Base Backend: dandelion.base

Preprocessing: pp

assign_isotype(fasta[, org, evalue, ...])	Annotate contigs with constant region call using blastn.
assign_isotypes(fastas[, org, evalue, ...])	Annotate contigs with constant region call using blastn.
check_contigs(data[, adata, ...])	Check contigs for whether they can be considered as ambiguous or not.
format_fasta(fasta[, prefix, suffix, sep, ...])	Add prefix to the headers/contig ids in input fasta and annotation file.
format_fastas(fastas[, prefix, suffix, sep, ...])	Add prefix to the headers/contig ids in input fasta and annotation file.
reannotate_genes(data[, igblast_db, ...])	Reannotate cellranger fasta files with igblastn and parses to airr format.
reassign_alleles(data, combined_folder[, ...])	Correct allele calls based on a personalized genotype using tigger.

Tools: tl

clone_centrality(vdj)	Calculate node closeness centrality in BCR/TCR network.
clone_degree(vdj[, weight])	Calculate node degree in BCR/TCR network.
clone_diversity(data, group_by[, method, ...])	Compute clonal diversity with bootstrapping.
clone_overlap(vdj, group_by[, ...])	A function to tabulate clonal overlap for input as a circos-style plot.
clone_rarefaction(data, group_by[, ...])	Compute sample-based rarefaction curves with asymptotic extrapolation and optional plotting.
clone_size(vdj[, group_by, max_size, ...])	Quantify clone sizes, globally or per group.
clone_view(adata[, mode, ...])	Swap the 'active' connectivities, distances, and optionally embedding in AnnData.
concat(arrays[, check_unique, ...])	Concatenate data frames and return as Dandelion object.
define_clones(vdj, dist[, action, model, ...])	Find clones using changeo's DefineClones.py.
extract_edge_weights(vdj[, expanded_only])	Retrieve edge weights from graph.
find_clones(vdj[, identity, key, ...])	Find clones based on VDJ chain and VJ chain CDR3 junction hamming distance.
from_scirpy(data)	Convert data from scirpy format to Dandelion format.
generate_network(vdj[, adata, key, ...])	Generate a Levenshtein distance network based on VDJ and VJ sequences.
productive_ratio(adata, vdj, group_by[, ...])	Compute the cell-level productive/non-productive contig ratio.
project_pseudotime_to_cell(adata, pb_adata, ...)	Function to project pseudotime & branch probabilities from pb_adata (pseudobulk adata) to adata (cell adata).
pseudobulk_gex(adata_raw[, pbs, ...])	Function to pseudobulk gene expression (raw count).
pseudotime_transfer(adata, pr_res[, suffix])	Function to add pseudotime and branch probabilities into adata.obs in place.
setup_vdj_pseudobulk(adata[, mode, ...])	Function for prepare anndata for computing pseudobulk vdj feature space.
to_scirpy(vdj[, transfer, to_mudata, ...])	Convert Dandelion data to scirpy-compatible format.
transfer(adata, vdj[, expanded, gex_key, ...])
vdj_pseudobulk(adata[, pbs, obs_to_bulk, ...])	Function for making pseudobulk vdj feature space.
vdj_sample(vdj_data[, size, adata, p, ...])	Resample vdj data and corresponding AnnData to a specified size.
vj_usage_pca(adata, group_by[, min_size, ...])	Extract productive V/J gene usage from single cell data and compute PCA.

Plotting: pl

barplot(data, color[, palette, figsize, ...])	A barplot function to plot usage of V/J genes in the data.
clone_circlepackplot(data, group_by[, ...])	A bubble plot to visualise clone sizes within groups using circle packing.
clone_network(adata[, basis, edges])	Using scanpy's plotting module to plot the network.
clone_overlap(adata, group_by[, color_by, ...])	A plot function to visualise clonal overlap as a circos-style plot.
productive_ratio(adata[, figsize, palette, ...])	Plot productive/non-productive contig ratio from AnnData (cell level).
spectratype(vdj, color, group_by, locus[, ...])	A spectratype function to plot usage of CDR3 length.
stackedbarplot(data, color, group_by[, ...])	A stacked bar plot function to plot usage of V/J genes in the data split by groups.

