Heteromotility data analysis with `hmR`
- 2 minsHeteromotility extracts quantitative features of single cell behavior from cell tracking data. Analyzing this high dimensional data presents a challenge. A typical workflow incorporates various types of analysis, such as unsupervised clustering, dimensionality reduction, visualization, analysis of specific features, pseudotiming, and more.
Previously, heteromotility data analysis relied on a library of rather unwieldy functions released with the feature extraction tool itself. I’m excited to release hmR today to lend some sanity to this analysis process.
hmR provides a set of clean semantics around single cell behavior data analysis. Inspired by the semantics of Seurat in the single cell RNA-seq analysis field, hmR focuses analysis around a single data object that can be exported and transported across environments while maintaining all intermediates and final products of analysis.
hmR carries users from raw heteromotility feature exports, all the way to biologically meaningful analysis in just a few simple commands.
As an example, it’s easy to produce visualizations of cell behavior state space in just a few lines with hmR.
library(hmR)
df = read.csv('path/to/motility_statistics.csv')
mot = hmMakeObject(raw.data=df)
# Perform hierarchical clustering
mot = hmHClust(mot, k = 3, method='ward.D2')
# Run and plot PCA
mot = hmPCA(mot)
mot = hmPlotPCA(mot)
# Run and plot tSNE
mot = hmTSNE(mot)
mot = hmPlotTSNE(mot)
Running a pseudotime analysis is just as simple
mot = hmPseudotime(mot)
hmPlotPseudotime(mot)
hmR currently focuses on analysis of cell behavior data in the static context, with dynamic analysis (detailed balance breaking, N-dimensional probability flux analysis, statewise cell transition vectors, etc.) being handled by the original heteromotility analysis suite.
Give hmR a try with your single cell behavior data and let me know if I can be helpful!
library(devtools)
devtools::install_github('jacobkimmel/hmR')
Jacob C. Kimmel
Co-founder & Head of Research @ NewLimit. Interested in aging, genomics, imaging, & machine learning.