Hi! I'm Bobby Ranjan, a bioinformatics specialist at the Genome Institute of Singapore. Born in Scotland, raised in India and working in Singapore, I'm an ambitious student-researcher in the field of computational biology.
Presentations & Publications
Although cell type annotation is a crucial step in scRNA-seq data analysis, the single-cell community has yet to agree upon a single methodology. Semi-supervised clustering methods like RCA and SingleR robustly detect major cell types by correlating reference bulk transcriptomic datasets of sorted cell types with single-cell data. However, these methods are limited by the resolution of their reference data sets and cannot annotate intermediate cell states and case-specific phenotypes. De-novo clustering methods, implemented in packages like Seurat and Scanpy, overcome these limitations. Despite these advantages, they are more susceptible to batch and quality variation, occasionally producing heterogeneous cell clusters.
We utilise the merits and account for the demerits of both methodologies in scConsensus, a hybrid approach to obtain cell type annotation using a consensus of semi-supervised and unsupervised clustering. scConsensus uses the reference-based RCA to identify known cell types, simultaneously implementing de-novo clustering using Seurat on the same dataset. Both clustering results are then manually annotated using the strongest correlated reference cell type for RCA and marker gene expression for Seurat. In RCA, clusters that do not correlate with a unique reference, are case-specific or are expected to be subtypes of a reference cell type are annotated using differential expression of marker genes of their corresponding Seurat clusters. Conversely, heterogeneous Seurat clusters are reassigned using their best correlating RCA reference cell types. A consensus cluster annotation is thus obtained, where a unique cell type is assigned to each cell. scConsensus then looks for differentially expressed (DE) genes between every pair of cell types, and uses the union set of DE genes to cluster the cells using hierarchical clustering.
We evaluated the performance of scConsensus on 12 scRNA-seq (UMI and SmartSeq2) datasets. We show that scConsensus consistently outputs more homogeneous clusters compared to Seurat or RCA. Furthermore, we used 6 CITE-seq datasets to show that scConsensus’ results correlate better with antibody-based cell type annotations. Thus, we conclude that scConsensus is a robust methodology to detect known cell types and characterise new cell types in scRNA-seq data.
Background: Alzheimer's disease (AD) is a progressive neurological disorder, recognized as the most common cause of dementia affecting people aged 65 and above. AD is characterized by an increase in amyloid metabolism, and by the misfolding and deposition of β-amyloid oligomers in and around neurons in the brain. These processes remodel the calcium signaling mechanism in neurons, leading to cell death via apoptosis. Despite accumulating knowledge about the biological processes underlying AD, mathematical models to date are restricted to depicting only a small portion of the pathology.
Results: Here, we integrated multiple mathematical models to analyze and understand the relationship among amyloid depositions, calcium signaling and mitochondrial permeability transition pore(PTP)-related cell apoptosis in AD. The model was used to simulate calcium dynamics in the absence and presence of AD. In the absence of AD, i.e. without β-amyloid deposition, mitochondrial and cytosolic calcium level remains in the low resting concentration. However, our in silico simulation of the presence of AD with the β-amyloid deposition, shows an increase in the entry of calcium ions into the cell and dysregulation of Ca2+ channel receptors on the Endoplasmic Reticulum. This composite model enabled us to make simulation that is not possible to measure experimentally.
Conclusions: Our mathematical model depicting the mechanisms affecting calcium signaling in neurons can help understand AD at the systems level and has potential for diagnostic and therapeutic applications.
- Developing algorithms for cell type identification in single-cell data
- Built customer-facing license consumption report for all BitTitan products
- Conducted tech feasibility analysis to improve BitTitan’s reporting capacity
- Built code analysis tool to clean up database references across codebase
- Worked on the payments processing and payments testing development teams
- Redesigned database logging using a queueing mechanism with the help of Apache ActiveMQ and Java Spring Framework
- Also built an application to help onboard new testers onto the testing platform, using Java, AngularJS and SQL
Asia Risk Transfer Solutions - a software and technological solutions startup that aims to help the insurance industry and governments create and manage risk transfer products, known as Insurance for the Masses, for developing communities in Asia.
- Worked in a small team of 6 on Android application development for the company
- Developed 2 MVP Android applications for different user groups
- Enhanced server-side API of the enterprise web application
- Tech stack included Java, Android and Django