Reading: io

read([filename, distance_zarr, verbose])	Read in and returns a Dandelion class from .h5ddl format.
read_10x_airr(file[, prefix, suffix, sep, ...])	Read the airr_rearrangement.tsv produced from Cell Ranger directly and returns a Dandelion object.
read_10x_vdj(data[, filename_prefix, ...])	A parser to read .csv and .json files directly from folder containing 10x cellranger-outputs, or parse an existing pandas DataFrame.
read_airr(file[, prefix, suffix, sep, ...])	Reads a standard single-cell AIRR rearrangement file.
read_bd_airr(file[, prefix, suffix, sep, ...])	Read the TCR or BCR _AIRR.tsv produced from BD Rhapsody technology.
read_parse_airr(file[, prefix, suffix, sep, ...])	Read the TCR or BCR _annotation_airr.tsv produced from Parse Biosciences Evercode technology.
read_seekgene_vdj(data[, filename_prefix, ...])	A parser to read .csv and .json files directly from folder containing SeekGene VDJ outputs, or parse an existing pandas DataFrame.
read_ddl([filename, distance_zarr, verbose])	Read in and returns a Dandelion class from .h5ddl format.
read_h5ddl([filename, distance_zarr, verbose])	Read in and returns a Dandelion class from .h5ddl format.

Dandelion Class

Dandelion.add_cell_prefix(prefix[, sync, ...])	Add prefix to cell_id and optionally to sequence_id.
Dandelion.add_cell_suffix(suffix[, sync, ...])	Add suffix to cell_id and optionally to sequence_id.
Dandelion.add_sequence_prefix(prefix[, ...])	Add prefix to sequence_id and then apply to cell_id as well.
Dandelion.add_sequence_suffix(suffix[, ...])	Add suffix to sequence_id and then apply to cell_id as well.
Dandelion.compute()	Convert self.distances to a concrete csr matrix.
Dandelion.copy()	Performs a deep copy of all slots in Dandelion class.
Dandelion.data	One-dimensional annotation of contig observations.
Dandelion.data_names	Names of observations (alias for .data.index).
Dandelion.metadata	One-dimensional annotation of cell observations.
Dandelion.metadata_names	Names of observations (alias for .metadata.index).
Dandelion.simplify(**kwargs)	Disambiguate VDJ and C gene calls when there's multiple calls separated by commas and strip the alleles.
Dandelion.store_germline_reference([...])	Update germline reference with corrected sequences and store in Dandelion object.
Dandelion.update_data([skip])	Sync missing metadata columns into data via dictionary mapping.
Dandelion.update_metadata([retrieve, ...])	A Dandelion initialisation function to update and populate the .metadata slot.
Dandelion.update_plus([option])	Retrieve additional data columns that are useful.
Dandelion.write([filename, compression, ...])	Writes a Dandelion class to .h5ddl format.
Dandelion.write_10x([folder, ...])	Alias for write_vdj() kept for backwards compatibility.
Dandelion.write_airr([filename])	Writes a Dandelion class to AIRR formatted .tsv format.
Dandelion.write_ddl([filename, compression, ...])	Writes a Dandelion class to .h5ddl format.
Dandelion.write_h5ddl([filename, ...])	Writes a Dandelion class to .h5ddl format.
Dandelion.write_vdj([folder, ...])	Writes a Dandelion object to contig-annotation formatted files compatible with multiple platforms (10x Genomics, SeekGene, etc.) so that it can be ingested by other tools.

