Single cell analysis on Melanoma (PMID:27124452)

Paper: Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016
Organism: Homo sapiens
Cell type: CD45+/- cells (Melanoma, T-cells, B-cells, Macrophages, NK cell, CAFs and Endothelial cells)
Sample number: 4,645
Library preparation: Smarter
DATA: GSE72056

  1. 25PE/Bowtie(1.4.1) -n 0 -e 99999999 -l 25 -I 1 -X 2000 -a -m 15 –S/RSEM
  2. Focus on across all cells combined (average log2(TPM)>4.5) or within
    a single tumor (average log2(TPM)>6 in at least one tumor)

  3. exclude from analysis any cells that did not express at least 2,000 of these 5,948 genes
  4. Inference of CNVs from RNA-seq
  5. tSNE followed by density clustering.
  6. Controlling for inter-tumor differences (???)
  7. PCA (PC1 is technical, PC2 is cell cycle, )
  8. CCND3 in high cell cycle, KDM5B non cycle
  9. PC3&6 is location, PC4&5 is MITF
  10. Variability of T-cell exhaustion related genes (reflect treatment response etc)

Proliferation vs Differentiation in CD4 Th2 condition in vivo&vitro (PMID: 27176874)

Paper: Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation. Genome Biology. (2016)
Organism: Mus Musculus
Cell type: CD4 T-cell in vivo and vitro
Sample number: 287
Library preparation: Smarter/C1, 75PE

  1. mapped to Ensembl38.70 combined with ERCC / GSNAP / HTSeq / TPM / Poor quality cells are detected by mapping on 37 genes on the mitochondrial genome
  2. Continue reading