Pedro Ferreira

SpOTr integrates single-cell and spatial transcriptomics data by aligning evolutionary trees with spatial distributions to map cancer clones in tumors.

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.

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

LongSom detects somatic variants in LR scRNA-seq data without normal samples, revealing clonal heterogeneity in ovarian cancer.

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.

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.

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

This paper introduces two probabilistic models for scRNA-seq data analysis that improve scalability and robustness in dimensionality reduction and clustering.