Function reference • redeemR

Core redeemR2.0 workflow

Primary functions for importing REDEEM-V consensus calls and producing a filter2-ready redeemR object.

redeemR.read.trim(): Function to read in redeemV outputs with edge trimming, default is trimming 4bp

redeemR.read(): Function to read in redeemV outputs

Create_redeemR(): Create_redeemR

Create_redeemR_model(): Create a redeemR object from raw variant summaries

clean_redeem(): clean_redeem

add_annotation_redeem(): add_annotation_redeem

clean_redeem_remove_blacklist_RSRS50(): clean_redeem_remove_blacklist_RSRS50

Add_DepthMatrix_filter2(): Add Filter-2-adjusted depth matrix to a redeemR object

add_median_depth_to_redeemR(): Add median depth information to redeemR object

clean_redeem_remove_low_median_depth(): Remove variants with low median depth and depth-corrected homoplasmy

add_prop_2_3_to_redeemR(): add_prop_2_3_to_redeemR

update_redeemR_from_GTsummary(): Update redeemR object from GTsummary.filtered (helper)

REDEEM-V parsing and summaries

Utilities for reading final/ outputs, depth summaries, genotype summaries, and consensus diagnostics.

DepthSummary(): Function to summarize the depth (Total that passed Q30)

PosCoverageCellCount(): Function to count cells with non-zero coverage per position

GTSummary(): Function to generate GTS summary

reconstruct_genotype_summary(): This is a function borrowed from https://github.com/NickWilliamsSanger/treemut/blob/main/R/treemut.R#L68 Input phylo object, return a "profile matrix"--Edge(or denoted as the ending node) vs cell. a 0, 1 character string that indicate what cells in a given node

ComputeRejectRate(): Function to compute the reject rate(The filtering rate in concensus variant calling)

Show_Consensus(): Function to plot consensus mtDNA mutation benchmark

run_redeem_qc(): run_redeem_qc

print_redeemR_matrix_dims(): Print redeemR matrix dimensions

check_redeem(): Check redeem object consistency

Variant annotation

Population, context, blacklist, coding-impact, disease, and mutation-type annotation helpers.

annotate_all_variants(): Apply full suite of variant annotations

annotate_variants_population_stats(): Annotate variant table with population frequency and haplogroup statistics

annotate_variants_hypermutable(): Annotate hypermutable variants

annotate_variants_blacklist(): Annotate variants with mitochondrial blacklist region flag

annotate_variants_homopolymer(): Annotate variants with mitochondrial homopolymer context

annotate_variants_aachange(): Annotate variants with amino-acid change and predicted impact via dndscv

annotate_variants_mito_disease(): Annotate variants with mitochondrial disease associations

Annotate_base_change(): Annotate variant annotation with base-change and type

add_changes(): Extract nucleotide change from variant string

add_types(): Classify nucleotide change as transition or transversion

add_hypermutable(): add_hypermutable This function annotates the redeem@V.fitered with hypermutable mutations_v2 (using 3 young donor and 2 aged donors)

MutationProfile.bulk(): Function to plot bulk level mutation signatures

redeemR-data all.genes.refer ATACWhite CellPCT CellPCT.update ContextsDic gr_genes Griffin_Signatures haplogroup_markers mito_diseases mito_homopolymer mitomap_freq msig.db ref_all_long RefCDS RNAWhite: Bundled annotation and reference data

Matrix filtering and subsetting

Helpers for cell-by-variant matrices, binary matrices, depth matrices, and additional variant-level filters.

subset_redeem(): Subset redeemR object to a whitelist of cells and rebuild matrices

Subset_redeemR(): Subset_redeemR Subset a redeemR object by selecting a subset of cells, return a new redeemR object with only 4 slots: para; CellMeta; Cts.Mtx.bi; UniqueV, can be used for downstreme compute distance, clonal clustering, make tree, etc

filter_redeemR_by_cells(): Filter by cell subset

filter_redeemR_by_UMI(): Filter by UMI count (Freq) threshold on GTsummary

filter_redeemR_by_hetero(): Filter by VAF (hetero) threshold on GTsummary

filter_redeemR_by_meancount(): Filter by mean UMI count threshold on GTsummary

filter_redeemR_by_rules(): Filter by predefined rules using GTsummary data

filter_redeemR_by_LIS(): Filter by LIS score and VAF threshold (Wang et al., 2025, Genome Biology)

make_V.fitered_from_mtx(): Create variant summary table from count matrix

convert_redeem_matrix_long(): Convert a redeemR variant & depth matrix into long format for downstream analysis

convert_variant(): convert_variant

safe_dim(): Safely get matrix-like dimensions

Lineage and tree utilities

Distance, matrix, and tree helpers for downstream lineage reconstruction and visualization.

