ORCID Profile
0000-0002-5666-2551
Current Organisations
Indraprastha Institute of Information Technology Delhi
,
Queensland University of Technology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Singapore
Date: 2019
Publisher: Wiley
Date: 11-05-2021
DOI: 10.1002/WIDM.1414
Abstract: Single‐cell omics technologies provide biologists with a new dimension for systematically dissecting the underlying complexities within biological systems. These powerful technologies have triggered a wave of rapid development and deployment of new computational tools capable of teasing out critical insights by analysis of large volumes of omics data at single‐cell resolution. Some of the key advancements include identifying molecular signatures imparting cellular identities, their evolutionary relationships, identifying novel and rare cell‐types, and establishing a direct link between cellular genotypes and phenotypes. With the sharp increase in the throughput of single‐cell platforms, the demand for efficient computational algorithms has become prominent. As such, devising novel computational strategies is critical to ensure optimal use of this wealth of molecular data for gaining newer insights into cellular biology. Here we discuss some of the grand opportunities of computational breakthroughs which would accelerate single‐cell research. These are: predicting cellular identity, single‐cell guided in silico drug screening for precision medicine, transfer learning methods to handle sparsity and heterogeneity of expression data, establishing genotype–phenotype relationships at single‐cell resolution, and developing computational platforms for handling big data. This article is categorized under: Algorithmic Development Biological Data Mining Fundamental Concepts of Data and Knowledge Big Data Mining Technologies Machine Learning
Publisher: Informa UK Limited
Date: 29-12-2021
Publisher: Cold Spring Harbor Laboratory
Date: 30-12-2022
DOI: 10.1101/2022.12.28.522060
Abstract: Robust characterization of cellular phenotypes from single-cell gene expression data is of paramount importance in studying complex biological systems and diseases. Single-cell RNA-sequencing (scRNA- seq), coupled with robust computational analysis, facilitates characterization of phenotypic heterogeneity in tumors. Current scRNA-seq analysis pipelines are capable of accurately identifying a myriad of malignant and non-malignant cell subtypes from single-cell profiling of tumor microenvironments. Unfortunately, given the extent of phenotypic heterogeneity, it is not straightforward to assess the risk associated with in idual malignant cell subpopulations in a tumor, primarily due to the complexity of the cancer phenotype space and the lack of clinical annotations associated with tumor scRNA-seq studies, involving prospectively collected tissue s les. Effective risk-stratification of in idual malignant subclones holds promise for formulating tailored therapeutic interventions. To this end, we present SCellBOW, a computational approach that facilitates risk-stratification by leveraging scRNA-seq profiles and language modeling techniques. We compared SCellBOW with existing best practice methods for its ability to precisely represent phenotypically ergent cell types across multiple scRNA-seq datasets, including our in-house generated human splenocyte and matched peripheral blood mononuclear cell (PBMC) dataset. SCellBOW offers a remarkable feature for executing algebraic operations such as ’+’ and ’–’ on single-cells in the latent space while preserving the biological meanings. This feature catalyzes the simulation of the residual phenotype of tumors, following positive and negative selection of specific malignant cell subtypes in a tumor. As a proof of concept, we tested and validated phenotype algebra across three independent cancer types – glioblastoma multiforme, breast cancer and metastatic prostate cancer. In particular, we demonstrate how the negative selection of specific clones may lead to variable prognosis. From the metastatic prostate cancer scRNA-seq data, SCellBOW identifies a hitherto unknown and pervasive AR−/NE low (androgen receptor negative, neuroendocrine-low) malignant cell subpopulation with a conspicuously high predictive risk score. We could trace this back in a large-scale spatial omics atlas of 141 well-characterized metastatic prostate cancer s les at the spot resolution.
Publisher: Springer International Publishing
Date: 2021
Location: India
No related grants have been discovered for Namrata Bhattacharya.