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README.md
[![R](https://github.com/wenjie1991/scMitoMut/actions/workflows/r.yml/badge.svg)](https://github.com/wenjie1991/scMitoMut/actions/workflows/r.yml) # The {scMitoMut} package <img src="./CellBarcode_logo.png" width="200"> The [**{scMitoMut}**](https://github.com/wenjie1991/scMitoMut) is a R/Bioconductor Package for lineage informative mitochondrial mutation calling in Single-Cell sequencing. The mitochondrial somatic mutations are promising lineage markers for single-cell sequencing data. The {scMitoMut} package provides a comprehensive function to call the lineage informative mitochondrial mutations using beta-binomial model. # Installation ## Install from bioconductor ```r ## Install `BiocManager` package to manage Bioconductor packages. install.packages("BiocManager") ## Install `scMitoMut` from Bioconductor BiocManager::install("scMitoMut") ``` ## Install the devel version from GitHub or Bioconductor ```r # install.packages("devtools") devtools::install_github("wenjie1991/scMitoMut", build_vignettes = TRUE) # or BiocManager::install("scMitoMut", version = "devel") ``` # Vignette A vignette can be found in [Bioconductor](https://www.bioconductor.org/packages/devel/bioc/vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.html). And the source code can be found [here](https://github.com/wenjie1991/scMitoMut/blob/main/vignettes/Analysis_colon_cancer_dataset.Rmd). You can also access the vignette by the R command `browseVignettes('scMitoMut')` after installing the package. # Mini Example This is a simple example that demonstrates the main function of the package. It can be executed in less than 1 minute. ```r library(scMitoMut) # load the data ## Use the example data f = system.file("extdata", "mini_dataset.tsv.gz", package = "scMitoMut") ## Load the data with parse_table function f_h5 = parse_table(f, sep = "\t", h5_file = "./mut.h5") ## open the h5f file x = open_h5_file(f_h5) # run the model fit # You can increase the cpu core to accelerate # This step need some time, so the result will be kept in h5 file, # you do not need to re-run this step, when you load the h5 file next time. run_model_fit(x, mc.cores = 1) # Filter the loci based on the model fit results # The filter options will be keeped in the object by memory # Next time you re-load the h5 file, the filter will be initiated as default x = filter_loc(x, min_cell = 5, model = "bb", p_threshold = 0.01, p_adj_method = "fdr" ) x # Set the cell annotation f = system.file("extdata", "mini_dataset_cell_ann.csv", package = "scMitoMut") cell_ann = read.csv(f, row.names=1) # Prepare the color for cell annotation colors = c( "Cancer Epi" = "#f28482", Blood = "#f6bd60") ann_colors = list("SeuratCellTypes" = colors) # plot the heatmap for p-value plot_heatmap(x, type = "p", cell_ann = cell_ann, ann_colors = ann_colors, percent_interp = 0.2) # plot the heatmap for allele frequency plot_heatmap(x, type = "af", cell_ann = cell_ann, ann_colors = ann_colors, percent_interp = 0.2) # check af~coverage for one loci plot_af_coverage(x, "chrM.1227") ``` # Contribution You are welcome to open an issue or make a pull request.