BinaryDist(): Compute distances for binary distances

quick_w_jaccard(): Compute weighted jaccard distance

quick_w_cosine(): Compute weighted cosine distance

compute_distance_matrix(): Compute Distance Matrix from Heteroplasmy Matrix

Make_matrix(): Make_matrix This will make the matixies of Cell VS mitochondrial variants and return redeemR Results stored in Cts.Mtx and Cts.Mtx.bi

AddDist(): AddDist This add Jaccard, Dice, Jaccard3W distance and stored in DistObjects

AddTree(): Add_Tree Optional, if a phylogentic tree object phylo is already available, can be directly added to the redeemR

Make_tree(): Make_tree This will generate a basic phylogenetic tree

create_rooted_tree(): Create Rooted Phylogenetic Tree from Distance Matrix

plot_tree_heatmap_teng_cw(): plot_tree_heatmap_teng_cw

plot_depth(): Function to plot the mito depth summary

plot_variant(): Function to plot variant metrics

plot_npSummary(): plot_npSummary to assess the outputlevel

Classes and S4 methods

redeemR object classes and method documentation.

redeemR-class redeemR: Major redeem class that store clonal-resolved multi-omics

mitoTracing-class mitoTracing: Legacy function: Major mitoTracing class that store clonal-resolved multi-omics

Datatoplots-class Datatoplots: An intermediate S4 class Datatoplots

DistObjects-class DistObjects: An intermediate S4 class Datatoplots

TREE-class TREE: An intermediate S4 class Tree that store tree info

Add_AssignVariant(): Add_AssignVariant a function to assign variants to edges based on maximum likihood

Add_DepthMatrix(): Add_DepthMatrix Optional, add a matrix with same dimension with the Cts.Mtx and Cts.Mtx.bi, which display the depths

Add_tree_cut(): Add_tree_cut a function to cut tree using assigned variant as branch-length on edge

AddDatatoplot_clustering(): AddDatatoplot_clustering This prepare the clonal clustering data to plot

SeuratLSIClustering(): SeuratLSIClustering This will use the mito variants for Seurat clustering (LSI based)

AddDatatoplot_clustering(<redeemR>): AddDatatoplot_clustering This prepare the clonal clustering data to plot

AddDist(<redeemR>): AddDist This add Jaccard, Dice, Jaccard3W distance and stored in DistObjects

AddTree(<redeemR>): Add_Tree Optional, if a phylogentic tree object phylo is already available, can be directly added to the redeemR class in slot TREE

Add_AssignVariant(<redeemR>): a function to assign variants to edges based on maximum likihood

Add_DepthMatrix(<redeemR>): Add_DepthMatrix Optional, add a matrix with same dimension with the Cts.Mtx and Cts.Mtx.bi, which display the depths

Add_tree_cut(<redeemR>): a function to cut tree using assigned variant as branch-length on edge

Make_matrix(<redeemR>): Make_matrix This will make the matixies of Cell VS mitochondrial variants and return redeemR Results stored in Cts.Mtx and Cts.Mtx.bi

Make_tree(<redeemR>): Make_tree This will generate a basic phylogenetic tree

SeuratLSIClustering(<redeemR>): SeuratLSIClustering This will use the mito variants for Seurat clustering (LSI based)

show(<redeemR>): show This will show the basics of redeemR class

Legacy and exploratory helpers

Older APIs, wrappers, plotting helpers, and project-specific utilities kept for backward compatibility.

ATAC_Wrapper(): Wrap Seurat ATAC clustering

AddDist_legacy(): AddDist_legacy This add Jaccard, Dice, Jaccard3W distance and stored in DistObjects

AddDist_legacy(<mitoTracing>): AddDist_legacy This add Jaccard, Dice, Jaccard3W distance and stored in DistObjects

AddHemSignature(): Function to add hematopoietic signatures from Griffin_Signatures

AddsigTag.6.new(): AddsigTag.6.new

Build_Manhattan_Tree(): Build Manhattan Tree

CW_mgatk.read(): Old Function to read in redeemV outputs It process the data same way as redeemR.read but simultanously reading in all threadhold as a list This function allows you to read raw data from XX/final folder, the output from redeemV

Clone_FinderMarker(): Define a function to perform Find marker for top vs bottom clones This function was developed based on DN4T2.basics.ipynb

ComputeRejectRate_legacy(): Function to compute the reject rate(The filtering rate in concensus variant calling)

CountOverlap_Adj(): CountOverlap_Adj function to count the connectedness (adjacency matrix), or the number of cells sharing more than n variants with the given cell

CountVperCell(): Internal function in plot_variant

Create_mitoTracing(): Create_mitoTracing

CV(): Internal CV

DE.gettripple(): DE.gettripple

DoDE(): DoDE

FromDist2Graph(): FromDist2Graph From distance object or matrix to graph, default is to return igraph object This function was developed based on

