title	authors
PISCES Tutorial	Lukas Vlahos Aleksandar Obradovic Pasquale Laise Andrea Califano

Authors: Lukas Vlahos, Aleksandar Obradovic, Pasquale Laise, Andrea Califano
Contacts:

• Lukas Vlahos: lv2395@cumc.columbia.edu
• Aleksandar Obradovic: azo2104@cumc.columbia.edu

Overview

The pipeline for Protein Activity Inference in Single Cells (PISCES) is a regulatory-network-based methdology for the analysis of single cell gene expression profiles.

Currently, the PISCES manuscript is available on bioRxiv: https://doi.org/10.1101/2021.05.20.445002

NOTE: This version of the pipeline is a newer iteration, implementing new algorithms developed by the Califano lab. An updated manuscript and more robust set of vignettes for the newer iteration of the pipeline is forthcoming.

Installation

Here's how you can install the PISCES package:

# install cran packages
install.packages("abind", "BiocManager", "circlize", "cluster", "devtools", 
                 "ggplot2", "ggpubr", "ggrepel", "grDevices", "Matrix", 
                 "RColorBrewer", "RSpectra", "Seurat", "uwot")
# install bioconductor packages
BiocManager::install("biomaRt")
BiocManager::install("ComplexHeatmap")
# install PISCES
devtools::install_github("califano-lab/PISCES")

You can then learn about how to use PISCES with our vignettes:

library(PISCES)
browseVignettes(package = "PISCES")

Some other features we're working on right now:

• Vignette demonstating the functionality of MWKMeans for analyzing trajectories
• RCPP ARACNe for easier network generation

=======

References

1. Lachmann, A., et al., ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics, 2016. 32(14): p. 2233-5.
2. Califano, H.D.a.A., iterClust: Iterative Clustering. R package version 1.4.0. 2018: https://github.com/hd2326/iterClust.
3. Ding, H., et al., Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. Nat Commun, 2018. 9(1): p. 1471.
4. Rosseeuw, P.J., Journal of Computational and Applied Mathematics 20 (1987) 53-65
5. Izenman, A.J., Modern Multivariate Statistical Techniques. Regression, Classification, and Manifold Learning. Springer text in statistics, 2008 (Chapter 12)

Acknowledgements

Jeremy Dooley - for his advice and expertise in single cell sequencing experiments.
Hongxu Ding - whose work in the Califano laid the groundwork for the development of this pipeline.
Evan Paull - for help with software and tutorial development and testing.