davisidarta,Davi Sidarta,davisidarta,github

Hi! I'm Davi

I develop tools to understand and interpret high-dimensional data, with a focus on single-cell omics.

I developed TopOMetry, a comprehensive framework for high-dimensional data analysis. TopOMetry learns similarity graphs, estimates the dimensionality of the data, obtains latent dimensions using topological operators, clusters samples and layouts topological graphs into two-dimensional visualizations. TopOMetry learns and evaluates dozens of possible visualizations so that users do not have to stick with any pre-determined model (e.g. t-SNE or UMAP). It was designed to be compatible with a scikit-learn centered workflow, as most classes and functions can be pipelined. TopOMetry manuscript is freely available at BioRxiv.
I'm currently a postdoc at Ana Domingos' lab at the University of Oxford. We are working on generating and analyzing single-cell datasets from a variety of tissues relevant to obesity and metabolism to build updated comprehensive neuroanatomical maps with cellular resolution. These will serve as a foundation for new studies investigating cellular-specific therapeutic targets for obesity and its comorbidities.

I'm always open to interesting conversations and enjoy getting involved in many projects. Feel free to reach me by email.

I tweet about medicine, neuroscience, computational biology, machine learning, and sometimes about my personal life.

Davi Sidarta's Projects

code

Compilation of R and Python programming codes

Analysis methods for analysing single cell RNA-seq data; particularly with the goal of checking if tentative clusters of cells are significantly different to one another in terms of their gene expression.

dbmap

A fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.

demap

Denoised Embedding Manifold Preservation - Repo for # manuscript benchmarking

fastlapmap

Fast Laplacian Eigenmaps: lightweight multicore LE for non-linear dimensional reduction with minimal memory usage. Outperforms sklearn's implementation and escalates linearly beyond 10e6 samples.