Tools, Packages, and Public Data

CompBio Tools

• Python Library for handling genetic data: https://hail.is/docs/stable/index.html
• R package for handling genetic data: https://github.com/XiaoleiLiuBio/MVP
• SNPRelate: Parallel computing toolset for relatedness and principal component analysis of SNP data: https://github.com/zhengxwen/SNPRelate
• Tools for Genome-Wide Association Studies: http://bioconductor.org/packages/release/bioc/html/GWASTools.html
• R package to access TCGA data: https://github.com/BioinformaticsFMRP/TCGAbiolinksGUI
• R package to visualize genomic data: https://github.com/griffithlab/GenVisR
• Read PLINK in R -- method (1): https://www.bioconductor.org/packages/release/bioc/html/snpStats.html
• Read PLINK in R -- method (2): https://github.com/QuantGen/BEDMatrix
• Read PLINK in R -- method (3) -- Not good for big data: https://cran.r-project.org/web/packages/MultiPhen/index.html
• Read PLINK in R -- method (4): https://github.com/gabraham/plink2R
• Read PLINK in R -- method (5): https://cran.r-project.org/web/packages/BGData/BGData.pdf
• Fast Gene Set Enrichment Analysis : https://github.com/ctlab/fgsea

Deep Learning Repos

• Good repo on various VAE/GAN: https://github.com/wiseodd/generative-models
• UNet in PyTorch: https://github.com/shiba24/3d-unet
• Library for Generative Models using PyTorch: https://github.com/uber/pyro

Learning Material

• Intro to RL: http://joschu.net/#presentations
• Course Slides about RL: https://mila.umontreal.ca/en/cours/deep-learning-summer-school-2017/slides/
• Papers about GAN: https://github.com/nightrome/really-awesome-gan

Python and Jupyter

• Interactive Programming with C++ in Jupyter: https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92
• An amazingly simple tool to develop command line interface: http://docopt.org/
• Interactive 2D visualization in Jupyter: https://github.com/bloomberg/bqplot
• Interactive 3D visualization in Jupyter: https://github.com/maartenbreddels/ipyvolume
• Combine Using C and Python: https://github.com/tmcclintock/cffi_example
• Gallery of Plots in Python: http://pythonplot.com/
• Make presentation with IPython Notebook: https://github.com/datitran/jupyter2slides
• Collaborative IPython Notebook: g.co/colab

Natural Language Processing Tools

• https://spacy.io/
• Amazon pipeline for medical text (Amazon Link)

Causal Discovery:

• R package for causal effect: https://arxiv.org/pdf/1806.07161.pdf
• Python package for causal data challenge: https://github.com/kevinmcgregor/CausalChallenge2016

Public Data for Segmentation

• Medical Segmentation Decathlon: http://medicaldecathlon.com

Misc

• Connect to R running in a remote server: https://github.com/cloudyr/rmote