Jack Kuipers

Reliable single-cell perturbations explain and improve model performance

The paper evaluates perturbation reliability in single-cell data and shows that using reliable perturbations improves model performance and reduces training data needs.

Clone-level multi-modal prediction of tumour drug response

This paper introduces scClone2DR, a framework predicting tumour drug response at the clone level using single-cell DNA/RNA sequencing and drug-screening data, showing improved accuracy in identifying clonal sensitivities and resistance.

Phylogenetic tree inference from single-cell RNA sequencing data

This paper introduces a method for inferring phylogenetic trees from single-cell RNA sequencing data by alternating between two tree representations to avoid local optima.

DelSIEVE: cell phylogeny model of single nucleotide variants and deletions from single-cell DNA sequencing data

DelSIEVE infers cell phylogeny and variants from scDNA-seq data, improving detection of deletions and evolutionary events compared to existing methods.

Probabilistic cell lineage inference from mitochondrial mutations

mt-SCITE reconstructs cell lineages from mitochondrial mutations, outperforming existing methods and revealing subclonal relationships in CD8+ T cells post-vaccination.

Bayesian inference of fitness landscapes via tree-structured branching processes

FiTree infers fitness landscapes from single-cell tumor mutation trees using Bayesian methods, outperforming existing models and identifying epistatic effects in leukemia data.

DemoTape: Computational demultiplexing of targeted single-cell sequencing data

DemoTape is a computational method for demultiplexing targeted single-cell DNA sequencing data, showing improved performance over existing methods for RNA sequencing data.

Joint copy number and mutation phylogeny reconstruction fromsingle-cell amplicon sequencing data

This paper introduces COMPASS, a method to jointly reconstruct mutation and copy number phylogenies from single-cell amplicon sequencing data, showing it detects both clonal and subclonal alterations in leukemia patients.

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

SIEVE jointly infers somatic variants and cell phylogeny from scDNA-seq data, showing improved accuracy in both tasks compared to existing methods.

Identifying hierarchical cell states and gene signatures with deep exponential families for single-cell transcriptomics

The paper introduces scDEF, a Bayesian model for single-cell RNA-seq data that identifies hierarchical cell states and improves gene signature detection across batches.

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

The paper introduces bnClustOmics, a Bayesian network mixture model for multi-omics clustering, identifies three HCC subtypes with survival associations, and learns cluster-specific molecular networks.

Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence

This paper presents a Bayesian method to call mutations and reconstruct phylogenetic trees from single-cell tumour sequencing data, accounting for mutational loss and recurrence.

scAmpi - A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

scAmpi provides a clinical workflow for scRNA-seq analysis, extracting clinically relevant information and supporting drug treatment identification.

scROSHI - robust supervised hierarchical identification of single cells

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

This paper develops a Bayesian framework for learning dynamic Bayesian networks from time series gene expression data to discover gene regulatory networks in multiple phenotypic groups, showing improved classification accuracy in cancer and therapy response datasets.

Within-patient genetic diversity of SARS-CoV-2

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

This paper develops a computational method to infer tumour evolution trees from combined single-cell and bulk sequencing data, showing improved accuracy in subclone identification and mutation history reconstruction.

SCIΦ: Single-cell mutation identification via phylogenetic inference

SCIΦ identifies mutations in single-cell tumor data by inferring phylogenetic relationships, outperforming existing methods on simulated and real datasets.

Improved pathway reconstruction from RNA interference screens by exploiting off-target effects

The paper improves pathway reconstruction from RNAi screens by incorporating off-target effects into nested effects models, enhancing network accuracy.

Tree inference for single-cell data

SCITE is a stochastic algorithm for inferring tumor mutation histories from single-cell data, showing improved accuracy and scalability.