Biology subjects

Ferreira, P. F.

Publications and source records attributed to Ferreira, P. F..

6 recordsLinked to original sources

bioRxiv2026

DisCell: Separating Spillover from Niche Response in Spatial Transcriptomics

Spatial transcriptomics at single-cell resolution measures every cell in its place in the tissue, but diffusion and segmentation errors also add transcripts of neighboring cells to a cell's counts. This spillover makes a cell resemble its neighbors, and a response to the niche makes cells in the same niche resemble each other. Both tie a cell's expression to its surroundings. The count data alone cannot tell the two apart. Findings from single-cell transcriptomes, such as a gene program that is higher in one niche than in another or signaling genes that change depending on the neighborhood, may therefore be artifacts of spillover. Current models describe a cell by its intrinsic state together with either its niche response or spillover, but none models all three. We present DisCell, the first method that models all three components jointly and measures how robust each finding is to spillover. DisCell is a generative model that describes single-cell gene expression profiles in terms of two latent variables, an intrinsic state and a response to the niche, and adds spillover from its neighbors as a separate term. DisCell fits the model at increasing levels of spillover and recomputes each finding on the spillover-free expression to see how much spillover it withstands. DisCell also estimates the most spillover a section can contain, and a finding that holds even at that amount is not explained by spillover. In simulated tissue, DisCell recovers each cell's intrinsic state and response and tells findings created by spillover apart from genuine responses. On five sections of ovarian and lung tissue, the intrinsic state keeps more of each cell's own properties, measured by its cell-cycle phase, than any competing method that holds as little of its neighborhood. Within each cell type, the model partitions the variation of each gene into intrinsic state, niche response and spillover. Using DisCell, we rank findings about niches by their robustness to spillover and confirm that a previously reported finding withstands spillover, that esophageal cancer cells in niches infiltrated by immune or stromal cells express more of an epithelial-mesenchymal transition program. DisCell also predicts what the gene expression of a cell type would be in another niche, with its spillover part separated, at lower error on most tasks than the competing method built for this purpose. Training the model takes under ten minutes on one consumer GPU for sections of up to 400,000 cells.

bioinformatics↗

bioRxiv2026

Clone-level multi-modal prediction of tumour drug response

Tumour heterogeneity presents a major challenge for precision oncology, as genetically and phenotypically distinct tumour clones may respond differently to therapy. To address this, we introduce scClone2DR, a probabilistic multi-modal framework that predicts drug responses at the level of individual tumour clones by integrating single-cell DNA and RNA sequencing with ex-vivo drug-screening data. In simulations, scClone2DR substantially outperforms alternatives in recovering true drug-effects and clonal sensitivities. Applied to 60 melanoma and 21 acute myeloid leukaemia patient samples, the method identifies heterogeneous clonal responses, yields biologically meaningful feature rankings, highlights clones that may be resistant to treatment, and improves the prediction of clinical outcomes compared to models ignoring clonal structure. These results demonstrate that modelling tumour evolution and clonal diversity is crucial for accurate drug-response prediction and provides a foundation for more effective, clone-aware precision oncology.

cancer biology↗

bioRxiv2024

De novo detection of somatic variants in long-read single-cell RNA sequencing data

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variants detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

cancer biology↗

bioRxiv2022

Deep exponential families for single-cell data analysis

Single-cell gene expression data characterizes the complex heterogeneity of living systems. Tissues are composed of various cells with diverse cell states driven by different sets of genes. Cell states are often related in a hierarchical fashion, for example, in cell differentiation hierarchies. Clustering which respects a hierarchy, therefore, can improve functional interpretation and be leveraged to remove noise and batch effects when inferring gene signatures. For this task, we present single-cell Deep Exponential Families (scDEF), a multi-level Bayesian matrix factorization model for single-cell RNA-sequencing data. The model can identify hierarchies of cell states and be used for dimension reduction, gene signature identification, and batch integration. Additionally, it can be guided by known gene sets to jointly type cells and identify their hierarchical structure, or to find higher resolution states within the provided ones. In simulated and real data, scDEF outperforms alternative methods in finding cell populations across biologically distinct batches. We show that scDEF recovers cell type hierarchies in a whole adult animal, identifies a signature of response to interferon stimulation in peripheral blood mononuclear cells, and finds both patient-specific and shared cell states across nine high-grade serous ovarian cancer patients.

bioinformatics↗

bioRxiv2021

Mapping single-cell transcriptomes to copy number evolutionary trees

Cancer arises and evolves by the accumulation of somatic mutations that provide a selective advantage. The interplay of mutations and their functional consequences shape the evolutionary dynamics of tumors and contribute to different clinical outcomes. In the absence of scalable methods to jointly assay genomic and transcriptomic profiles of the same individual cell, the two data modalities are usually measured separately and need to be integrated computationally. Here, we introduce SCATrEx, a statistical model to map single-cell gene expression data onto the evolutionary history of copy number alterations of the tumor. SCATrEx jointly assigns cancer cells assayed with scRNA-seq to copy number profiles arranged in a copy number aberration tree and augments the tree with clone-specific clusters. Our simulations show that SCATrEx improves over both state-of-the-art unsupervised clustering methods and cell-to-clone assignment methods. In an application to real data, we observe that SCATrEx finds inter-clone and intra-clone gene expression heterogeneity not detectable using other integration methods. SCATrEx will allow for a better understanding of tumor evolution by jointly analysing the genomic and transcriptomic changes that drive it.

cancer biology↗

bioRxiv2018

Scalable probabilistic matrix factorization for single-cell RNA-seq analysis

MotivationThe gene expression profile of a cell dictates its function in molecular processes, and can be used to probe its health status. This represents a step forward in the deep characterization of diseases such as cancer and may lead to breakthroughs in their treatment. The technology used to measure the gene expression of isolated cells, single-cell RNA-seq (scRNA-seq), has emerged in the last decade as a key enabler of this progress. However, the use of existing methods for dimensionality reduction, clustering and differential expression is limited by the specificities of the data obtained from scRNA-seq experiments, where technical factors may confound analyses of the true biological signal and contribute to spurious results. To overcome this issue, a possible approach is designing probabilistic generative models of the data with hidden variables encoding different underlying processes.\n\nResultsWe propose two novel probabilistic models for scRNA-seq data: modified probabilistic count matrix factorization (m-pCMF) and Bayesian zero-inflated negative binomial factorization (ZINBayes). These build upon previous models in the literature while leveraging scalable Bayesian inference via variational methods. We show that the proposed methods are competitive with the state-of-the-art models for robust dimensionality reduction in modern data sets, and improve upon the current best Bayesian model for small numbers of cells. The results show that building probabilistic models of latent variables which encode domain knowledge and using variational inference constitute a promising approach to analyse scRNA-seq data in a scalable way.\n\nAvailabilitym-pCMF and ZINBayes are publicly available as Python packages at https://github.com/pedrofale/, along with the code to reproduce all the results.\n\nContactsusanavinga@tecnico.ulisboa.pt

bioinformatics↗