MitoHEAR

MitoHEAR (Mitochondrial HEteroplasmy AnalyzeR) is an R package that allows the estimation as well as downstream statistical analysis of the mtDNA heteroplasmy calculated from single-cell datasets.

Installation

You can install the released version of MitoHEAR from CRAN with:

install.packages("MitoHEAR")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("https://github.com/ScialdoneLab/MitoHEAR/tree/master")
library(MitoHEAR)

Getting started

The package has two main functions: get_raw_counts_allele and get_heteroplasmy.

library(MitoHEAR)

load(system.file("extdata", "meta_data_antonio_final.Rda", package = "MitoHEAR"))
#cell_names <- meta_data_antonio_final$antonio_array.Comment.ENA_RUN.
#path_to_bam <- "full_path_to_bam_files"
#bam_input <- paste(path_to_bam,cell_names, ".unique.bam", sep = "")
#path_fasta <- "full_path_to_fasta_file"
#output_SNP_antonio_mt <- get_raw_counts_allele(bam_input, path_fasta, cell_names, cores_number = 1 )

load(system.file("extdata", "output_SNP_antonio_mt.Rda", package = "MitoHEAR"))
matrix_allele_counts <- output_SNP_antonio_mt[[1]]
# In this example we have 124 cells and 65196 columns (4 possible alleles for the 16299 bases in the mouse MT genome)
name_position_allele <- output_SNP_antonio_mt[[2]]
name_position <- output_SNP_antonio_mt[[3]]

row.names(meta_data_antonio_final) <- meta_data_antonio_final$antonio_array.Comment.ENA_RUN.
meta_data_antonio_final <- meta_data_antonio_final[row.names(matrix_allele_counts), ]
row.names(matrix_allele_counts) <- meta_data_antonio_final$antonio_array.Source.Name
row.names(meta_data_antonio_final) <- meta_data_antonio_final$antonio_array.Source.Name

We select only the cells for the 2-cells stage for down-stream analysis.

stage_2_cells <- row.names(matrix_allele_counts)[grep("2cell_", row.names(matrix_allele_counts))]
stage_2_cells <- stage_2_cells[!grepl("32cell_", stage_2_cells)]

The next step is to obtain a matrix with allele frequencies and a matrix with heteroplasmy values for each pair of cell-base. This is obtained with the function get_heteroplasmy. This function performs a two step filtering procedure, the first on the cells and the second on the bases. The aim is to keep only the cells that have more than number_reads counts in more than number_positions bases and to keep only the bases that are covered by more than number_reads counts in all the remaining cells (filtering=1) or in at least 50% of cells in each cluster (filtering=2).

sc_data <- get_heteroplasmy(matrix_allele_counts[stage_2_cells, ], name_position_allele, name_position, 50, 2000, filtering = 1)

Among the output of get_heteroplasmy there are the matrix with heteroplasmy values and the matrix with allele frequencies, for all the cells and bases that pass the two steps filtering procedure. The heteroplasmy is computed as 1-max(f), where f are the frequencies of the four alleles for every cell-base pair. For more info about the output see ?get_heteroplasmy.

sum_matrix <- sc_data[[1]]
sum_matrix_qc <- sc_data[[2]]
heteroplasmy_matrix_sc <- sc_data[[3]]
allele_matrix_sc <- sc_data[[4]]
cluster_sc <- as.character(meta_data_antonio_final[row.names(heteroplasmy_matrix_sc), ]$antonio_array.Characteristics.developmental.stage.)
index_sc <- sc_data[[5]]

Down-stream analysis

MitoHEAR offers several ways to extrapolate relevant information from heteroplasmy measurement. For the identification of most different bases according to heteroplasmy between two group of cells (i.e. two clusters), an unpaired two-samples Wilcoxon test is performed with the function get_wilcox_test. The heteroplasmy and the corresponding allele frequencies for a specific base can be plotted with plot_heteroplasmy and plot_allele_frequency. If for each sample a diffusion pseudo time information is available, then it is possible to detect the bases whose heteroplasmy changes in a significant way along pseudo-time with dpt_test and to plot the trend with plot_dpt. It is also possible to perform a cluster analysis on the samples based on distance matrix obtained from allele frequencies with clustering_angular_distance and to visualize an heatmap of the distance matrix with samples sorted according to the cluster result with plot_heatmap. This approach could be useful for lineage tracing analysis. For more exhaustive information about the functions offered by MitoHEAR see Vignettes section below and the help page of the single functions. (?function_name).

Vignettes

The following vignette is provided within the package MitoHEAR and is accessible within R:

#utils::vignette("MitoHEAR")