We calculate the MAF for the MAEGATK results.
We calculate the MAF from a reference reads matrix and an alternative reads matrix. +This function is intended to be used with the mitochondrial genome and not with other somatic mutations.
CalculateAlleleFrequency(reference_reads, alternative_reads)
CalculateAlleleFrequency(reference_reads, alternative_reads, pseudo_count = 0)
CalculateConsensus.Rd
We calculate the consensus information from the MAEGATK results.
We calculate the consensus information from the MAEGATK results. +We set cells that have only alternative reads to 2 (Alternative). +We set cells that have only reference reads to 1 (Reference). +We set cells that have a mixture of alternative and reference reads to 3 (Both). +We set cells that have no reads to 0 (NoCall).
Please note. Cells can have reads for the reference of a specific variant and no reads for the alternative. +The cell can still have a reads for the other alternative alleles. Then the cell is still considered as 0 (NoCall) for this variant. +For example: +A cell has at position 3: 0 A reads, 53 T reads, 63 C reads, 148 T reads. +For the variant chrM_3_T_A, the cell would have 53 reference reads, but also reads for other variants at this position. +To make sure that there is no confusion, the cell is set to NoCall.
CalculateConsensus(SE, chromosome_prefix = "chrM")
CalculateConsensus(SE, chromosome_prefix = "chrM", verbose = FALSE)
CalculateQuality( - SE, - variants = rownames(reads_alt), - chromosome_prefix = "chrM" -)
CalculateQuality(SE, variants, chromosome_prefix = "chrM")
We do this for one sample at a time. We want to remove:
all cells that are blacklisted,
all cells that are not in a Seurat object,
all cells that do not have at least one variant with >1 (Reference),
all variants that are for alternative transcripts,
all variants that are always NoCall,
set variants with a VAF below a threshold to reference.
set variants with a VAF below a threshold to NoCall or Reference.
GetCellInfoPerVariant(se, voi_ch)
GetCellInfoPerVariant(se, voi_ch, verbose = FALSE)
GetVariantInfo.Rd
We get the genotyping information for a set of variants. +The function returns a matrix with the values from the specified assay.
GetVariantInfo(SE, information = "consensus", variants = NULL, cells = NULL)
SummarizedExperiment object.
The assay with the desired information. Default: consensus
A vector of variants.
A vector of cell IDs. On default all cells are returned. Default: NULL
HeatmapVoi(SE, voi, annotation_trait = NULL, column_title = NULL)
HeatmapVoi( + SE, + voi, + annotation_trait = NULL, + column_title = NULL, + remove_empty_cells = FALSE +)
We load the MAEGATK output and transform it to be compatible with the VarTrix output. The input file is a specifically formated csv file with all the necessary information to run the analysis. Note that the source column in the input file needs to be one of the following: vartrix, mgaetk, mgatk. -This is hard coded and case insensitive.
samples_path
barcodes_path
patient
samples_file = NULL
The minimum number of cells with coverage for a variant. Variants with coverage in less than this amount of cells are removed. Default = 2
Path to the barcodes file tsv. Default = NULL
Should the function be verbose? Default = TRUE
SetVariantInfo.Rd
We add the genotyping information for a set of variants to a Seurat object. +The function returns a matrix with the values from the specified assay.
SetVariantInfo(SE, seurat_object, information = "consensus", variants = NULL)
The Seurat object.
We generate a heatmap showing the correlation of somatic variants with the MT variants.
We generate a heatmap showing the correlation of somatic variants with the MT variants. +Packages I want to remove. I cannot see where they are used. +ggplot2 parallel rcompanion tidyr
We generate a heatmap showing the Fisher test of somatic variants with the MT variants.
We generate a heatmap showing the Fisher test of somatic variants with the MT variants. +Packages I want to remove. +ggplot2 parallel rcompanion tidyr
We correlate the variants with each other using the Pearson correlation. -This function calls CalculateCorrelationPValue to perform the actual correlation.
VariantWiseCorrelation(variants_list, n_cores = 1, p_value_adjustment = "fdr")
VariantWiseCorrelation( + variants_list, + n_cores = 1, + p_value_adjustment = "fdr", + verbose = TRUE +)
We perform the Fisher test to determine which variants are associated. -This function calls CalculateFisherTestPValue to perform the actual testing.
VariantWiseFisherTest(variants_list, n_cores = 1, p_value_adjustment = "fdr")
VariantWiseFisherTest( + variants_list, + n_cores = 1, + p_value_adjustment = "fdr", + verbose = TRUE +)
char_to_numeric.Rd
A function to convert the heterozygous/homozygous information from the VCF to the consensus information from VarTrix. +It is only used in LoadingVCF_typewise.R.
char_to_numeric(char_value)
What is the genotype encoding you want to convert?
getMutMatrix.Rd
This function gets the allele frequency for a specific allele. It is used in computeAFMutMatrix. +Source: https://github.com/petervangalen/MAESTER-2021
getMutMatrix(SE, cov, letter, ref_allele, chromosome_prefix)
The coverage matrix from MAEGATK/MGATK.
The base we are interested in.
Vector of reference alleles.
The chromosome prefix used.
We get the consensus information for a specific matrix.
We get the consensus information for a specific matrix. +I want to remove some packages if they are not needed. See below which package apperantly wasn't needed. +Package to remove: dplyr, SummarizedExperiment
The alternative base.
The reference base.
CalculateCorrelationPValue()
HeatmapVoi()
LoadingVCF_typewise()
LoadingVarTrix_typewise()
VariantBurden()
combine_NAMES()
getReadMatrix()
save_object(object, file_name, file_format = NULL)
save_object(object, file_name, file_format = "zstd")