GitHub - Nanostring-Biostats/SpatialDecon-manuscript-analyses: Holds the code and data to reproduce the analyses of Danaher & Kim et a;, "Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data"

Each folder contains data, code and results underlying an analysis in the paper.

For Fig 3, which used all of TCGA's RNAseq data, only code is posted.

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Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
fig 2 - cell pellet array analysis		fig 2 - cell pellet array analysis
fig 3 - gene rates in tumors		fig 3 - gene rates in tumors
fig 4 - serial sections validation		fig 4 - serial sections validation
fig 5 - NSCLC tumor analysis		fig 5 - NSCLC tumor analysis
fig 6 - reverse decon		fig 6 - reverse decon
fig S1 - lognormal distribution in TCGA		fig S1 - lognormal distribution in TCGA
fig S10 - outlier removal in kidney and pancreas		fig S10 - outlier removal in kidney and pancreas
fig S11 - impact of G in merging tumor profiles		fig S11 - impact of G in merging tumor profiles
fig S12 - analysis of ST data from Andersson 2020		fig S12 - analysis of ST data from Andersson 2020
fig S13 - analysis of example Visium data		fig S13 - analysis of example Visium data
fig S2 - lognormal distribution in geomx		fig S2 - lognormal distribution in geomx
fig S3 - lognormal distribution in healthy tissues		fig S3 - lognormal distribution in healthy tissues
fig S8 - sensitivity to epsilon		fig S8 - sensitivity to epsilon
fig S9 - impact of outlier removal in NSCLC		fig S9 - impact of outlier removal in NSCLC
fig s5 - performance in small regions		fig s5 - performance in small regions
fig s6 and s7 - granular cell types in NSCLC		fig s6 and s7 - granular cell types in NSCLC
README.md		README.md