Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.

Koki Tsuyuzaki, Hiroyuki Sato, Kenta Sato, Itoshi Nikaido

January 2020 Genome Biol

Synopsis of Social media discussions

Many discussions highlight the practical applications of the benchmarking study, with users noting the challenges of scalability in handling large datasets. Phrases like "limiting step in my analyses" and mentions of the effectiveness of algorithms based on Krylov subspace and randomized singular value decomposition indicate a shared understanding of the article's relevance. The exciting tone used in these discussions, alongside detailed mentions of how specific algorithms outperform others, contributes to an overall atmosphere of enthusiasm and recognition of the publication's significance.

A
Agreement
 Strong agreement

The majority of posts reflect strong support for the methods and findings discussed in the publication.

I
Interest
 High level of interest

There is a high level of curiosity expressed regarding the advancements presented in PCA algorithms for RNA-sequencing.

E
Engagement
 Moderate level of engagement

While many users engage with the content, there are some posts that seem more focused on sharing rather than providing insights or analysis.

I
Impact
 High level of impact

Users perceive the publication to have a significant influence on the field of bioinformatics and computational biology.

Social Mentions

YouTube

2 Videos

Twitter

90 Posts

Blogs

2 Articles

Metrics

Video Views

6,479

Total Likes

334

Extended Reach

583,903

Social Features

94

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

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July 6, 2020

6,120 views

Benchmarking Efficient PCA for Single-Cell RNA Sequencing

Benchmarking Efficient PCA for Single-Cell RNA Sequencing

This video discusses benchmarking principal component analysis (PCA) for large-scale single-cell RNA sequencing. It reviews fast, memory-efficient PCA algorithms, focusing on methodologies like Krylov subspace and randomized singular value decomposition, which enhance performance for complex datasets.

JSB UCLA

April 19, 2024

359 views

  • 阿部2
    @cocotan_0 (Twitter)

    このベンチマーク論文でも取り上げられてる https://t.co/mZQy3i7Fvm
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    October 12, 2024

  • Tom Kelly ケリー・トム
    @tomkXY (Twitter)

    The main challenge is scalability, especially with the exponential growth in single-cell datasets and advent of new modalities or multi-omics. This will be informed by their benchmarking study of PCA. (I've found this a limiting step in my analyses.) https://t.co/i4Bm5ufy4H https://t.co/bWvtSAqSU3
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    October 15, 2020

  • もじゃもじゃのほう@求職中
    @antiplastics (Twitter)

    自分は単純なランダムサンプリングがクラスタリング精度を損なうことは確認していたが（https://t.co/ZKavQErjrF ）、こういう工夫はしたことが無いので機会があれば試したい。
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    August 3, 2020

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    RT @BioDecoded: Benchmarking principal component analysis for large-scale single-cell RNA-sequencing | Genome Biology https://t.co/ajXZBRzK…
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    February 25, 2020

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    Benchmarking principal component analysis for large-scale single-cell RNA-sequencing | Genome Biology https://t.co/ajXZBRzKRy #bioinformatics #PCA https://t.co/lJMRP5dhtj
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    February 25, 2020

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  • BioDecoded
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    Benchmarking principal component analysis for large-scale single-cell RNA-sequencing | Genome Biology https://t.co/ajXZBRzKRy #bioinformatics #PCA https://t.co/sjYnXJUyim
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    January 26, 2020

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    RT @BioMedCentral: Our top-rated article on @altmetric this week was "Benchmarking principal component analysis for large-scale single-cell…
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    January 24, 2020

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    Benchmarking principal component analysis for large-scale single-cell RNA-sequencing https://t.co/A6QtwiVpKi
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    January 24, 2020

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    RT @BioMedCentral: Our top-rated article on @altmetric this week was "Benchmarking principal component analysis for large-scale single-cell…
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  • BMC
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    Our top-rated article on @altmetric this week was "Benchmarking principal component analysis for large-scale single-cell RNA-sequencing". Read the article published in @GenomeBiology here. https://t.co/t8gj45wWN1 https://t.co/n4I3hW7we7
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    RT @sroyyors: Benchmarking principal component analysis for large-scale single-cell RNA-sequencing https://t.co/tdb1vhjmiZ
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    January 23, 2020

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    January 22, 2020

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    RT @sroyyors: Benchmarking principal component analysis for large-scale single-cell RNA-sequencing https://t.co/tdb1vhjmiZ
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    @Janis3_14159 (Twitter)

    RT @dritoshien: We finally out benchmarking paper of 10 fast and RAM-efficient PCA algorithms (21 implementations) for large-scale #singlec…
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    Benchmarking principal component analysis for large-scale single-cell RNA-sequencing https://t.co/tdb1vhjmiZ
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    January 21, 2020

  • Itoshi NIKAIDO
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    RT @RNASeqBlog: Researchers from @riken_en Center for Biosystems Dynamics Research review the existing fast and memory-efficient #principal…
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  • ゲノムのほうの愛ちゃん
    @dritoshi (Twitter)

    RT @RNASeqBlog: Researchers from @riken_en Center for Biosystems Dynamics Research review the existing fast and memory-efficient #principal…
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  • Nancy Yu
    @ynancy (Twitter)

    RT @RNASeqBlog: Researchers from @riken_en Center for Biosystems Dynamics Research review the existing fast and memory-efficient #principal…
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    January 21, 2020

