Posts Tagged ‘x57l’

ScEQTL

May 12, 2022

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)

Setting up an email address for a Slack channel

April 29, 2022

https://slack.com/help/articles/206819278-Send-emails-to-Slack

Hartwig

April 23, 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.

Mutation bias reflects natural selection in Arabidopsis thaliana | Nature

April 14, 2022

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

Biologically informed deep neural network for prostate cancer discovery | Nature

April 12, 2022

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 legacy free edition – Google Workspace Admin Help

February 14, 2022

G suite free appears to be ending
https://support.google.com/a/answer/2855120?hl=en

The Worst of Both Worlds: Zooming From the Office – The New York Times

December 5, 2021

https://www.nytimes.com/2021/11/16/business/return-to-office-hybrid-work.html

Learn How to Solve a Rubik’s Cube in 10 Minutes (Beginner Tutorial) – YouTube

November 23, 2021

https://www.youtube.com/watch?v=7Ron6MN45LY

A single-cell atlas of chromatin accessibility in the human genome: Cell

November 12, 2021

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

The triumphs and limitations of computational methods for scRNA-seq | Nature Methods

November 10, 2021

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)

https://www.nature.com/articles/s41592-021-01171-x