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Предыдущая публикация

The ρPCA method is implemented in an easy to use Python package github.com/pachterlab/r... Quick to install. Fast. We now use it routinely whenever engaging in exploratory data analysis. 8/

GitHub - pachterlab/rhopcaContribute to pachterlab/rhopca development by creating an account on GitHub.github.com

1 прямых ответов · 8 сообщений во всей ветке

Lior Pachter

13 апр., 02:48

In Part II, we develop ρPCA in two ways: adding kernel weighting (from spatial PCA), and also operating in the space of basis coefficients (from functional PCA). The former is great for spatial analysis, the latter for time series. 9/

Lior Pachter

13 апр., 02:48

The second preprint is here: www.biorxiv.org/content/10.6... 10/

www.biorxiv.org

Lior Pachter

13 апр., 02:48

The contrastive spatial analysis is incredibly powerful. Examining a colorectal spatial single-cell RNA-seq sample (Visium HD) as target with a matched normal (non-spatial) as background, we are able to delineate tumor cells, and find interesting and relevant gene programs. 11/

Lior Pachter

13 апр., 02:48

Particularly powerful here is the approach to "integrate" spatial data with non-spatial controls. Moreover, since k-ρPCA (the k is for kernel) is a variant of ρPCA it is efficient, which is non-trivial. We are able to process very large datasets easily (e.g. Visium HD). 12/

Lior Pachter

13 апр., 02:48

Functional PCA has a long history dating back to the 1990s. I think the contrastive version we introduce via the Rayleigh quotient is going to be very useful. We showcase the power on a bulk RNA-seq time series, finding relevant and interesting differentially variable genes. 13/

Lior Pachter

13 апр., 02:48

Thus, ρPCA, k-ρPCA, f-ρPCA are immediately useful across a wide variety of applications. Moreover, they can be combined (e.g. kernel weighted PCA on basis coefficients). The interpretability is especially useful in biology, but we expect also in other scientific domains. 14/

Lior Pachter

13 апр., 02:48

k-ρPCA and f-ρPCA are also implemented as part of the github.com/pachterlab/r... package. 15/

GitHub - pachterlab/rhopcaContribute to pachterlab/rhopca development by creating an account on GitHub.github.com

Lior Pachter

13 апр., 02:48

This project was tons of fun. It started with a journal club reviewing the @jameszou.bsky.social contrastive PCA paper last fall. Maria Carilli and Kayla Jackson have been incredible in figuring out all the details, including working out all the math (in the supplements). 16/

Lior Pachter

13 апр., 02:49

Tl;dr: two preprints on contrastive PCA w/ Maria Carilli and Kayla Jackson solve the non-spatial setting, spatial contrastive PCA, and contrastive functional PCA. www.biorxiv.org/content/10.1... www.biorxiv.org/content/10.6... 17/17

www.biorxiv.org