https://pubmed.ncbi.nlm.nih.gov/12701331/
Barabási, A., & Bonabeau, E. (2003). Scale-Free networks. Scientific American, 288(5), 60–69.
https://doi.org/10.1038/scientificamerican0503-60
https://pubmed.ncbi.nlm.nih.gov/12701331/
Barabási, A., & Bonabeau, E. (2003). Scale-Free networks. Scientific American, 288(5), 60–69.
https://doi.org/10.1038/scientificamerican0503-60
https://pmc.ncbi.nlm.nih.gov/articles/PMC6687398/
(Description of cell type annotation. See fig. 1, which is explained in the beginning of Results section)
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019). Comprehensive integration of Single-Cell data. Cell, 177(7), 1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031
https://www.nature.com/articles/nmeth.3971
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016). Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods, 13(10), 845–848. https://doi.org/10.1038/nmeth.3971
(first page summarizes the Pseudotime algorithm)
https://www.nature.com/articles/s41596-020-00409-w
(Single Cell overview; goes over every step)
Andrews, T. S., Kiselev, V. Y., McCarthy, D., & Hemberg, M. (2020). Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nature Protocols, 16(1), 1–9.
https://doi.org/10.1038/s41596-020-00409-w
g accounts for the cumulative effect of all other variants on the phenotype besides the effect of the specific variant being tested (SNP s).
Although theoretically we should consider the effect of g when testing for GWAS associations, in practice don’t think this happens in standard GWAS tools, such as PLINK and REGENIE (see below).
PLINK: https://www.cog-genomics.org/plink/2.0/assoc

REGENIE: https://www.nature.com/articles/s41588-021-00870-7#Sec10
Here are some links for ICA deconvolution on omics data:
https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-019-0578-4 https://urszulaczerwinska.github.io/UCzPhDThesis/deconica.html https://academic.oup.com/nar/article/49/9/e54/6146629
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6751789/
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008647
Just stumbled on this. Amazing how well GPT-4 seems to do on AP tests. One notable exception is AP English. I wonder why?
https://twitter.com/mattgroh/status/1635755153830584321
https://cdn.openai.com/papers/gpt-4.pdf
Test results that gpt4 can get; Doesn’t do well in ap English