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MangiolaLaboratory / sccomp

Testing differences in cell type proportions from single-cell data.
https://stemangiola.github.io/sccomp/
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Download real-world datasets (possibly cell cluster and subject covariate labels), for benchmarking purposes. #13

Closed stemangiola closed 9 months ago

stemangiola commented 3 years ago

If you dig in their github repository you will likely find the data already summarised for you in form of R script for reproducibility, so you have to avoid the 95% of work in getting raw single-cell data.

Used by this article: https://www.pnas.org/content/118/22/e2100293118

Segerstolpe A, Palasantza A, Eliasson P, Andersson EM, Andreasson AC, Sun X, Picelli S, Sabirsh A, Clausen M, Bjursell MK, Smith DM, Kasper M, Ammala C, Sandberg R. Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes. Cell Metab. 2016; 24(4):593–607.

Delile J, Rayon T, Melchionda M, Edwards A, Briscoe J, Sagner A. Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord. Development. 2019. https://doi.org/10.1242/dev.173807.

M. Sade-Feldman et al., Defining t cell states associated with response to checkpoint immunotherapy in melanoma.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120575

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122043

K. Gupta et al., Single-cell analysis reveals a hair follicle dermal niche molecular differentiation trajectory that begins prior to morphogenesis. Dev. Cell 48, 17–31 (2019).

X. Fan et al., Single cell and open chromatin analysis reveals molecular origin of epidermal cells of the skin. Dev. Cell 47, 21–37 (2018).

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE102086

https://ndownloader.figshare.com/files/22927382

R. L. Chua et al., Covid-19 severity correlates with airway epithelium–immune cell interactions identified by single-cell analysis. Nat. Biotechnol. 38, 970–979 (2020).

M. Liao et al., Single-cell landscape of bronchoalveolar immune cells in patients with covid-19. Nat. Med. 26, 842–844 (2020)

https://cells.ucsc.edu/covid19-balf/nCoV.rds

https://singlecell.broadinstitute.org/single_cell/study/SCP263/aging-mouse-brain#/

M. Ximerakis et al., Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).

Used by this article: https://www.biorxiv.org/content/10.1101/2020.12.14.422688v1.full

https://singlecell.broadinstitute.org/single_cell/study/SCP259

https://github.com/zhangzlab/covid_balf

https://singlecell.broadinstitute.org/single_cell/study/SCP44

stemangiola commented 3 years ago

Hello @jeffreypullin,

I tag you here in case you feel contributing to this point. @CastielZhao also plans to approach this point of course.

I created two directories

I believe that we can fish many of the cluster_counts directly from the Github repositories of the above-mentioned studies, while the RNA counts would be useful to do a proper clustering-dependent study and bring this article to the next level.

Of course, everyone feel free to add datasets to the above list

jeffreypullin commented 3 years ago

Hi @stemangiola

Sorry about my slow response!

Unfortunately, I won't have much bandwidth to work on this at the moment, but I'll look into it when I can.

Best, Jeffrey

stemangiola commented 3 years ago

Please Zijie and Mengyao edit this comment writing your progress on what datasets you are working on.