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Method: 'Differential expression analysis' part

Data: 'GSE120575_Sade_Feldman_melanoma_single_cells_TPM_GEO.txt', Gene expression values of 6,350 CD8+ T cells (cell ID's found in Table S2)

Truth: Table S2

Results:

1. Applied Fisher's Exact test for corresponding table, respose & Non-response (results can be found in '/Results/pvalueSort.csv')
2. Applied Fisher's Exact test for corresponding table, CD8_B & CD8_G (code can be found in 'Identify_markers.rmd')

head(pvalueSort)

Method: 'Survival analysis' part

Data: Table S3, including patients' survival data, patients status and immunofluorescence-generated TCF7+CD8+/TCF7−CD8+ ratios.

Truth: Figure. 3H

Results: Kaplan-Meier survival curve for 33 patients treated with anti-PD-1 therapy. Patients were divided into two groups based on TCF7+CD8+/TCF7−CD8+ ratio (n = 16 > 1; n = 17 < 1) from IF.

Method: 'Unsupervised clustering of immune cells' parts

Data: Using all genes with variance > 6, yielding ∼4000 genes. /Results/clusterData.csv

Truth: Table S1, S2, S4

Results: Determine the optimal number of clusters

• Step 1 Applied the elbow method

Examined how much of the complexity each cluster captures by applying the elbow method Select the solutions that are near plateau (k = 10,..., 15)

• Step 2 Performed differentical expression

Performed differentical expression anlysis to search for gene markers that are siginificatly more highly expressed in a specific cluster as compared to all other clusters (excluded solutions with clusters that have too few marker genes (< 20) distinguishing between them and the rest of the cells.)

• Step 3 Robustness analysis

Finally, we performed a robustness analysis and selected the clustering solution with the highest median robustness score.

Method: 'Unsupervised clustering of T cells' parts, same as above

Data: Gene expression values of 6,350 CD8+ T cells (cell ID's found in Table S2)