@lpachter @vitaliikl @GorinGennady @GallowayLabMIT And yes, any dimensionality reduction method can throw out biological signal on occasion. PCA comes out on top in this study: https://t.co/xSQrcMjNuM. Curious to know if there are other studies that come t
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
RT @WomenInStat: Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old met…
Dimension Reduction is critical to single cell (sc) data analysis. PCA is the most widely used approach PCA is an old method, however always ranked highly in benchmarking studies often because of its speed. Sun et al https://t.co/LkchdezBCY https://t.co
6. Metrics: Jaccard index for neighborhood preserving, normalized mutual information and ARI for cell clustering analysis, and Kendall correlation coefficient for trajectory inference, and stability of each dimension reduction method across data splits.
@biobenkj Sun et al recently published a comparison of 18 dimension reduction methods including PCA, tSNE and UMAP. Evaluated by comparing their effectiveness at downstream clustering and trajectory analysis. https://t.co/0yD0jiusxj
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis https://t.co/mvZLcHcsS7 a comparison of 18 different dimensionality reduction methods @GenomeBiology
RT @BioDecoded: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology…
RT @BioDecoded: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology…
RT @BioDecoded: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology https://t.co/MdkchRXAPz #bioinformatics #RNAseq https://t.co/TmLinjK1Ch
RT @artofbiology: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biolog…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/cCcW9uXjgb
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/KygZ2UODbu
RT @LGMartelotto: 💣 Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biol…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/tixugbq43f
RT @StatGenDan: Quite a comprehensive comparison. https://t.co/kMO6CLri30
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis https://t.co/dpO32Uxw7t
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
RT @jsantoyo: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9…
RT @jsantoyo: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9…
RT @jsantoyo: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9…
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
RT @ivivek87: Indeed! Dimension reduction benchmarking 😃! https://t.co/ZmRNwVPVML Github: https://t.co/Wd3XvwFAGP #SingleCell #Bioinfor…
RT @StatGenDan: Quite a comprehensive comparison. https://t.co/kMO6CLri30
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/FFDOOggHV6 https://t.co/QW9KEqdtZ9 Dimensionality reduction is an indispensable analytic component for man
RT @LGMartelotto: 💣 Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biol…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @RiyueSunnyBao: 18 algorithms & 30 publicly available cohorts across 7+ sequencing techniques - comprehensive #evaluation of #dimensiona…
RT @daweonline: Majestic and extremely useful. It’s nice that UMAP and diffmap work well (the ones I use the most in sc) but also NMF is ve…
18 algorithms & 30 publicly available cohorts across 7+ sequencing techniques - comprehensive #evaluation of #dimensionality reduction methods on #singlecell #RNAseq data - #accuracy #robustness #scalability from @xzlab_org https://t.co/h40p839AnF &a
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
https://t.co/FFDOOggHV6 Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/QW9KEqdtZ9 Dimensionality reduction is an indispensable analytic component for man
RT @LGMartelotto: 💣 Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biol…
Top #tweeted story in #bioinformatics: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/lzXyZFS4qZ, see more https://t.co/x4TiUjeQ4E
RT @LGMartelotto: 💣 Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biol…
💣 Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/wgrCdMVxsx
Top @RNomics #tweeted story: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis | Genome Biology | Full Text https://t.co/lzXyZFS4qZ, see more https://t.co/mx2YzvfCax
RT @petr_taus: No one method to rule them all 🧙♂️ no surprise ... Very useful comparison of dimensionality reduction techniques for scRNA-…
RT @daweonline: Majestic and extremely useful. It’s nice that UMAP and diffmap work well (the ones I use the most in sc) but also NMF is ve…
No one method to rule them all 🧙♂️ no surprise ... Very useful comparison of dimensionality reduction techniques for scRNA-seq data 🤹♂️
RT @jsantoyo: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @jsantoyo: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9…
Majestic and extremely useful. It’s nice that UMAP and diffmap work well (the ones I use the most in sc) but also NMF is very appropriate (the one I use and know best for many other purposes)
😮
RT @jsantoyo: Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. https://t.co/kFYw9hsn9D
RT @StatGenDan: Quite a comprehensive comparison. https://t.co/kMO6CLri30
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @ppgardne: Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (ora…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
Indeed! Dimension reduction benchmarking 😃! https://t.co/ZmRNwVPVML Github: https://t.co/Wd3XvwFAGP #SingleCell #Bioinformatics
Good old PCA does well, tSNE is not so flash... Caption: good performance = 2 (sky blue), intermediate performance = 1 (orange), and poor performance = 0 (gray)
Quite a comprehensive comparison.
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @LachowiczMarrah: Not one method that works well across all tasks? Sounds like a gap that needs to be addressed! https://t.co/XFuHlBpiTB
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis https://t.co/dHBTnUHWRZ
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
RT @GenomeBiology: A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compa…
Not one method that works well across all tasks? Sounds like a gap that needs to be addressed!
💖💖👏👏👏
A benchmarking of dimensionality reduction techniques for scRNA-seq data from @SunShiquan, @xzlab_org and co. They compare 18 methods on 2 simulated and 30 real datasets. There is not one method that works well across all tasks. https://t.co/bpDTBqYhQc ht