Look into Raghavan et al about pertpy

[Zethson]

scBasal and scClassical programs

We first scored each malignant single cell for the basal-like and classical genes identified by Moffitt et al., 2015 as these were well described by unbiased analysis in our data (PCA, Figure S2B,C, S3B). To derive refined single-cell basal (scBasal) and single-cell classical (scClassical) signatures using our malignant cohort and determine programs associated with these cell states, we correlated the aforementioned basal and classical scores to the entire gene expression matrix containing malignant cells and identified the 1,909 genes significantly associated with either subtype (r > 0.1; > 3 SD above the mean for shuffled data, full data in Table S3). Biological pathway correlates for scBasal and scClassical are summarized in Figure S2E,F [WNT signaling (Kim et al., 2017); EMT (Gröger et al., 2012); cell cycle progression (Tirosh et al., 2016a)]. For visualization, we use the scBasal and scClassical genes (top 30 correlated genes for each). In Figure 2C we score single cells for EMT (Gröger et al., 2012) and the union of Hallmark and Reactome interferon response gene sets to show their divergence within cells expressing the scBasal state.
Intermediate co-expressor (IC) program

Ordering the cells by their polarization or “score difference,” simply the difference of the two scores, using these basal and classical scores related to PC1 and PC2 revealed a significant fraction of cells co-expressing intermediate levels of both cell states (Figure 2B, Figure S3A,B). Co-expressing cells showed associations with features across several additional PCs, but lacked a single dominant axis. To define a consensus set of genes that are preferentially expressed by coexpressing cells, we computed the Euclidean distance to the line representing equal basal and classical co-expression for each cell. To limit the influence of cell quality on this analysis and to specifically identify genes related to co-expression, we used cells from each group (basal, intermediate, and classical) with fractionally low mitochondrial genes (< 0.2) and non-zero basal or classical expression (basal or classical score > 0) and correlated their Euclidean distance (Figure S3C) to the entire gene expression matrix of malignant cells. Next, for each gene positively associated with this co-expressor state (Pearson’s r > 0), we subtracted the second highest correlation coefficient for each subtype-associated gene (basal and classical), and then re-ranked the matrix by this corrected value. This enriched for genes more specific to the co-expressor state by excluding those that were also associated with basal or classical programs. We then selected the 115 genes with a corrected correlation value > 0.1 (p < 0.00001, shuffled data) as our intermediate co-expressor (IC) signature (Figure S3D, Table S3). Single cells were classified based on Euclidian distance to co-expression, where cells with Euclidian distance < 0.2 are defined as intermediate co-expressor and the remainder (Euclidian distance > 0.2) by their maximal of either scBasal or scClassical scores. We binned each organoid cell (Figure 4C,D) by its maximal expression for one of the 3 in vivo scores (scBasal, scClassical, or IC). Here a cell must be within 1 SD of the mean expression for a given subtype in vivo, else it was considered “organoid-specific” as this program was superimposed on all organoid cells, regardless of their subtype identity (Figure 4C). We used these classifications to summarize overall sample malignant cell composition and visualize the groups. Tumor heterogeneity measures were not significantly affected by changing these cutoffs.

Code is not available and I asked Alex

[Zethson]

I never got followed up.