Biology subjects

Javaid, A.

Publications and source records attributed to Javaid, A..

3 recordsLinked to original sources

bioRxiv2023

Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE)

Cytokine interaction activity modeling is a pressing problem since uncontrolled cytokine influx is at fault in a variety of medical conditions, including viral infections like COVID19, and cancer. Accurate knowledge of cytokine activity levels can be leveraged to provide tailored treatment recommendations based on individual patients transcriptomics data. Here, we describe a novel method named Single cell transcriptomics-level Cytokine Activity Prediction and Estimation (SCAPE) that can predict cell-level cytokine activity from scRNA-seq data. SCAPE generates activity estimates using cytokine-specific gene sets constructed using information from the CytoSig and Reactome databases and scored with a modified version of the Variance-adjusted Mahalanobis (VAM) method adjusted for negative weights. We validate SCAPE using both simulated and real single cell RNA-sequencing (scRNA-seq) data. For the simulation study, we perturb real scRNA-seq data to reflect the expected stimulation signature of up to 41 cytokines, including chemokines, interleukins and growth factors. For the real data evaluation, we use publicly accessible scRNA-seq data that captures cytokine stimulation and blockade experiment conditions and a COVID19 transcriptomics data. As demonstrated by these evaluations, our approach can accurately estimate cell-level cytokine activity from scRNA-seq data. Our model has the potential to be incorporated in clinical settings as a way to estimate cytokine signaling for different cell populations within an impacted tissue sample.

bioinformatics↗

bioRxiv2022

STREAK: A Supervised Cell Surface Receptor Abundance Estimation Strategy for Single Cell RNA-Sequencing Data using Feature Selection and Thresholded Gene Set Scoring

The accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on three joint scRNA-seq/CITE-seq datasets that represent two human tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model.

bioinformatics↗

bioRxiv2022

SPECK: An Unsupervised Learning Approach for Cell Surface Receptor Abundance Estimation for Single Cell RNA-Sequencing Data

The rapid development of single cell transcriptomics has revolutionized the study of complex tissues. Single cell RNA-sequencing (scRNA-seq) can profile tens-of-thousands of dissociated cells from a tissue sample, enabling researchers to identify cell types, phenotypes and interactions that control tissue structure and function. A key requirement of these applications is the accurate estimation of cell surface protein abundance. Although technologies to directly quantify surface proteins are available, this data is uncommon and limited to proteins with available antibodies. While supervised methods that are trained on Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data can provide the best performance, this training data is also limited by available antibodies and may not exist for the tissue under investigation. In the absence of protein measurements, researchers must estimate receptor abundance from scRNA-seq data. We thereby developed a new unsupervised method for receptor abundance estimation using scRNA-seq data called SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and evaluated its performance against other unsupervised approaches on up to 215 human receptors and multiple tissue types. This analysis reveals that techniques based on a thresholded reduced rank reconstruction (RRR) of scRNA-seq data are effective for receptor abundance estimation with SPECK providing the best overall performance.

bioinformatics↗