Adan Tanveer

Methods and techniques

Bench techniques from two years on colorectal cancer, and the computational methods used across the glioblastoma work and the systems oncology projects before it.

Bench

Cell and tissue culture

  • Patient-derived organoid culture
  • Human tumor tissue processing
  • Tissue dissociation
  • Mammalian cell culture
  • Aseptic technique and sterile culture
  • Transfection
  • Stable cell line generation

Molecular biology

  • PCR and qPCR
  • DNA and RNA extraction, purification and quantification
  • Gel electrophoresis
  • SDS-PAGE
  • Western blot sample preparation
  • Plasmid prep, miniprep and isolation
  • Bacterial culture

Cell-based assays

  • Flow cytometry and cell sorting
  • Cell cycle profiling
  • Apoptosis detection, Annexin V and propidium iodide
  • Surface marker phenotyping
  • ELISA and immunoassays
  • Cell viability assays
  • Proliferation assays
  • Migration assays

In vivo

  • PDX mouse models
  • CDX mouse models
  • Subcutaneous tumor implantation
  • Dosing
  • Caliper tumor volume measurement
  • Body weight monitoring
  • Endpoint tissue harvest and necropsy

PDX is a patient-derived xenograft, a mouse carrying tumor tissue taken from a patient. CDX is a cell line-derived xenograft, a mouse carrying tumor cells grown in the lab.

Data and public resources

Data types

  • Single-cell transcriptomics
  • Bulk transcriptomics
  • Spatial and anatomic region data
  • Proteomics
  • Multi-omics

Repositories and reference sets

  • TCGA
  • CGGA
  • Ivy GAP
  • CPTAC
  • GEO
  • GTEx
  • cBioPortal
  • Human Protein Atlas
  • MSigDB
  • Reactome
  • KEGG
  • Gene Ontology

TCGA is The Cancer Genome Atlas and CGGA is the Chinese Glioma Genome Atlas, both public collections of molecular data from patient tumors. Ivy GAP is an atlas of glioblastoma measurements labeled by where in the tumor they were taken. CPTAC is a public proteomics resource. GEO is the Gene Expression Omnibus.

Bulk RNA-seq

  • Quality control and preprocessing
  • Count normalization
  • Differential expression with DESeq2
  • edgeR
  • limma-voom
  • Batch correction
  • Volcano and MA plots
  • Clustered heatmaps
  • Principal component analysis
  • Kaplan-Meier survival analysis
  • Cox proportional hazards regression

Differential expression asks which genes change between two groups of samples. Kaplan-Meier and Cox regression relate those changes to how long patients survive.

Single-cell RNA-seq

  • Seurat
  • Scanpy
  • Quality control and filtering
  • Normalization
  • Integration across samples
  • Harmony
  • Clustering
  • UMAP
  • Cell type annotation
  • Marker gene identification
  • Myeloid and macrophage subclustering
  • Cell to cell communication with CellChat
  • CellPhoneDB
  • Trajectory and pseudotime with Monocle3
  • Slingshot

Single-cell RNA-seq measures gene activity one cell at a time, so a signal can be traced to the cell type producing it rather than to the tumor as a whole.

Signature scoring and enrichment

  • Gene set scoring with AddModuleScore
  • AUCell
  • UCell
  • GSVA
  • ssGSEA
  • GSEA
  • fgsea
  • Over-representation analysis with clusterProfiler
  • Gene ontology enrichment
  • Building a gene signature
  • Validating a signature in an independent dataset

A gene signature is a defined set of genes that stand for one biological program. Scoring measures how strongly that program is switched on in a sample or in a single cell.

Tumor composition

  • Bulk deconvolution with CIBERSORTx
  • xCell
  • MCP-counter
  • ESTIMATE tumor purity
  • Region-resolved signature mapping
  • Spatial transcriptomics analysis

Deconvolution estimates which cell types make up a bulk tumor sample, and in what proportion, from a single mixed measurement.

Statistics and networks

  • Regression analysis
  • Cluster analysis
  • Robustness and sensitivity analysis
  • Multiple testing correction and false discovery rate
  • Dimensionality reduction
  • WGCNA co-expression networks
  • Gene interaction network construction
  • Pathway model construction
  • Cellular circuit modeling
  • Multi-scale cancer simulation
  • Multi-omics integration

WGCNA groups genes that rise and fall together across samples, which finds programs without being told what to look for.

Software and languages

R

  • Seurat
  • DESeq2
  • edgeR
  • limma
  • clusterProfiler
  • fgsea
  • GSVA
  • WGCNA
  • survival
  • ggplot2
  • R Markdown

Python

  • Scanpy
  • pandas
  • NumPy
  • SciPy
  • scikit-learn
  • matplotlib
  • seaborn
  • Jupyter

Other languages

  • MATLAB
  • C++
  • JavaScript

Statistics and figures

  • SPSS
  • Minitab
  • GraphPad Prism

Networks, structure and platforms

  • Gephi
  • I-TASSER
  • SWISS-MODEL
  • PHYRE2
  • ChemDraw
  • TISON
  • Git
  • EndNote

I-TASSER, SWISS-MODEL and PHYRE2 predict the three-dimensional shape of a protein from its sequence. Gephi draws and analyzes networks. TISON is the multi-scale cancer modeling platform described on the research page.