Recent Activity

The Carpentry Instructor training

When: November 7, 2023 - February 6, 2024

I am in the process of becoming a certified Carpentry instructor!

Clinical and Single-Cell Transcriptomics for Pneumonia Codeathon

When: September 25-28, 2023

I participated in Clinical and Single-Cell Transcriptomics for Pneumonia Codeathon hosted by NIAID, CZI, BV-BRC, and Northwestern University.

BioC2023

When: August 2-4, 2023

I was accepted to present a poster at Bioconductor Conference titled “Statistical analysis of mass spectrometry generated peptidomics data”.

Abstract: Peptidomics is the comprehensive characterization of endogenous peptides from a biological sample mainly by HPLC and mass spectrometry. This analytical methodology allows the detection of a multitude of single peptides, even in complex mixtures. Label-free quantitative peptidomics has gained popularity in recent years as it has fewer sample processing steps, no limitation on sample number, and no need for pricy isotope labeling, and thus is well suited for large-scale experiments. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs, where run-to-run variability introduces a number of difficulties in data analysis and needs to be carefully adjusted for. In addition, a higher percentage of missing values are often present in multi-run LC-MS peptidomics data due to a number of factors, including the inability of the mass spectrometer to select and fragment every peptide in every run. Here, we outline the data processing and statistical analysis steps using R software and various Bioconductor packages for label-free quantitative peptidomics experiments, with an emphasis on setting up criteria for missing value evaluation and addressing the run-to-run variability issue, which can lead to several major problems in label-free experiments. Overall, our analysis pipeline provides researchers with a roadmap for the data processing and statistical analysis of their own label-free quantitative peptidomics data generated from a wide variety of different biological sources.

BV-BRC Workshop

When: June 26-30, 2023

We attended BV-BRC Workshop at Argonne National Laboratory as a team.

St. Jude KIDS23 BioHackathon

When: May 3-5, 2023

I participated in St. Jude KIDS23 (Knowledge in Data Science) BioHackathon on May 3-5, 2023 as a member of team-13, “Reusable R shiny modules for common plots and data types”. This project aims to develop high-quality, flexible R Shiny modules for common plots used in bioinformatics analyses (e.g. volcano plots, scatter plots, box/violin plots, heatmaps, line plots, etc). These modules will provide interactive plots (with (gg)plotly or d3.js) with full customization over the aesthetics via a compact series of inputs that users can tweak on the fly.

This project is still ongoing. Current progress can be found in team github.

Past activity

SDSC Summer Institute

When: August 1-5, 2022

I attended SDSC HPC and Data Science Summer Institute virtually.

rstudio::conf(2022)

When: July 25-29, 2022

I went to Washington DC for RStudio Conference as a diversity scholar!

ISMB Conference

When: July 11-15, 2022

We went to Madison, WI for International Society For Computational Biology Conference as a team.

UserR! 2022

When: Jun 20-23, 2022

I attended UserR! conference virtually.

CBW:MIC workshop

I attended Microbiome Analysis workshop (MIC 2021) hosted by Canadian Bioinformatics Workshop virtually.

UserR! 2021

When: Jul 5-9, 2021

I attended UserR! conference virtually.