New Figure 3 - Githubissues

LieberInstitute / spatialDLPFC

spatialDLPFC project involving Visium (n = 30), Visium SPG (n = 4) and snRNA-seq (n = 19) samples

http://research.libd.org/spatialDLPFC/

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New Figure 3 #123

Closed abspangler13 closed 1 year ago

abspangler13 commented 2 years ago

[ ] https://docs.google.com/presentation/d/1bf4RbkSyC6q6NhmWsU6vL5QvMPtjSqTyPpkH3bf0BzI/edit#slide=id.g13f84791fb2_0_45

lcolladotor commented 2 years ago

Here I think that we'll need to meet @lahuuki, @kmaynard12 and myself to clarify a bit more where each of these pieces are so Louise can assemble them and/or make the sub-plots. Analysis wise, I think that we have all the data thanks to Abby.

abspangler13 commented 2 years ago

Yes, all the data is saved and some of the plots are already made. The spot plots are made and the boxplots can be exported for the shiny app. nnSVG plots are made for top 20 svgs so just depends on which ones you're want to show.

lcolladotor commented 2 years ago

Cool, thanks Abby!

lahuuki commented 2 years ago

Layer 1 & Layer 6 are both split in k9 & k16. Identify interesting genes in pairwise DE

k9 highlight layer 1
k16 highlight layer 6

lcolladotor commented 2 years ago

Related to why a pairwise t-stat might not pick up something that nnSVG might

lahuuki commented 2 years ago

From @kmaynard12:

For k=9, focus on meninges genes: CLDN5, TAGLN, MYL9, ACTA2, SLC2A1, HBA1, EPAS1 For k=16, focus on layer "1a" vs. "1B": 1a -SPARC, MSX1 (BBB?), 1b - RELN, APOE For paper, we can add additional k=16 focused on layer 6A vs. 6B: 6a - SMIM32, DACH1, KIF1A (poor layer resolution in manual), GALNT14, 6b- KRT17, DIRAS2, SEMA3E

lcolladotor commented 1 year ago

I'm closing this issue since it's no longer an accurate representation of what we are doing. Though feel free to re-open it @lahuuki