Publications by authors named "Manav Chakravarthy"
Measuring single-cell genomic profiles at different timepoints enables our understanding of cell development. This understanding is more comprehensive when we perform an integrative analysis of multiple measurements (or modalities) across various developmental stages. However, obtaining such measurements from the same set of single cells is resource-intensive, restricting our ability to study them integratively.
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- The growth of publicly available single-cell datasets has greatly improved our understanding of biology, but it raises significant privacy issues.
- Recent studies on data sharing have mainly focused on bulk gene expression data due to noise and a lack of large single-cell datasets.
- Our research reveals that individuals in single-cell datasets are at risk of linking attacks that expose sensitive information, and we propose a method for predicting genotypes that operates independently of eQTLs, allowing for the discovery of private information across different studies.
Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments.
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