Polars Backend: dandelion.polars

Preprocessing: pp

assign_isotype(fasta[, org, evalue, ...])	Annotate contigs with constant region call using blastn.
assign_isotypes(fastas[, org, evalue, ...])	Annotate contigs with constant region call using blastn.
check_contigs(vdj[, adata, productive_only, ...])	Check contigs for whether they can be considered as ambiguous or not.
format_fasta(fasta[, prefix, suffix, sep, ...])	Add prefix to the headers/contig ids in input fasta and annotation file.
format_fastas(fastas[, prefix, suffix, sep, ...])	Add prefix to the headers/contig ids in input fasta and annotation file.
reannotate_genes(data[, igblast_db, ...])	Reannotate cellranger fasta files with igblastn and parses to airr format.
reassign_alleles(data, combined_folder[, ...])	Correct allele calls based on a personalized genotype using tigger.

Tools: tl

clone_centrality(vdj)	Calculate node closeness centrality in BCR/TCR network.
clone_degree(vdj[, weight])	Calculate node degree in BCR/TCR network.
clone_diversity(data, group_by[, method, ...])	Compute clonal diversity with bootstrapping.
clone_overlap(vdj, group_by[, ...])	A function to tabulate clonal overlap for input as a circos-style plot.
clone_rarefaction(data, group_by[, ...])	Compute sample-based rarefaction curves with asymptotic extrapolation and optional plotting.
clone_size(vdj[, group_by, max_size, ...])	Quantify clone sizes, globally or per group.
clone_view(adata[, mode, ...])	Swap the 'active' connectivities, distances, and optionally embedding in AnnData.
concat(arrays[, check_unique, ...])	Concatenate data frames and return as Dandelion object.
define_clones(vdj, dist[, action, model, ...])	Find clones using changeo's DefineClones.py.
extract_edge_weights(vdj[, expanded_only])	Retrieve edge weights from graph.
find_clones(vdj[, identity, hard_cutoff, ...])	Find clones based on VDJ chain and VJ chain CDR3 junction hamming distance.
from_scirpy(data)	Convert data from scirpy format to Dandelion format.
generate_network(vdj[, adata, key, ...])	Generate a Levenshtein distance network based on VDJ and VJ sequences.
productive_ratio(adata, vdj, group_by[, ...])	Compute the cell-level productive/non-productive contig ratio.
project_pseudotime_to_cell(adata, pb_adata, ...)	Function to project pseudotime & branch probabilities from pb_adata (pseudobulk adata) to adata (cell adata).
pseudobulk_gex(adata_raw[, pbs, ...])	Function to pseudobulk gene expression (raw count).
pseudotime_transfer(adata, pr_res[, suffix])	Function to add pseudotime and branch probabilities into adata.obs in place.
setup_vdj_pseudobulk(adata, vdj[, mode, ...])	Function to prepare AnnData for computing pseudobulk vdj feature space.
to_scirpy(data[, transfer, to_mudata, ...])	Convert Dandelion data to scirpy-compatible format.
transfer(adata, vdj[, main_view, gex_key, ...])
vdj_pseudobulk(adata[, vdj, pbs, ...])	Function for making pseudobulk vdj feature space.
vdj_sample(vdj_data[, size, adata, p, ...])	Resample vdj data and corresponding AnnData to a specified size.
vj_usage_pca(adata, vdj, group_by[, ...])	Extract productive V/J gene usage from single cell data and compute PCA.

Plotting: pl

barplot(data, color[, palette, figsize, ...])	A barplot function to plot usage of V/J genes in the data.
clone_circlepackplot(data, group_by[, ...])	A bubble plot to visualise clone sizes within groups using circle packing.
clone_network(adata[, basis, edges])	Using scanpy's plotting module to plot the network.
clone_overlap(adata, group_by[, color_by, ...])	A plot function to visualise clonal overlap as a circos-style plot.
productive_ratio(adata[, figsize, palette, ...])	Plot productive/non-productive contig ratio from AnnData (cell level).
spectratype(vdj, color, group_by, locus[, ...])	A spectratype function to plot usage of CDR3 length.
stackedbarplot(data, color, group_by[, ...])	A stacked bar plot function to plot usage of V/J genes in the data split by groups.

