# QCscATAC
## Description
QCscATAC
is an R package that will set up the Quality Control and eliminate low-quality data from obtained scATAC-seq data set before any pre-processing analysis. Generally speaking, this package will improve the data quality from raw scATAC-seq data set, and thus increase the quality (fast analysis speed, smaller batch effects, lower redundant cells for dimension reduction) of pre-processing analysis.
The package is developed under R 4.1.1 in Mac.
To install the latest version of the package:
``` r require("devtools") devtools::install_github("Yue-Zhou0429/QCscATAC", build_vignettes = TRUE) library("QC")
To run the Shiny app: Under construction ## Overview ``` r ls("package:QCscATAC") data(package = "QCscATAC") # optional
QCscATAC
contains 2 functions to enforce quality control on the import data, and 3 function to output UMAP, TSS enrichment plot and Fragment Size Distribution plot as reference.
browseVignettes("QCscATAC")
An overview of the package is illustrated below.
The author of the package is Yue Zhou.
Himes et al. (2014). RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. PloS one, 9(6), e99625. https://doi.org/10.1371/journal.pone.0099625
Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. https://doi.org/10.1093/bioinformatics/btp616
## Acknowledgements
This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinfor- matics, University of Toronto, Toronto, CANADA.
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