findings. Another poster remarked on the implications of using alternative methods by saying, 'Wilcoxon rank-sum test for the win!' The choice of phrases like 'Uh oh!!' and 'game changer' demonstrates an urgent tone, indicating the perceived significance of this research within the community.
Overall, there seems to be a consensus on the limitations of DESeq2 and edgeR, with many discussions acknowledging the article's insights.
The discussions generated significant curiosity, with many participants eager to explore the implications of the findings.
While some users commented on their personal experiences, there was a mixture of deeper analysis and casual conversation regarding the article.
Participants believe the publication carries considerable importance, as it could affect how future studies are designed.
2/ But here’s the issue: When you have hundreds of samples, DESeq2’s FDR can increase significantly. A 2022 study highlights this problem:https://t.co/d5si8W8eJv https://t.co/d5si8W8eJv
DESeq2 is amazing, but it’s not a one-size-fits-all tool. Learn its strengths and limitations! More on RNA-seq pitfalls: https://t.co/d5si8W8eJv Thoughts? How do you handle large datasets? Let’s discuss below!
https://t.co/JILH3bcVIT "A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples...a caveat...led to incorrect comparisons of ... methods." Normalization & realistic sim. matters critically in eval.
The original paper (https://t.co/7wkJYkvOPB) generated a lot of discussion #onhere. Here's a different perspective on the reported exaggerated false positive rates in common DE tools like DESeq2 and edgeR! https://t.co/YerubnwnKZ
Uh oh!! Someone is in trouble. "#DESeq2 and #edgeR, have unexpectedly high false discovery rates". Authors recommend Wilcoxon rank-sum test #TakeHeadsOutOfSand #Bioinformatics #RNAseq #Genomics #NGS https://t.co/s2huklllNP
"When identifying DEGs between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. " ... 1/2 https://t.co/X9vk4qJRf6 1/2
Jingyi Jessica Li discusses the importance of statistical rigor in genomics data science, covering differential expression analysis, gene expression simulation, and FDR control, with practical guidelines for making…
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…
@tangming2005 For large sample size DE analysis, DESeq2 and edgeR are not the optimal choice. https://t.co/00VQDazP9p
RT @tangming2005: DESeq2 is amazing, but it’s not a one-size-fits-all tool. Learn its strengths and limitations! More on RNA-seq pitfalls:…
RT @tangming2005: DESeq2 is amazing, but it’s not a one-size-fits-all tool. Learn its strengths and limitations! More on RNA-seq pitfalls:…
RT @tangming2005: 2/ But here’s the issue: When you have hundreds of samples, DESeq2’s FDR can increase significantly. A 2022 study highl…
RT @tangming2005: 2/ But here’s the issue: When you have hundreds of samples, DESeq2’s FDR can increase significantly. A 2022 study highl…
RT @tangming2005: DESeq2 is amazing, but it’s not a one-size-fits-all tool. Learn its strengths and limitations! More on RNA-seq pitfalls:…
RT @tangming2005: 2/ But here’s the issue: When you have hundreds of samples, DESeq2’s FDR can increase significantly. A 2022 study highl…
RT @tangming2005: DESeq2 is amazing, but it’s not a one-size-fits-all tool. Learn its strengths and limitations! More on RNA-seq pitfalls:…
RT @tangming2005: 2/ But here’s the issue: When you have hundreds of samples, DESeq2’s FDR can increase significantly. A 2022 study highl…
DESeq2 is amazing, but it’s not a one-size-fits-all tool. Learn its strengths and limitations! More on RNA-seq pitfalls: https://t.co/d5si8W8eJv Thoughts? How do you handle large datasets? Let’s discuss below!
2/ But here’s the issue: When you have hundreds of samples, DESeq2’s FDR can increase significantly. A 2022 study highlights this problem:https://t.co/d5si8W8eJv https://t.co/d5si8W8eJv
RT @nomad421: https://t.co/JILH3bcVIT "A recent study reported exaggerated false positives by popular differential expression methods when…
https://t.co/JILH3bcVIT "A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples...a caveat...led to incorrect comparisons of ... methods." Normalization & realistic sim. matters critically in eval.
pmc.ncbi.nlm.nih.gov/articles/PMC... "A recent study reported exaggerated false positives by popular differential expression methods when analyzing large population samples...a caveat...led to incorrect comparisons of benchmark methods." Normalization & realistic sim. matters critically in eval.
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
@davidjglassMD Congrats ! Your short description of your work makes me think of this paper: https://t.co/YIacEhEIft While the title is catchy, I am not convinced by how the authors addressed the issue of false positives for DEGs. Is your work very different? If so, it is worth the reading.
