README.md

In SingerLab/gac: Genetic Analysis of Cells

GAC: Genetic Analysis of Cells

GAC is currently in ALPHA-release

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 facilitate the integration of algorithms for the downstream analysis of single-cell DNA data wich is so desperately needed.

GAC facilitates the downstream analyses of segmented data with common segments by concurrently managing the X, 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 copy number / 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)

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)
data(segCol)
data(legSeg)

( 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. During the 11th hour (more like in borrowed time), I saw I was spending 85% of my time keeping 3 tables syncornized (bins, genes, and phenotypes), 10% rendering heatmaps, and 5% actually looking at the 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 diference 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 syncronized matrix with genes is of outmost importance. At the 11th hour, having a 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, but it’s not the restricted to the mouse mouse or human genomes. Cows, viruses, and plants have genomes too!

The Singer Lab single-cell wet-lab and dry-lab endevours are carried forward by a skeleton crew. The need to have something simple that can help reduce the 85% of the time spent syncronizing bins, to genes, to 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, I integrated functions to deal with the n+1 problem. Lastly, my background in animal genomics allowed me to borrow the succesful frameworks used in Genomic Selection in an abstract way in hopes that we can provide appropriate models for future same-cell technologies.

We hope you enjoy !

• Rodrigo, et al.

What’s in the works

• Integration of Henderson’s Animal Model for the phenoype-genotype analyses

• Integration with MLR for non-linear genetic models

• Integration with CORE and GISTIC2 for fidning 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 Oct. 14, 2022, 4:09 a.m.