Xiran Liu

Xiran Liu

she/her

Postdoctoral Researcher at Data Science Institute & Center for Computational Molecular Biology

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

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Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

Adaptation of genetic evolution methods to studying cultural evolution

Cultural evolution is the study of how socially learned behaviors, ideas, technologies, and norms change over time by processes analogous to biological evolution, but operating through social learning rather than genes. I study cultural variation using methods inspired by biological evolution, including the extraction of hierarchical structure in cultural variation and the intergenerational transmission of cultural traits.

Cultural Evolution

Mathematical properties of allele-sharing dissimilarities

Allele-Sharing Dissimilarities

I derive and analyze the mathematical properties of variants of allele-sharing dissimilarity measures (both within and between populations) as functions of allele-frequency distributions, providing guidance on their appropriate use in empirical studies.

Alignment of inferred latent ancestries in multiple population structure analysis results

I tackle cluster misalignment in population structure inference, where the inferred latent ancestries (clusters) vary across runs and choices of number of clusters (K>). I developed Clumppling to align multiple results, consolidate distinct solutions (modes), and clarify correspondence among inferred clusters. My tool visualizes the inferred population structure across K with multipartite graphs of bar plots and explicit alignment connections, enabling consistent, interpretable comparisons.
Clumppling

ML-based methods for multivariate genetic association analysis

I devise methods to analyze genetic association signals and better characterize the genetic architecture of complex traits. I developed ML-MAGES, which uses neural-network-based supervised learning to account for LD-induced inflation in single-trait, SNP-level genetic associations, uses an infinite mixture model to categorize multi-trait association patterns, and aggregates and visualize association signals at gene-level. This approach substantially improves computational efficiency with large LD matrices and nonlinear inflation, achieves effect-size shrinkage comparable to or better than existing methods, and enables data-driven inference of an arbitrary number of association patterns in high-dimensional multi-trait settings.
ML-MAGES

Clustering alignment to complement and advance single-cell clustering

I extend the clustering alignment framework to the single-cell analysis pipeline. Specifically, I develop new metrics for feature-level interpretation to identify cluster-informative genes, and provide measures of clustering consistency for comparing results from different clustering models. The alignment-enabled single-cell analysis pipeline streamlines robust clustering results evaluation, straightforward model comparison, and gene selection for downstream analyses.

Clumppling

publications

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

Victoria N Parikh, Alexander G Ioannidis, David Jimenez-Morales, John E Gorzynski, Hannah N De Jong, Xiran Liu, Jonasel Roque, Victoria P Cepeda-Espinoza, Kazutoyo Osoegawa, Chris Hughes, Shirley C Sutton, Nathan Youlton, Ruchi Joshi, David Amar, Yosuke Tanigawa, Douglas Russo, Justin Wong, Jessie T Lauzon, Jacob Edelson, Daniel Mas Montserrat, Yongchan Kwon, Simone Rubinacci, Olivier Delaneau, Lorenzo Cappello, Jaehee Kim, Massa J Shoura, Archana N Raja, Nathaniel Watson, Nathan Hammond, Elizabeth Spiteri, Kalyan C Mallempati, Gonzalo Montero-Martín, Jeffrey Christle, Jennifer Kim, Anna Kirillova, Kinya Seo, Yong Huang, Chunli Zhao, Sonia Moreno-Grau, Steven G Hershman, Karen P Dalton, Jimmy Zhen, Jack Kamm, Karan D Bhatt, Alina Isakova, Maurizio Morri, Thanmayi Ranganath, Catherine A Blish, Angela J Rogers, Kari Nadeau, Samuel Yang, Andra Blomkalns, Ruth O’Hara, Norma F Neff, Christopher DeBoever, Sándor Szalma, Matthew T Wheeler, Christian M Gates, Kyle Farh, Gary P Schroth, Phil Febbo, Francis DeSouza, Omar E Cornejo, Marcelo Fernandez-Vina, Amy Kistler, Julia A Palacios, Benjamin A Pinsky, Carlos D Bustamante, Manuel A Rivas, Euan A Ashley

Nature Communications, 2022

DOI:10.1038/s41467-022-32397-8

talks

teaching

DSCoV Workshop

Instructor for Workshop, Brown University, Data Science Institute, 2025

A workshop facing the general audience, including but not limited to undergraduate and graduate students interested in data science topics.