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t-SNE does different things to different data. Here is a look: biorxiv.org/content/10.110…

You'd be surprised where most shanks actually end up. #ShankWeek

More hockey in Cotton Bowl® Stadium? Y U P. ❄️ JAN. 3 ➡️ Chicago Steel vs. Green Bay Gamblers @ 5:00 PM JAN. 5 ➡️ Texas A&M vs. Texas @ 5:15 PM Both of these games are FREE to attend! For more info: bit.ly/2Qakwj1

#WinterClassic is over but hockey at Cotton Bowl is not! The UT (Texas Ice Hockey) vs. A&M (Texas A&M Ice Hockey) rivalry will be renewed, except it'll be on ice! A big deal for TCHC Hockey/ACHA Men's D2 and hockey in TX. My latest all about it The Athletic Dallas👇🏽 theathletic.com/1475250/2020/0…

Throwback to last week’s QSBC seminar with Cody Heiser presenting - our first of the semester! Looking forward to another semester of seminars, VU speakers, and visiting speakers

We routinely use various dimension reduction tools to project multi-dimensional datasets into latent spaces for visualization and downstream analysis, but how much information do we lose? Cody Heiser may provide some insight. cell.com/cell-reports/f… [1/4]

We present dropkick - a machine learning Python package that leverages ambient signal for QC and automatic droplet filtering of scRNA-seq data Cody Heiser biorxiv.org/content/10.110…

Our dropkick cell filtering manuscript is now online at Genome Research. dropkick provides fast, automated cell identification in droplet-based #singlecell RNA-seq datasets, performing especially well on high-background samples. dx.doi.org/10.1101/gr.271…

For those who wants to know how we process+filter scRNA-seq data from all sorts of platforms (low quality/high quality, and the like), here is a protocol for y'all: sciencedirect.com/science/articl…

Creating foot traffic for my next poster presentation 👀 ken-lau-lab.github.io/2020/10/09/dro…

This is not my personal opinion, but a PSA endorsed by my lab of- why you should never code in R. Please, in the name of all things good in this world, do the following: 1) use Python, 2) if you can’t do (1), comment your code, 3) EXPLICIT is better than IMPLICIT -a Python mantra

Cell Vanderbilt CPB Cherie Scurrah Vanderbilt Cell and Developmental Biology A cytotoxic immune microenvironment in hypermutated tumors confer better patient prognosis and immunotherapy response. Strikingly, serrated polyps (precursors to MSI-H CRCs) already present a cytotoxic immune microenvironment preceding hypermutation

This Cell article was a blast to contribute to. Sets the stage for ongoing efforts to build an even HIGHER-res view of tumor/microenv co-evolution (read: spatially-resolved) across these two major classes of CRC. Stay tuned. 📻

Outstanding kickoff for the annual HTAN meeting! Thanks to Ken Lau Lab William J. Greenleaf @SantagataLab Michael Snyder, PhD Reyka Jayasinghe, PhD Nataly Naser Al Deen Cody Heiser Great to see a talented and diverse group of scientists generating incredible approaches to spatially resolved data! @NCIHTAN

First preprint by LauLab student Harsimran Kaur about MILWRM - a python package that identifies consensus tissue domains across specimens (aka slides) in high dimensional mIF and ST data. Looking forward to your QE! Simon Vandekar Cody Heiser @etmckinley biorxiv.org/content/10.110…

Capstone manuscript from my dissertation is out!🎓📜 Important implications for predicting (and eventually targeting) immune exclusion in CRC

Capstone paper from my dissertation research is out in Cell! Thanks Ken Lau Lab and Vanderbilt CPB for the tremendous support along the way.

Extremely excited about our atlas as a proof-of-concept for leveraging spatial molecular data to gain insights into tumor evolution and microenvironment that is lost at the bulk and even single-cell level.

A deep dive into immune exclusion was a great application for these multimodal, spatially resolved data, but I'm confident we only scratched the surface! Looking forward to how this atlas and future similar datasets enable target discovery in #oncology

Unsupervised pixel-level spatial analysis tool from my colleagues at Vanderbilt! Simple, fast, interpretable - very promising for all flavors of ST and MxIF data. Great work by Harsimran Kaur