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Lior Pachter
26.5K posts
Lior Pachter
@lpachter
Bren Professor of Computational Biology @caltech. Blog at liorpachter.wordpress.com. Tweets represent my views, not my employer's. #methodsmatter
It's time to stop making t-SNE & UMAP plots. In a new preprint w/ Tara Chari we show that while they display some correlation with the underlying high-dimension data, they don't preserve local or global structure & are misleading. They're also arbitrary.🧵biorxiv.org/content/10.110…
- This recently published figure by @Sarah_E_Ancheta et al. is very disturbing and should lead to some deep introspection in the single-cell genomics community (I doubt it will). It demonstrates complete disagreement among 5 widely used "RNA velocity" methods 1/
- Aristotle was the first to notice honeybees dancing. In 1927 Karl von Frisch decoded the waggle. How it works was "explained" by MV Srinivasan AM FRS in the 1990s. Except @NeuroLuebbert found his papers are junk. A 🧵 about her discovery & our report: arxiv.org/abs/2405.12998 1/
- The choice of whether to use Seurat or Scanpy for single-cell RNA-seq analysis typically comes down to a preference of R vs. Python. But do they produce the same results? In biorxiv.org/content/10.110… w/ @Josephmrich et al. we take a close look. The results are 👀 1/🧵
- This is the paper that the terrorist who killed 10 people in Buffalo cited. @sapinker described the genetic variants as "collectively predict[ing] a big chunk of variance in educational attainment", which is false. 1/2
- I've noticed it's becoming increasingly common in genomics to report results of regressions with ridiculously low correlation as "significant" based on a tiny p-value (for the hypothesis that the slope = 0). Can you guess R^2, the p-value, and where the data below was published?
- Analysis of #scRNAseq requires constant, tedious, interaction with genomics databases. To facilitate querying from @ensembl et al., @NeuroLuebbert developed gget: biorxiv.org/content/10.110… (code @ github.com/pachterlab/gget). gget has many uses; a 🧵on the its amazing versatility: 1/
- A friend (who does not work in science) asked me today whether it is true that "protein folding has been solved". My short answer: The AlphaFold method produced very impressive results on CASP14. Protein folding is not a solved problem.
- Kind of weird to see genomics people here today celebrating the log-fold-change of 0.0007371 in the top two times for the 100m dash at the olympics, but also throwing out any result where the log-fold-change is less than 1.