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  • RNA-Seq Blog
    @RNASeqBlog (Twitter)

    Researchers from @riken_en Center for Biosystems Dynamics Research review the existing fast and memory-efficient #principalcomponentanalysis (PCA) #algorithms and implementations & evaluate their practical application to large-scale #scRNAseq datasets. https://t.co/ojBXb41y30
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  • Jamie Timmons
    @metapredict (Twitter)

    RT @BDR_RIKEN: PUBLICATION: Koki Tsuyuzaki, TL @dritoshien (Lab for Bioinformatics Research) et al propose guidelines for selecting appropr…
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  • Genome Biology
    @GenomeBiology (Twitter)

    RT @BDR_RIKEN: PUBLICATION: Koki Tsuyuzaki, TL @dritoshien (Lab for Bioinformatics Research) et al propose guidelines for selecting appropr…
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  • know thyself（子豚のオリバー)
    @thyself_know (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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    @bio_pypy (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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    @ybazetag (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • Joseph Powell
    @drjosephpowell (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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    January 21, 2020

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  • Marjan
    @Marjan13915420 (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • Robert Castelo
    @robertclab (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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    January 21, 2020

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  • Davide Risso
    @drisso1893 (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • Ming
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    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • Aedin Culhane (@AedinCulhane@genomic.social)
    @AedinCulhane (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • Shrabanti Chowdhury
    @Shrabanti1987 (Twitter)

    RT @dritoshien: We finally out benchmarking paper of 10 fast and RAM-efficient PCA algorithms (21 implementations) for large-scale #singlec…
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  • Osama Shiraz Shah, PhD
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    @KawataKentaro (Twitter)

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    @essepf (Twitter)

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  • Itoshi NIKAIDO
    @dritoshien (Twitter)

    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • ゲノムのほうの愛ちゃん
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    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • ZheFrench
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    RT @dritoshien: We finally out benchmarking paper of 10 fast and RAM-efficient PCA algorithms (21 implementations) for large-scale #singlec…
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    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • big0517
    @bigmaxbook (Twitter)

    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
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    RT @dritoshien: We finally out benchmarking paper of 10 fast and RAM-efficient PCA algorithms (21 implementations) for large-scale #singlec…
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  • James Ferguson
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    RT @dritoshien: We finally out benchmarking paper of 10 fast and RAM-efficient PCA algorithms (21 implementations) for large-scale #singlec…
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    RT @GenomeBiology: Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world…
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  • Genome Biology
    @GenomeBiology (Twitter)

    Tsuyuzaki, @dritoshien and co benchmark PCA approaches for analyzing scRNA-seq. 20 methods were assessed on 4 real-world datasets on a variety of metrics. Methods based on Krylov subspace and randomized singular value decomposition come out best https://t.co/PRjv5NpJPF https://t.co/o5zO00bmhW
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    Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. https://t.co/FUgAYtv8Ei
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    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
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    @yuifu (Twitter)

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    RT @dritoshien: We finally out benchmarking paper of 10 fast and RAM-efficient PCA algorithms (21 implementations) for large-scale #singlec…
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    RT @dritoshien: Our study shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, mem…
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  • Itoshi NIKAIDO
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    Our study shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and more accurate than the other algorithms. https://t.co/UpHIuTIuTs
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  • Itoshi NIKAIDO
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  • Itoshi NIKAIDO
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    RT @BDR_RIKEN: PUBLICATION: Koki Tsuyuzaki, TL @dritoshien (Lab for Bioinformatics Research) et al propose guidelines for selecting appropr…
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  • Ryuichiro Nakato (中戸 隆一郎)
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    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
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  • RIKEN BDR
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    PUBLICATION: Koki Tsuyuzaki, TL @dritoshien (Lab for Bioinformatics Research) et al propose guidelines for selecting appropriate PCA algorithms for use w/ large-scale scRNA-seq data sets based on benchmarking of existing PCA algorithms. In @GenomeBiology https://t.co/KC9GSoQdwk
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    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
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    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
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    January 19, 2020

    16

  • siero
    @siero5335 (Twitter)

    これですね。bioRxivのときから参考にしてた。 “Benchmarking principal component analysis for large-scale single-cell RNA-sequencing”, https://t.co/3AFPtFKxYZ
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    January 19, 2020

  • (｢・ω・)｢ｶﾞｵｰ
    @bicycle1885 (Twitter)

    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
    view full post

    January 19, 2020

    16

  • もじゃもじゃのほう@求職中
    @antiplastics (Twitter)

    RT @RIKEN_BDR: 研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single…
    view full post

    January 19, 2020

    16

  • 理研BDR(生命機能科学研究センター)
    @RIKEN_BDR (Twitter)

    研究成果「大規模データに対する主成分分析の性能を評価－100万規模の1細胞発現データで検証－」の原著論文はこちらです→"Benchmarking principal component analysis for large-scale single-cell RNA-sequencing" https://t.co/ndUv5rnW3k
    view full post

    January 19, 2020

    33

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Abstract Synopsis

  • Principal component analysis (PCA) is crucial for analyzing large single-cell RNA sequencing (scRNAseq) datasets, but it typically requires significant computation time and memory.
  • This work reviews fast and memory-efficient PCA algorithms, particularly those using Krylov subspace and randomized singular value decomposition, which perform better than other methods.
  • The study provides guidelines for choosing the right PCA implementation based on user and developer differences in computational environments.