@metapredict I did not implemented anything in limma (I wish I was that good). As for large samples I can only think of this https://t.co/vVzVunsI1M
RT @a_a_yurchenko88: @Mucus_Man A very common problem of deseq and other model-based methods. See this paper: https://t.co/qV4X1fqq5a
RT @a_a_yurchenko88: @Mucus_Man A very common problem of deseq and other model-based methods. See this paper: https://t.co/qV4X1fqq5a
@Mucus_Man A very common problem of deseq and other model-based methods. See this paper: https://t.co/qV4X1fqq5a
Paper mentioned: "Exaggerated false positives by popular differential expression methods when analyzing human population samples" https://t.co/r7vblCZzFM
https://t.co/QhJmjB9rqm
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @JakkeNeiro: "When identifying DEGs between two conditions using human population RNA-seq samples, we found a phenomenon by permutation…
"When identifying DEGs between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. " ... 1/2 https://t.co/X9vk4qJRf6 1/2
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
Exaggerated false positives by popular differential expression methods when analyzing human population samples https://t.co/bRXWCaT7LC
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
@tomkXY thank you for your answer, I read this paper yesterday and it found that the DESeq2 and edgeR have a high false disovery rates when analyzing human samples. so i was thinking that the same apply for fish samples as well. https://t.co/WhSi7OEIf5
@GenomeBiology I do like the way you publish short articles highlighting major flaws in how OMIC studies are analysed... https://t.co/wTdOuV31Kd https://t.co/dSSVlBvi2t But then do absolutely nothing to ensure - editorially -solutions are implemented @Cell_Onion
The original paper (https://t.co/7wkJYkvOPB) generated a lot of discussion #onhere. Here's a different perspective on the reported exaggerated false positive rates in common DE tools like DESeq2 and edgeR! https://t.co/YerubnwnKZ
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @GenomeBiology: Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweil…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
@lucacozzuto Giorni ho trovato questo https://t.co/mnDWelnH1E
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
The long battle between DESeq2 and EdgeR is apparently over. Wilcoxon rank-sum test for the win! An interesting read: https://t.co/zf6Bcqpc69
RT @knstnr: This is quite interesting: '...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rat…
This is quite interesting: '...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates.' tl;dr use Wilcoxon rank-sum test for large population samples (>8 samples per condition) https://t.co/Hf8lhGcOju
RT @jsantoyo: Exaggerated false positives by popular differential expression methods when analyzing human population samples. #PopulationLe…
RT @jsantoyo: Exaggerated false positives by popular differential expression methods when analyzing human population samples. #PopulationLe…
Exaggerated false positives by popular differential expression methods when analyzing human population samples. #PopulationLevelStudies #RNAseq #ResultBias https://t.co/jd8CAP5dbk @GenomeBiology
RT @jsb_ucla: Lesson learned from DE gene analysis on immunotherapy data: with large sample sizes, permutation analysis offers sanity check…
RT @jsb_ucla: Lesson learned from DE gene analysis on immunotherapy data: with large sample sizes, permutation analysis offers sanity check…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @GenomeBiology: Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweil…
RT @GenomeBiology: Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweil…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @GenomeBiology: Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweil…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @jsb_ucla: Lesson learned from DE gene analysis on immunotherapy data: with large sample sizes, permutation analysis offers sanity check…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
@O_Elashkar For detailed discussion, please see our paper https://t.co/ACb2hvFK44
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @GenomeBiology: Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweil…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @detective_louis: Uh oh!! Someone is in trouble. "#DESeq2 and #edgeR, have unexpectedly high false discovery rates". Authors recommend W…
RT @detective_louis: Uh oh!! Someone is in trouble. "#DESeq2 and #edgeR, have unexpectedly high false discovery rates". Authors recommend W…
Uh oh!! Someone is in trouble. "#DESeq2 and #edgeR, have unexpectedly high false discovery rates". Authors recommend Wilcoxon rank-sum test #TakeHeadsOutOfSand #Bioinformatics #RNAseq #Genomics #NGS https://t.co/s2huklllNP
RT @GenomeBiology: Study of false positives when calling differentially expressed genes, from @YM123411, @ge_xinzhou, @FanglueP, @superweil…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @Loose_Lab_Rat: Exaggerated false positives by popular differential expression methods when analyzing human population samples https://…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
RT @lpachter: "...two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates [when identifying diff…
Exaggerated false positives by popular differential expression methods when analyzing human population samples https://t.co/CuTEHXWjxd
Exaggerated false positives by popular differential expression methods when analyzing human population samples | Genome Biology | Full Text https://t.co/gkbtFFLKuI
Exaggerated false positives by popular differential expression methods when analyzing human population samples https://t.co/p56fPhkxAM