Biology subjects

Chrysinas, P.

Publications and source records attributed to Chrysinas, P..

4 recordsLinked to original sources

bioRxiv2025

SCREAM: Single-cell Clustering using Representation Autoencoder of Multiomics

MotivationSingle-cell multiomics technologies offer unprecedented opportunities to study cellular heterogeneity. But, integrating information across different omics modalities remains a major challenge due to high dimensionality, sparsity, and modality-specific noise characteristics. To address this, we develop SCREAM (Single-cell Clustering using Representation Autoencoder of Multiomics), a novel deep learning framework for the robust integration and clustering of multi-modal single-cell data. SCREAM leverages Stacked Autoencoders (SAEs) to generate robust latent representations for each omics modality as well as for their fusion. Subsequently, borrowing Deep Embedding Clustering (DEC), SCREAM iteratively fine tunes the integrated mulitomics latent space and single-cell cluster assignments. ResultsWe evaluated SCREAM against eleven state-of-the-art methods using SNARE-seq and CITE-seq datasets. In this benchmarking, SCREAM consistently demonstrated superior performance, yielding the highest or near-highest Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) scores on both datasets. These findings validate SCREAM as a highly accurate and robust approach for identifying cell types from multiomics data. Furthermore, its multiomics embeddings provides biologically meaningful latent representations for diverse downstream analyses. AvailabilitySCREAM is available at http://www.github.com/cabsel/scream. Contactrgunawan@buffalo.edu

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bioRxiv2023

Cell and tissue-specific glycosylation pathways and transcriptional regulation informed by single-cell transcriptomics

While single cell studies have made significant impacts in various subfields of biology, they lag in the Glycosciences. To address this gap, we analyzed single-cell glycogene expressions in the Tabula Sapiens dataset of human tissues and cell types using a recent glycosylation-specific gene ontology (GlycoEnzOnto). At the median sequencing (count) depth, [~]40-50 out of 400 glycogenes were detected in individual cells. Upon increasing the sequencing depth, the number of detectable glycogenes saturates at [~]200 glycogenes, suggesting that the average human cell expresses about half of the glycogene repertoire. Hierarchies in glycogene and glycopathway expressions emerged from our analysis: nucleotide-sugar synthesis and transport exhibited the highest gene expressions, followed by genes for core enzymes, glycan modification and extensions, and finally terminal modifications. Interestingly, the same cell types showed variable glycopathway expressions based on their organ or tissue origin, suggesting nuanced cell- and tissue-specific glycosylation patterns. Probing deeper into the transcription factors (TFs) of glycogenes, we identified distinct groupings of TFs controlling different aspects of glycosylation: core biosynthesis, terminal modifications, etc. We present webtools to explore the interconnections across glycogenes, glycopathways, and TFs regulating glycosylation in human cell/tissue types. Overall, the study presents an overview of glycosylation across multiple human organ systems.

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bioRxiv2023

CrossTx: Cross-cell line Transcriptomic Signature Predictions

MotivationPredicting the cell response to chemical compounds is central to drug discovery, drug repurposing, and personalized medicine. To this end, large datasets of drug response signatures have been curated, most notably the Connectivity Map (CMap) from the Library of Integrated Network-based Cellular Signatures (LINCS) project. A multitude of in silico approaches have also been formulated to leverage drug signature data for accelerating novel therapeutics. However, the majority of the available data are from immortalized cancer cell lines. Cancer cells display markedly different responses to compounds, not only when compared to normal cells, but also among cancer types. Strategies for predicting drug signatures in unseen cells--cell lines not in the reference datasets--are still lacking. ResultsIn this work we developed a computational strategy, called CrossTx, for predicting drug transcriptomic signatures of an unseen target cell line using drug transcriptome data of reference cell lines and background transcriptome data of the target cells. Our strategy involves the combination of predictor and corrector steps. Briefly, the Predictor applies averaging (mean) or linear regression model to the reference dataset to generate cell line-agnostic drug signatures. The Corrector generates target-specific drug signatures by projecting cell line-agnostic signatures from the Predictor onto the transcriptomic latent space of the target cell line using Principal Component Analysis (PCA) and/or an Autoencoder (AE). We tested different combinations of Predictor-Corrector algorithms in an application to the CMap dataset to demonstrate the performance of our approach. ConclusionCrossTx is an efficacious and generalizable method for predicting drug signatures in an unseen target cell line. Among the combinations tested, we found that the best strategy is to employ Mean as the Predictor and PCA followed by AE (PCA+AE) as the Corrector. Still, the combination of Mean and PCA (without AE) is an attractive strategy because of its computationally efficiency and simplicity, while offering only slightly less accurate drug signature predictions than the best performing combination. Availability and implementationhttp://www.github.com/cabsel/crosstx Contactrgunawan@buffalo.edu

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bioRxiv2019

DeltaNeTS+: Elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks

BackgroundKnowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes.\n\nResultsIn this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ relies on a gene regulatory network model to identify direct perturbations to the transcription of genes. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course gene expression profiles, as well as to leverage information on the gene network structure that is increasingly becoming available for a multitude of organisms, including human. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains.\n\nConclusionDeltaNeTS+ is an enabling tool to infer gene transcriptional perturbations caused by diseases and drugs from gene transcriptional profiles. By incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~10s). DeltaNeTS+ can freely downloaded from http://www.github.com/cabsel/deltanetsplus.

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