WANG_Di's repositories
BE-GIM_Comparison
Single-Cell RNA Sequencing Unifies Developmental Programs of Esophageal and Gastric Intestinal Metaplasia
ACC_Project
Intratumor microbes in adrenocortical carcinoma
cancer-proteomics-compendium-n2002
We assembled a compendium dataset of mass-spectrometry-based proteomics data of 2002 primary tumors from 14 cancer types and 17 studies
Code_for_circSC
Analysis pipeline for our circSC manuscript
COPD_multiomics
This repository contains computer codes for main analyses of the manuscript titled 'Multi-omic Landscape of Airway Microbe-Host Interaction in Chronic Obstructive Pulmonary Disease'.
ecotyper
EcoTyper is a machine learning framework for large-scale identification of cell states and cellular ecosystems from gene expression data.
host_gene_microbiome_interactions
Analysis framework for integrating gut microbiome and host gene expression data
ISLR-Exercises
Exercises of 'An Introduction to Statistical Learning with Applications in R (V1)'
MFP
Conserved pan-cancer microenvironment subtypes predict response to immunotherapy
MIMESIS
A package to identify type/subtype of tumor samples and estimate tumor content using minimal DNA-methylation signatures
ml4calibrated450k
This is a companion repository for the article "Comparative analysis of machine learning classifiers and calibration algorithms for estimating class probabilities for personalized cancer diagnostics on DNA methylation microarray data"
organ-site-mapping
Mapping ICD billing codes and terms to a standard ontology
pan-cancer-host-microbiome-associations
Manuscript for pan-cancer-host-microbiome-associations
pancancer_prot_act
Pan‐Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival
PEACE_melanoma_14_paper
Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
sygnal
SYstems Genetic Network AnaLysis (SYGNAL) pipeline
tcga-microbiome-prediction
Predictive models and analysis of cancer prognosis and drug response using primary tumor microbial abundances derived from WGS and RNA-seq sequencing data for 32 TCGA cancers (Poore et al. Nature 2020), including equivalent models using TCGA RNA-seq gene expression and combined microbial abundance and gene expression for comparison.
Tumor-cellular-states-and-ecosystems-in-CRC
Deep immunophenotyping reveals clinically distinct cellular states and ecosystems in large-scale colorectal cancer