Fun.enrich_withFC(): Fun.enrich_withFC

Fun.enrich_withFC.pvalue(): Fun.enrich_withFC.pvalue

GEM_Wrapper(): Wrap Seurat RNA clustering

Get_Clonal_Variants(): Get_Clonal_Variants

Getgenenumber(): Getgenenumber

Getpallet2(): Getpallet2

LineageBiasPlot(): plot_npSummary to plot the lineage composition

MakeAllNodes(): Define a function make the Allnodes(Node|Parent|Freq|CladeSize), where Freq is the number of variants assigned to the node(as ending point) from redeemR object,

MakeDF4Regress(): MakeDF4Regress Define a function to make two dataframe for regression analysis This function was developed based on HSC_multiome_Het_2.ipynb

MakeNN(): Define a function to make nn list, which can be further used to make adjacency matrix This scan row by row, looking for k.param nearest neighbours

Make_AnnTable(): Make_AnnTable, Make a big dataframe, each row is a cell, each column includes info such as clonal UMAP, Clonal ID, ATAC/RNA/WNN UMAP, PCA, gene expression of chosen gene, etc. Require a redeemR object and a multiome wrapper that better matches the cells in the redeemR

Make_Cells4Nodes(): Define a function to make a list, each contains the cell names for a node

Make_tree_legacy(): Make_tree_legacy This will generate a basic phylogenetic tree

Make_tree_legacy(<mitoTracing>): Make_tree_legacy This will generate a basic phylogenetic tree

MergeMtx(): Function to Merge sparse Matrix

Motifenrich.binom(): Motifenrich.binom In house function to compute enrichment from Fimo This function was developed based on HSC_multiome_Het.ipynb and HSC_multiome_Het_2.ipynb

Multi_Wrapper(): Wrap Seurat Multiomics clustering

NN2M(): Define a function convert nn list to adjacency matrix that can be further used for igraph

ProgenyMapping(): Define a function to perform single-cell based hard porogeny assignment This function was developed based on DN4T2.basics.ipynb

ProgenyMapping_np(): ProgenyMapping_np Define a function to compute network propagation based probability FromDist2Graph is needed to convert fistance matrix into MNN graph

Reclustering(): Function to reclustering a seurat object

Reclustering_hm(): Function to reclustering_hm a seurat object with Harmony

Run_Lin_regression(): Run_Lin_regression

Run_Lin_regression_poi(): Run_Lin_regression_poi Firstly used in HSC_multiome_Het_2.ipynb This function was developed based on

Runplot_scale_2(): plot_npSummary to assess the outputlevel vs lineage bias, normalize by assigned

Runplot_scale_3(): plot_npSummary to assess the outputlevel vs lineage bias, normalize by HSC original clone size

Tomerge.col(): Tomerge.col

Tomerge_v2(): Tomerge_v2

Translate_RNA2ATAC() Translate_RNA2ATAC(): Function to translate the RNA barcode into ATAC barcode and add a column

Translate_simple_ATAC2RNA(): Translate_simple_ATAC2RNA

Translate_simple_RNA2ATAC(): Translate_simple_RNA2ATAC

Vfilter_v3(): Function to filter variants, deprecated

Vfilter_v4(): Function to filter variants, v4

add_derived_profile_info(): This is a convinience function, internal borrowed from https://github.com/NickWilliamsSanger/treemut/blob/main/R/treemut.R#L68

add_freq_raw(): Convinient function that takes raw fragment, and out put fragment with frequency Produce KS test results, input is the redeem object from redeemR object,

add_raw_fragment(): Produce a raw fragment table with frequency (how many cells) and the reletive distance from redeemR object,

append_dim_row(): Append redeemR matrix dimension snapshot

binomialtest.msig.enrch_deplet(): binomialtest.msig.enrch_deplet

clean_redeem_removehomo(): clean_redeem_removehomo

clean_redeem_removehot(): clean_redeem_removehotcall

convert_mitotracing_redeemR(): convert_mitotracing_redeemR

df2ProfileMtx(): This is a convinience function, internal

get_ancestral_nodes(): This is a convinience function, internal borrowed from https://github.com/NickWilliamsSanger/treemut/blob/main/R/treemut.R#L68

get_hype_v2(): get_hype_v2 This function annotates the redeem@V.fitered with hypermutable mutations_v2 (using 3 young donor and 2 aged donors)

gsea_enrichmentheat(): gsea_enrichmentheat

make_ks_test_df(): Produce KS test results, input is the redeem object from redeemR object,

make_position_df_3.4(): Function needed to compute the

make_position_df_3.5(): make_position, but remain all the raw genotyp information Input is the ind_df or

plot_depth_legacy(): Legacy Function to plot the mito depth summary

plot_variant_legacy(): Legacy Function to plot variant metrics

redeemR.read.multiple.trim(): Function to read in multiple runs of redeemV outputs with edge trimming, default is trimming 5bp

split_profile(): This is a convinience function, internal borrowed from https://github.com/NickWilliamsSanger/treemut/blob/main/R/treemut.R#L68

str2vector(): This is a convinience function, internal

theme_cw1(): Define a custom theme function

theme_cw2(): Define a custom theme function