Joshua M. Dempster

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

The paper uses Perturb-seq to analyze gene knockouts in cancer cell lines, identifying context-specific gene dependencies and transcriptional responses.

Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA

GeneTEA uses natural language processing to analyze gene descriptions and improve overrepresentation analysis by creating a de novo gene set database that better controls false discovery.

Controlling False Discovery in CRISPR Screens

This paper introduces Chronos, a module for estimating false discovery rates in CRISPR screens, showing it outperforms existing methods like MAGeCK in controlling false positives.

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data

This study benchmarks seven computational methods for correcting CN and proximity biases in CRISPR-Cas9 screens, finding AC-Chronos and CRISPRcleanR perform best in different scenarios.

Transcriptional plasticity drives leukemia immune escape

The paper identifies transcription factors regulating MHC-II expression in AML and shows their role in immune evasion and relapse.

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia

The paper identifies a modular core regulatory circuitry in AML that enforces oncogene expression in KMT2A-rearranged leukemias.

Integrated cross-study datasets of genetic dependencies in cancer

The paper integrates two large CRISPR-Cas9 screens to improve the detection of gene dependencies and cancer vulnerabilities by addressing biases and batch effects.

Chronos: a CRISPR cell population dynamics model

Chronos infers gene knockout fitness effects by explicitly modelling cell proliferation dynamics after knockout, using longitudinal readouts and correcting several known screen biases.

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The paper improves gene dependency estimation from RNAi screens by using model-based normalization and data integration, enhancing accuracy and enabling cross-dataset analysis.