https://www.nature.com/articles/s41586-022-04713-1
Published: 11 May 2022
Single-cell eQTL models reveal dynamic T cell state dependence of disease loci Aparna Nathan, Samira Asgari, Kazuyoshi Ishigaki, Cristian Valencia, Tiffany Amariuta, Yang Luo, Jessica I. Beynor, Yuriy Baglaenko, Sara Suliman, Alkes L. Price, Leonid Lecca, Megan B. Murray, D. Branch Moody & Soumya Raychaudhuri
Nature (2022)
the marker paper of the Hartwig Medical Foundation paper.
Pan-cancer whole-genome analyses of metastatic solid tumours https://www.nature.com/articles/s41586-019-1689-y
Peter Priestley, Jonathan Baber, Martijn P. Lolkema, Neeltje Steeghs, Ewart de Bruijn, Charles Shale, Korneel Duyvesteyn, Susan Haidari, Arne van Hoeck, Wendy Onstenk, Paul Roepman, Mircea Voda, Haiko J. Bloemendal, Vivianne C. G. Tjan-Heijnen, Carla M. L. van Herpen, Mariette Labots, Petronella O. Witteveen, Egbert F. Smit, Stefan Sleijfer, Emile E. Voest & Edwin Cuppen
Nature volume 575, pages
210–216 (2019)
There have been numerous follow-up papers since.
Also, the Glioma Longitudinal Analysis (GLASS) Consortium datasets, which are publicly accessible via www.synapse.org/glass.
The first marker paper is here:
https://www.nature.com/articles/s41586-019-1775-1.
https://www.nature.com/articles/s41586-021-04269-6
https://twitter.com/MarkGerstein/status/1510065060806283269
Interesting paper, relating de novo mutation rate to epigenetic features. Wonder exactly how this connects to the fact that the background mutation rate in cancer genomes depends strongly on epigenetics.
Important genomic regions mutate less often than do other regions https://www.nature.com/articles/d41586-022-00017-6
https://www.nature.com/articles/s41586-021-03922-4
https://twitter.com/SEHanlon/status/1513548800949989377
At #AACR22, @VanAllenLab gives a nice overview of his paper using an interpretable NN model to get insights into determining cancer severity (https://nature.com/articles/s41586-021-03922-4)
Biologically informed deep neural network for prostate cancer discovery
Liked the way he hard-coded specific genes & pathways into the model & looked in detail where the model misclassified specific patients #AACR22 #AACR2022
Also, thought the hard-coding of genes into the model was similar to that in another interpretable AI approach (for brain disease, https://science.org/doi/10.1126/science.aat8464) Could have used the “rank projection trees” from this to highlight important genes #AACR22 #AACR2022
G suite free appears to be ending
https://support.google.com/a/answer/2855120?hl=en
https://www.cell.com/cell/fulltext/S0092-8674(21)01279-4
A single-cell atlas of chromatin accessibility in the human genome
Kai Zhang 8
James D. Hocker 8
Michael Miller
….
Allen Wang
Sebastian Preissl
Bing Ren 9
Published:November 12, 2021
DOI:https://doi.org/10.1016/j.cell.2021.10.024
Liked this @KharchenkoLab review – in particular, the descriptions of the various low-dimensional approximations & the simple motivation for these using PCA. Also, found the step-by-step workflow in the text & figures helpful.
Note also the reference to expression entropy for determining the direction in trajectories.
Review Article
Published: 21 June 2021
The triumphs and limitations of computational methods for scRNA-seq
Peter V. Kharchenko
Nature Methods volume 18, pages723–732 (2021)
linkstream2 microblog 