README.md

In SingerLab/gac: Genetic Analysis of Cells

GAC: Genetic Analysis of Cells

GAC is currently under alpha testing

The goal of GAC is to deliver a formal end-to-end analysis by integrating proven methods of quantitative genetics, statistics, and evolutionary biology for the genetic analysis of single-cell DNA copy number. GAC implements a simple, lightweight, and open-source R framework (Figure 1). Inspired, but unlike Seurat and Scanpy, adapts the logic of espressioSet/AnnData into relational matrices in native R which facilitates the integration of algorithms for the downstream analysis of single-cell DNA data which is so desperately needed.

GAC facilitates the downstream analyses of segmented data with common segments by concurrently managing the X, and Y across all cells or samples e.g. the output of Varbin/Ginkgo, FACETS, MUMdex, HMMcopy, or SCOPE. The unsegmented bin read counts is not a correct input. GAC uses ComplexHeatmap, an ultra-powerful tool for heatmaps to help visualize the data.

To implement GAC we require five easy-to-generate inputs: - a copynumber / genotype matrix (X) (bins[i] x cells[j]) - a phenotype matrix (Y) (cells[j] x phenotype[y]) - a qc matrix (technical wet-lab notes) (qc) (cells[j] x qc[c]) - a gene to bin index (gene.index) - the genomic coordinates of the bins or genotypes (chromInfo) - and an optional expression matrix (Ye; for DNA-RNA or same-cell G+T(Macaulay, et al.2015) (under development)

Installation

Dependencies:

• ComplexHeatmap
• ConsensusClusterPlus
• vegan
• dplyr
• SCclust

Install dependencies

install.packages("devtools")
devtools::install_github("KrasnitzLab/SCclust")

install.packages("BiocManager")
BiocManager::install(c("ComplexHeatmap", "ConsensusClusterPlus"))

You can install the development version from GitHub with:

devtools::install_github("SingerLab/gac")

Examples

This is a basic example for drawing a copy number heatmap. For a comprehensive overview of the package please follow the getting_started.Rmd in the vignettes/

library(gac)

## basic example code
data(cnr)

( excl.cells <- rownames(cnr$qc)[cnr$qc$qc.status == "FAIL"] )
#> [1] "cell5"  "cell11"

cnr <- excludeCells(cnr, excl = excl.cells)

aH <- HeatmapCNR(cnr, what = 'X', col = segCol, show_heatmap_legend = FALSE)

draw(aH, annotation_legend_list = list(legSeg))

bH <- HeatmapCNR(cnr, what = "genes",
                 which.genes = c("CDK4", "MDM2"),
                 col = segCol, show_heatmap_legend = FALSE)
#> Warning: The input is a data frame, convert it to the matrix.

draw(bH, annotation_legend_list = list(legSeg))

Motivation and design

This package came out of the need to deliver some results on borrowed time. During the 11th hour, I saw I was spending 85% phenotypes), 10% rendering heatmaps, and 5% looking at my time keeping multiple synchronized tables of bins, annotations, genes, and results. I began to think how lucky the people who only work with single-cell RNAseq are to have tools like Seurat and Scanpy, how simple and flexible those two tools are, and how nothing for DNA copy number is as powerful as the sister tools Seurat and Scanpy to manage the copy number matrix. I eventually realized that the main difference is the restriction imposed by the genome coordinates. While staring at the AnnData diagram I realized that for copy number data, the unit is a bin and the .X should be a matrix of common bins for all cells. However, to make biological sense of the data, gene level resolution is required. Thus, building a synchronized matrix with genes is of utmost importance. Having an internal gene-to-bin index (gene.index) allowed the flexibility to interpolate the bin data to gene-level resolution and integration to the complete set of phenotypes, and QC data.

The need to have a simple tool to manage input and output reduced 85% of the time spent synchronizing bins, genes, phenotypes, and QC matrices capable of handling a large data set of >24,000 cells was greatly needed. Knowing the data is growing by the week. Functions to handle the n+1 problem are integrated via addCells.

We hope you enjoy !

• Rodrigo, et al.

What’s in the works

• Integration with MLR for non-linear genetic models

• Integration with CORE and GISTIC2 for finding focal and recurrent events

• Integration of infScite for somatic alteration evolution

• Integration with Pathview for KEGG pathway visualization

• support for .seg files

• Cleaner code with tidyverse

• CRAN testing

Licence

GAC framework and code is distributed under a BSD-3 License

SingerLab/gac documentation built on March 23, 2024, 5:15 a.m.