Reading: io

read(filename[, distance_zarr, verbose])	Read a Dandelion object from a .zipddl file (hybrid Zarr v3 ZipStore container).
read_10x_airr(file[, prefix, suffix, sep, ...])	Read the airr_rearrangement.tsv produced from Cell Ranger directly and returns a DandelionPolars object.
read_10x_vdj([data, filename_prefix, ...])	A parser to read .csv and .json files directly from folder containing 10x cellranger-outputs, or parse an existing pandas/polars DataFrame.
read_airr(file[, prefix, suffix, sep, ...])	Reads a standard single-cell AIRR rearrangement file.
read_bd_airr(file[, prefix, suffix, sep, ...])	Read the TCR or BCR _AIRR.tsv produced from BD Rhapsody technology.
read_parse_airr(file[, prefix, suffix, sep, ...])	Read the TCR or BCR _annotation_airr.tsv produced from Parse Biosciences Evercode technology.
read_seekgene_vdj([data, filename_prefix, ...])	A parser to read .csv and .json files directly from folder containing SeekGene VDJ outputs, or parse an existing pandas/polars DataFrame.
read_ddl(filename[, distance_zarr, verbose])	Read a Dandelion object from a .zipddl file (hybrid Zarr v3 ZipStore container).
read_h5ddl([filename, distance_zarr, verbose])	Read in and returns a Dandelion class from .h5ddl format.
read_zipddl(filename[, distance_zarr, verbose])	Read a Dandelion object from a .zipddl file (hybrid Zarr v3 ZipStore container).

Dandelion Class

Dandelion.add_cell_prefix(prefix[, sync, ...])	Add prefix to cell_id and optionally to sequence_id.
Dandelion.add_cell_suffix(suffix[, sync, ...])	Add suffix to cell_id and optionally to sequence_id.
Dandelion.add_sequence_prefix(prefix[, ...])	Add prefix to sequence_id and then apply to cell_id as well.
Dandelion.add_sequence_suffix(suffix[, ...])	Add suffix to sequence_id and then apply to cell_id as well.
Dandelion.clone()	Polars-style clone: duplicate frames and state without sharing cache handles.
Dandelion.compute()	Convert self.distances to a concrete csr matrix.
Dandelion.copy()	Performs a deep copy of all slots in Dandelion class.
Dandelion.data	One-dimensional annotation of contig observations.
Dandelion.data_names	Names of contig observations.
Dandelion.metadata	One-dimensional annotation of cell observations.
Dandelion.metadata_names	Names of cell observations.
Dandelion.n_contigs	Number of contigs.
Dandelion.n_obs	Number of observations.
Dandelion.reset_ids()	Reset both IDs to their original values.
Dandelion.simplify(**kwargs)	Disambiguate VDJ and C gene calls when there's multiple calls separated by commas and strip the alleles.
Dandelion.store_germline_reference([...])	Update germline reference with corrected sequences and store in Dandelion object.
Dandelion.to_anndata()	Convert DandelionPolars.metadata to AnnData.
Dandelion.to_eager()	Convert lazy slots to eager slots.
Dandelion.to_lazy(*[, chunks])	Convert eager slots to lazy slots.
Dandelion.to_pandas()	Convert self from Polars to Pandas implementation.
Dandelion.to_polars([lazy])	Convert self from Pandas to Polars implementation.
Dandelion.update_data([skip])	Sync metadata columns into data via dictionary mapping.
Dandelion.update_metadata([retrieve, ...])	A Dandelion initialisation function to update and populate the .metadata slot.
Dandelion.update_plus([option])	Retrieve additional data columns that are useful.
Dandelion.write([filename, compress])	Write a Dandelion object to a single .zipddl file (Zarr v3 ZipStore, hybrid storage) with optional compression.
Dandelion.write_10x([folder, ...])	Alias for write_vdj() kept for backwards compatibility.
Dandelion.write_airr([filename])	Writes a Dandelion class to AIRR formatted .tsv format.
Dandelion.write_ddl([filename, compress])	Write a Dandelion object to a single .zipddl file (Zarr v3 ZipStore, hybrid storage) with optional compression.
Dandelion.write_h5ddl([filename, ...])	Write a Dandelion object to .h5ddl format.
Dandelion.write_vdj([folder, ...])	Writes a DandelionPolars object to contig-annotation formatted files compatible with multiple platforms (10x Genomics, SeekGene, etc.) so that it can be ingested by other tools.
Dandelion.write_zipddl([filename, compress])	Write a Dandelion object to a single .zipddl file (Zarr v3 ZipStore, hybrid storage) with optional compression.

