SQUID is an R package for conducting tumor deconvolution analyses. SQUID executes a combination of RNA-Seq transformation and dampened weighted least-squares deconvolution approaches in predicting the composition of cell mixtures and tissue samples based on the concurrent RNA-Seq and scnRNA-Seq profiles. Analyses of SQUID accuracy suggested that methods that harness the power of concurrent RNA-Seq and scnRNA-Seq profiling can consistently outperform other methods in predicting the composition of cell mixtures and tissue samples.
First load the requirements:
library(devtools)
devtools::source_url("https://github.com/favilaco/deconv_matching_bulk_scnRNA/blob/master/helper_functions.R?raw=TRUE")
library(dplyr)Now, you can install the latest version of SQUID from GitHub using the devtools package:
install_github("mjnajafpanah/SQUID")To use SQUID, load the package into your R session using the library function:
library(SQUID)You can then use the provided SQUID function to conduct the experimantal analyses. You need to provide the required inputs as described in the package documentation in details. SQUID package contains a toy example dataset including bulk and single-cell RNA-seq simulated count data which you can use to run the program and check how it works:
RESULTS <- SQUID(B=B, scC=scC , scMeta=scMeta, pB=NULL, P=NULL, LeaveOneOut=FALSE)Notice: To improve the performance of SQUID prediction, you may need to set up your research-specific pipeline for the quality contol, normalization, imputation, and single-cell clustering procedures on your datasets prior to run SQUID. You are welcome to look at our preprint paper as an example.
This package is licensed under the MIT license. See the LICENSE file for details.
For questions or comments about SQUID, please contact the package maintainers at Francisco Avila Cobos favil90@gmail.com and Mohammad Javad Najaf Panah mohammadjavad.najafpanah@bcm.edu. If you find a bug or have a feature request, please submit an issue on the GitHub repository.
Francisco Avila Cobos, Mohammad Javad Najaf Panah, Jessica Epps, Xiaochen Long, Tsz-Kwong Man, Hua-Sheng Chiu, Elad Chomsky, Evgeny Kiner, Michael J Krueger, Diego di Bernardo, Luis Voloch, Jan Molenaar, Sander R. van Hooff, Frank Westermann, Selina Jansky, Michele L. Redell, Pieter Mestdagh, Pavel Sumazin Effective methods for bulk RNA-Seq deconvolution using scnRNA-Seq transcriptomes (bioRxiv; https://www.biorxiv.org/content/10.1101/2022.12.13.520241v2)