Biology subjects

Altenbuchinger, M. C.

Publications and source records attributed to Altenbuchinger, M. C..

3 recordsLinked to original sources

bioRxiv2024

Deconvolution of omics data in Python with Deconomix -- cellular compositions, cell-type specific gene regulation, and background contributions

BackgroundGene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular composition, as differences in gene expression may arise from both variations in cellular composition and gene regulation. ResultsWe present Deconvolution of omics data (Deconomix) - a comprehensive toolbox for the cell-type deconvolution of bulk transcriptomics data, available as a Python package and standalone graphical user interface. Deconomix stands apart from competing solutions with rich functionality and highly efficient implementations. It facilitates (A) the inference of cellular compositions from bulk transcriptomics data, (B) the machine learning-based optimization of gene weights to resolve small cell populations and to disentangle phenotypically related cells, (C) the inference of background contributions which otherwise would deteriorate cell-type deconvolution, and (D) population estimates of cell-type specific gene regulation. To showcase the application of Deconomix, we present a case study on breast cancer data from TCGA, highlighting subtype-specific cellular compositions and cell-type-specific gene-regulatory programs. ConclusionWe present Deconomix, a comprehensive Python package including a graphical user interface for the inference of cellular compositions, cell-type-specific gene regulation, and background contributions from bulk transcriptomics data. O_TEXTBOXKey PointsO_LIDeconomix optimizes gene weights to disentangle small cell populations and phenotypically related cells. C_LIO_LIDeconomix estimates cell compositions, unknown background contributions and cell-type specific gene regulation from bulk transcriptomics data. C_LIO_LIA Python package is available as code repository and installable via PyPI [1]. C_LIO_LIA standalone graphical user interface is available as code repository [2]. C_LIO_LIAn exemplary analysis of a breast cancer case study is provided as tutorial [3]. C_LI C_TEXTBOX

bioinformatics↗

bioRxiv2024

CODEX: COunterfactual Deep learning for the in-silico EXploration of cancer cell line perturbations

MotivationHigh-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. ResultsWe propose CODEX as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in-silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. Availability and ImplementationOur implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.

bioinformatics↗

bioRxiv2023

Virtual Tissue Expression Analysis

MotivationBulk RNA expression data is widely accessible, whereas single-cell data is relatively scarce in comparison. However, single-cell data offers profound insights into the cellular composition of tissues and cell-type-specific gene regulation, both of which remain hidden in bulk expression analysis. ResultsHere, we present tissueResolver an algorithm designed to extract single-cell type information from bulk data, enabling us to attribute expression changes to individual cell types. The outcome is a virtual tissue that can be analyzed in a manner similar to single-cell RNA-seq data. When validated on simulated data tissueResolver outperforms competing methods. Additionally, our study demonstrates that tissueResolver reveals previously overlooked celltype specific regulatory distinctions between the activated B-cell-like (ABC) and germinal center B-cell-like (GCB) subtypes of diffuse large B-cell lymphomas (DLBCL). Availability and ImplementationR package available at https://github.com/spang-lab/tissueResolver. Code for reproducing the results of this paper is available at https://github.com/spang-lab/tissueResolver-docs. Contactjakob.simeth@klinik.uni-regensburg.de

bioinformatics↗