Utilities

extract_edge_weights(vdj[, expanded_only])	Retrieve edge weights from graph.
makeblastdb(ref)	Run makeblastdb on constant region fasta file.

Tutorial

setup_dandelion_tutorial_bcr([path])	Download example BCR datasets for Dandelion tutorial.
setup_dandelion_tutorial_parse([path])	Download the extremely large dataset from Parse Biosciences for Dandelion tutorial.
setup_dandelion_tutorial_tcr([path])	Download example TCR datasets for Dandelion tutorial.
setup_dandelion_tutorial_trajectory([path])	Download example datasets for Dandelion V(D)J trajectory tutorial.

Set Backend

set_backend(mode)

Override the active backend at runtime.

Logging

print_header([dependencies])	Versions that are essential for dandelion's operation.
print_versions([dependencies])	Versions that are essential for dandelion's operation.

External

scanpy

recipe_scanpy_qc(adata[, layer, ...])

Recipe for running a standard scanpy QC workflow.

Immcantation

Wrappers for tools in Immcantation pipeline.

Base

changeo

assigngenes_igblast(fasta[, igblast_db, ...])	Reannotate with IgBLASTn.
creategermlines(airr_file[, germline, org, ...])	Wrapper for CreateGermlines.py for reconstructing germline sequences.
makedb_igblast(fasta[, igblast_output, ...])	Parse IgBLAST output to AIRR format.
parsedb_heavy(airr_file)	Parse AIRR tsv file (heavy chain contigs only).
parsedb_light(airr_file)	Parse AIRR tsv file (light chain contigs only).

shazam

calculate_threshold(data[, mode, ...])	Calculating nearest neighbor distances for tuning clonal assignment with shazam.
quantify_mutations(data[, split_locus, ...])	Run basic mutation load analysis.

scoper

identical_clones(vdj[, method, junction, ...])	Clonal assignment using sequence identity partitioning.
hierarchical_clones(vdj, threshold[, ...])	Hierarchical clustering approach to clonal assignment.
spectral_clones(vdj[, method, germline, ...])	Spectral clustering method for clonal partitioning.

Polars

changeo

assigngenes_igblast(fasta[, igblast_db, ...])	Reannotate with IgBLASTn.
creategermlines(airr_file[, germline, org, ...])	Wrapper for CreateGermlines.py for reconstructing germline sequences.
makedb_igblast(fasta[, igblast_output, ...])	Parse IgBLAST output to AIRR format.
parsedb_heavy(airr_file)	Parse AIRR tsv file (heavy chain contigs only).
parsedb_light(airr_file)	Parse AIRR tsv file (light chain contigs only).

shazam

calculate_threshold(data[, mode, ...])	Calculating nearest neighbor distances for tuning clonal assignment with shazam.
quantify_mutations(data[, split_locus, ...])	Run basic mutation load analysis.

scoper

identical_clones(vdj[, method, junction, ...])	Clonal assignment using sequence identity partitioning with Polars.
hierarchical_clones(vdj, threshold[, ...])	Hierarchical clustering approach to clonal assignment with Polars.
spectral_clones(vdj[, method, germline, ...])	Spectral clustering method for clonal partitioning with Polars.

tigger

tigger_genotype(airr_file[, v_germline, ...])

Reassign alleles with TIgGER in R.