Hi, there! Thanks for visiting my website. I'm Aarthi, a Bioinformatician II at the Icahn School of Medicine at Mount Sinai. I analyze NGS datasets such as RNA-seq, ChIP-seq, ATAC-seq as well as use machine learning and deep learning models to analyze epigenetic datasets. My goal is putting my adept coding skills to use in finding cures for neurological diseases. My hobbies are reading books, writing and playing my piano.
I wanted to remind myself how the k-means clustering algorithm worked. Following are the steps involved in K-means clustering - 1. Start with a vector of 12 data points. For instance, [1, 2, 3, 4, 7, 8, 9, 10, 20, 21, 22, 23] 2. Randomly select 3 data points. These
Following is a collection of articles which I feel every Bioinformatician must be aware of. I will keep updating this list from time to time - 1. All biology is computational biology 2. Core services: Reward bioinformaticians 3. Importance of stupidity in scientific research
I came across an interesting Bioinformatics paper recently and wanted to read and understand it in its entirety. Reading the paper seemed intimidating at first, as the technical jargon that was being used seemed quite overwhelming. But reading a paper is not as difficult as it may first seem. Following
Screen is a very useful command to have in your toolbox if you frequently use interactive sessions on your supercomputer logged in through a VPN. A VPN typically has a time limit, and you may get disconnected from it without any warning when you have poor internet connection. Screen program
There are 2 major types of regression models one can specify in DESeq2 to explore the raw count matrices from an RNA-seq experiment - * Mean-reference model for Factors * Regression model for Covariates Mean-reference model for Factors - Factors typically represent categorical variable such as Gender, Ethnicity, Race etc. The mean-reference