p-Creode
p-Creode takes continuous single-cell data to generate transition trajectories and then use resampling to quantitatively score the quality and stability of computed results (Herring et al., Cell Systems, 2018)
dropkick
Self contained machine learning algorithm for identifying true cells from cell barcodes in high ambient background samples (Heiser et al., Genome Research, 2021)
MIRIAM
Multiplex microscopy imaging segmentation algorithm focusing on membrane connectivity and designed for columnar epithelial cells (McKinley et al., Cytometry A, 2022)
sc-UniFrac
sc-UniFrac compares single-cell landscape diversity between samples by representing the global and local structure of single-cell data as trees and then performing hierarchical tree comparisons (Liu et al., PLoS Biology, 2018)
DR-Structure
Quantitative comparisons of multi-dimensional data structures when transformed by different dimension reduction techniques (Heiser and Lau, Cell Reports, 2020)
pyNVR
pyNVR is a faster way to select relevant features as input for p-Creode and other trajectory analysis algorithms, selecting gene (features) based on small local variances (Chen et al., Bioinformatics, 2019)
Open Source scRNA-seq Processing
Open source pipeline to process scRNA-seq data from FastQ files to clustering/dimension reduction – containerized (Chen et al., Star Protocols, 2021)
p-Creode score for comparing graphs of different sizes
An implementation of the p-Creode score that compares graphs with different nodes and edges, but across different samples that may have unbalanced number of nodes (Liu et al., PLoS Biology, 2018)
Cell manuscript data processing
Code to reproduce analyses in Chen et al., Cell, 2021
Semi-supervised heuristic cell filtering
Our pipeline to semi-automatically to filter cells from empty barcodes for downstream analyses – deprecated version – new version wrapped in “Open source scRNA-seq processing” (Chen et al., Star Protocols, 2021)