matanhofree/lungTumorEvolution

Algorithms and code developed as part of the study described in Marjanovic et al.

Lung Tumor Evolution

"Emergence of a High-Plasticity Cell State during Lung Cancer Evolution"

Nemanja Despot Marjanovic, Matan Hofree, Jason E. Chan, ..., Tyler Jacks, Aviv Regev, Tuomas Tammela, Cell, 2019

10.1016/j.ccell.2020.06.012

The repository contains the princple computational methods and analysis scripts used in [1] Including code for the following analysis (Matlab):

• Basica single cell analysis workflow
  • Identify over-dispersed genes
  • NMF
  • Graph cluster
  • tSNE and PHATE 2D representations
• Genewise eCDF normalization
• NMI
• NMF-WOT
• Single cell gene signatures
• Consensus NMF and gsea of characteristic genes

Getting started

Software requirements and dependencies

• Matlab (code was tested with R2020a)
• R
• (Optional) Jupyter notebook with matlab kernel -- view and manipulate ".ipynb" notebooks
• (Optional) google cloud SDK

Clone repository:

git clone https://github.com/matanhofree/lungTumorEvolution.git
cd lungTumorEvolution

Download data:

• Login and download from the single cell portal:
  https://singlecell.broadinstitute.org/single_cell/data/public/SCP971/emergence-of-a-high-plasticity-cell-state-during-lung-cancer-evolution-timecourse-smartseq2?filename=lungTE_data.tar.gz

• Download data from the public google bucket:
  gsutil -u ADD GOOGLE USER PROJECT cp gs://regev-lung-tumorevo-mm-public/lungTE_data.tar.gz .
  tar xzvf lungTE_data.tar.gz
  

Main figure files:

• Figure 1 (Updated: 2/21/21)
• Figure 2 (Updated: 2/21/21)
• Figure 3 (Updated: 2/21/21)

Under construction (Last update 2/21/21)