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Ken Chen Laboratory

Ken Chen, Ph.D.

Principal Investigator

Areas of Research

Artificial Intelligence
Bioinformatics
Cancer Genomics
Computational Biology
Immunotherapy
Single-Cell Genomics
Systems Biology
Tumor Heterogeneity

Welcome to the Ken Chen Lab (KClab) site! KClab at MD Anderson focuses on developing and delivering cutting-edge computational approaches for characterizing and interpreting heterogeneous tumor specimens. In the context of translational research, the lab focuses on identifying molecular targets and algorithms that are potentially useful for personalized diagnosis and medicine. In addition to developing computational tools, the lab has applied and supported bioinformatics analyses and hypothesis generation/testing in The Cancer Genome Atlas, Human Cell Atlas, PreCancer Atlas, Moon Shots Program®, Cancer Target Discovery and Development (CTD2), and more. The lab has independent funding from the National Institutes of Health (NIH), Cancer Prevention and Research Institute of Texas (CPRIT), and Chan Zuckerberg foundations and also collaborates widely with oncologists, biotechnologists and cancer immunologists.

The Chen Laboratory is currently interested in the following research areas:

Single-cell/spatial transcriptomics, multiomics and statistics, such as

Monopogen: germline and somatic single nucleotide variant (SNV) calling from scRNA-seq, scATAC-seq, etc.
bindSC: integrating datasets obtained from different single-cell technologies including scRNA-seq, scATAC-seq, spatial omics and CyTOF
METAFlux: single-cell metabolomic flux from scRNA-seq
GSDensity: pathway/geneset-centric single-cell/spatial transcriptomic data analysis
SCMER: selecting most informative features/genes/proteins in a single-cell dataset.

Tissue clonal heterogeneity and evolution modeling:

Texomer, allele-specific copy number and RNA expression deconvolution of autologous DNA/RNA-seq data
SiFit, single-cell phylogenetic tree from somatic mutations detected in single cell sequencing data
MEDALT, single-cell phylogenetic tree from somatic copy number alterations detected in single cell sequencing data

Tumor-Immune systems biology (particularly, CAR-NK cell therapy)

Artificial intelligence in cancer research:

Cancer prognosis (e.g., MCL multiomics prognosis)
Protein structural-modeling (e.g., AlphaFold2)
Algorithms for big biomedical data analysis
Early detection and predictive modeling (e.g., ICU AKI/CRRD)
Regulatory network, e.g., DNA->RNA, RNA->protein
Relation extraction from biomedical literature

Structural and copy number alterations in human genomes, including:

BreakDancer, structural variant detection from NGS data
NovoBreak, de novo breakpoint assembly from NGS data

Systems/network biological approaches for molecular target discovery

Targeting transcriptional factors regulating DNA-RNA discordance in cancer

Clinical applications (including trial data analysis) and decision support:

ClinSeK, alignment-free, targeted genotyping/clonality characterization of clinical mutations
TransVar, trans-level annotation of mutations across DNA, RNA and proteins.

VISIT our github: https://github.com/KChen-lab

Postdoctoral and research assistant positions for graduates and undergraduates are often available. Please feel free to email Dr. Ken Chen to schedule a discussion.

Send an email of interest