I’m Ankit, a passionate researcher with a diverse background in computational biology, quantitative modeling, and data analysis. Welcome to my GitHub profile!
🧬 Current Work:
I'm currently immersed in the exciting world of machine learning, where I'm developing an innovative pipeline that seamlessly integrates single-cell RNA sequencing data with image-based spatial transcriptomics data. Through this integration of data sources, we're unraveling the intricate web of cellular crosstalk within various tissue niches. My focus extends to deciphering the covariation of gene modules in colocalized cellular states. We've applied this method to publicly available datasets from liver, organogenesis, brain, and intestine tissues, with the aim of identifying novel therapeutic targets. (Stay tuned, as manuscript is under revision!)
🦴 Past Endeavors:
In a previous project, I delved into the fascinating realm of bone growth morphogenesis. Using quantitative modeling, statistical analyses, and advanced morphometric techniques applied to growth plate tissue images, we unearthed novel insights into the mechanisms governing long bone elongation. Our journey included tasks such as cell/nuclei segmentation, bone registration, and the defining the parameters of various morphological properties. Among our discoveries, I observed isometric growth of chondrocytes in the resting zone of the growth plate, contrasting with allometric growth in the proliferative zone. I also identified distinct patterns in cell lineage clusters between embryonic and neonatal mice that linked to circumferential and elongation of bone growth.
To dive deeper into these discoveries, check out our publication: Application of 3D MAPs pipeline identifies the morphological sequence chondrocytes undergo and the regulatory role of GDF5 in this process.
Bone elongation in the embryo occurs without column formation in the growth plate.
🔬 Previous Research Highlights:
I've explored various facets of biology and data analysis throughout my career. This includes using gene expression as a proxy for activity to elucidate chromosome positioning in nuclei. I developed biophysical models to predict chromatin distributions in nuclei, even delving into their 2D/3D chromosomes shapes. My journey has also taken me into the realm of ChIP-seq data analysis to uncover mixtures of motifs using sequence similarity and position weight matrix models.
Interested in learning more? Dive into the details of my previous research through these publications:
Nonequilibrium Biophysical Processes Influence the Large-Scale Architecture of the Cell Nucleus.
THiCweed: fast, sensitive detection of sequence features by clustering big datasets.
🛠️ Skills:
Throughout my research journey, I've acquired a versatile skill set spanning machine learning, quantitative biology, image analysis, spatial biology, spatial transcriptomics, single cell RNA sequencing, neighborhood analysis, and the quantification of cell/nuclei and tissue morphological properties (volume, surface area, sphericity, principal component orientation, and many more).
📄 Learn More:
If you'd like to explore my credentials in more detail, feel free to peruse my CV.
Thank you for visiting my GitHub profile! If you have any questions or want to collaborate on fascinating research projects, don't hesitate to reach out. Let's unlock the mysteries of biology and data together! 🌟
W 1234# Scopus Author ID: 57193255954