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.
Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To understand this cross-talk, we analyzed the transcriptome of 91,103 unsorted single cells from 23 Korean and 6 Belgian patients. Cancer cells displayed transcriptional features reminiscent of normal differentiation programs, and genetic alterations that apparently fostered immunosuppressive microenvironments directed by regulatory T cells, myofibroblasts and myeloid cells. Intercellular network reconstruction supported the association between cancer cell signatures and specific stromal or immune cell populations. Our collective view of the cellular landscape and intercellular interactions in colorectal cancer provide mechanistic information for the design of efficient immuno-oncology treatment strategies.
Background: Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation.
Results: We present scConsensus, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations.
Conclusions: scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is freely available on GitHub.
Mapping the complete set of protein and gene interactions in the human cell has been a goal of the biological community for nearly two decades, since the first human genome was sequenced. To this end, computational approaches have been studied in depth to allow functional annotation of protein interactions. In this project, we explored the potential of using four common module detection algorithms - stochastic block model, Louvain method, modified Louvain (incremental Louvain) method and link community - in order to detect functional modules of for protein interaction networks. We implemented these algorithms in a Cytoscape application for users to run on their respective networks. Using this application, we conducted a comparative study of the algorithms to understand their applicability in protein function annotation and determine how close topological modules of protein-interaction networks are to their functional modules.
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