Browsing by Autor "Paula Siaucho"

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New algorithms for unsupervised cell clustering from scRNA-seq data
(2024) Melissa Robles; Jorge Díaz-Riaño; Cristhian Forigua; Soledad Ojeda; Laura Guio; Paula Siaucho; Jennifer J Guzmán-Porras; Danilo García-Orjuela; Andrés Naranjo; Silvia Maradei
Abstract The identification of cell types is a basic step of the pipeline for Single-Cell RNA sequencing data analysis. However, unsupervised clustering of cells from scRNA-seq data has multiple challenges: the high dimensional nature of the data, the sparse nature of the gene expression matrix, and the presence of technical noise that can introduce false zero entries. In this study, we introduce new algorithms for clustering scRNA-seq data. The first algorithm builds a k -MST graph from distances obtained directly from the input data without dimensionality reduction. The computation follows an iterative procedure of k steps in which each step calculates and stores the edges of minimum spanning trees over different subgraphs obtained removing edges selected in previous iterations. The Louvain algorithm is executed on the k -MST graph for cell clustering. We also explored alternatives based on neural networks in which an autoencoder is used to learn the parameters of a Gaussian mixture model, aiming to improve the handling of clusters with different shapes and sizes. Benchmark experiments with simulated data and public datasets show that the algorithms proposed in this work have competitive accuracy, compared to previous solutions, but also that sequencing depth, number of cells and tissue types have important effects on the performance of the algorithms. Moreover, we performed further experiments with scRNA-data taken from a patient with refractory epilepsy. The AE-GMM model achieved the best accuracy for this dataset, and the k -MST ranked first among methods that do not require previous information on the expected number of clusters.
Recent evolution, domestication and metabolism of cyanide compounds in Lima bean
(2025) Jorge Duitama; Erick Duarte; Tatiana García Navarrete; F. J. Pérez Zúñiga; Johanna Stepanian; Viviana Parra; Juan Pablo Londoño; Paula Siaucho; Edwin Bautista; Santiago Jiménez-Serrano
<title>Abstract</title> The evolution and functional genomics of the biosynthesis of secondary metabolites, including production of hydrogen cyanide (HCN), is a major goal in Lima bean research. This work describes our latest findings in short time evolution, genomics and expression, applied to Lima bean. This includes a chromosome-level assembly for the Andean gene pool and long-read sequencing of wild relatives. Large indels explained by transposable elements affect promoter regions of several genes related to domestication traits. The two major gene pools of P. lunatus diverged within the last million years, but recent insertions of LTRs produced important variations in genome size and composition. A core metabolic network for Phaseolus revealed patterns of variability in RNA expression for genes related to different primary and secondary metabolic processes. The Phaseolus genomes have important differences in the number, location, and expression of genes, which can explain the unique ability of Lima bean across domesticated Phaseolus species for production of HCN.