| standardise_ctd | R Documentation |
Convert a CellTypeDataset into standardized format
Description
This function will take a CTD,
drop all genes without 1:1 orthologs with the
output_species ("human" by default),
convert the remaining genes to gene symbols,
assign names to each level,
and convert all matrices to sparse matrices and/or DelayedArray.
Usage
standardise_ctd(
ctd,
dataset,
input_species = NULL,
output_species = "human",
sctSpecies_origin = input_species,
non121_strategy = "drop_both_species",
method = "homologene",
force_new_quantiles = TRUE,
force_standardise = FALSE,
remove_unlabeled_clusters = FALSE,
numberOfBins = 40,
keep_annot = TRUE,
keep_plots = TRUE,
as_sparse = TRUE,
as_DelayedArray = FALSE,
rename_columns = TRUE,
make_columns_unique = FALSE,
verbose = TRUE,
...
)
Arguments
ctd |
Input CellTypeData.
|
dataset |
CellTypeData. name.
|
input_species |
Which species the gene names in exp come from.
See list_species for all available species.
|
output_species |
Which species' genes names to convert exp to.
See list_species for all available species.
|
sctSpecies_origin |
Species that the sct_data
originally came from, regardless of its current gene format
(e.g. it was previously converted from mouse to human gene orthologs).
This is used for computing an appropriate backgrund.
|
non121_strategy |
How to handle genes that don't have
1:1 mappings between input_species:output_species.
Options include:
"drop_both_species" or "dbs" or 1 :
Drop genes that have duplicate
mappings in either the input_species or output_species
(DEFAULT).
"drop_input_species" or "dis" or 2 :
Only drop genes that have duplicate
mappings in the input_species.
"drop_output_species" or "dos" or 3 :
Only drop genes that have duplicate
mappings in the output_species.
"keep_both_species" or "kbs" or 4 :
Keep all genes regardless of whether
they have duplicate mappings in either species.
"keep_popular" or "kp" or 5 :
Return only the most "popular" interspecies ortholog mappings.
This procedure tends to yield a greater number of returned genes
but at the cost of many of them not being true biological 1:1 orthologs.
"sum","mean","median","min" or "max" :
When gene_df is a matrix and gene_output="rownames",
these options will aggregate many-to-one gene mappings
(input_species-to-output_species)
after dropping any duplicate genes in the output_species.
|
method |
R package to use for gene mapping:
"gprofiler" : Slower but more species and genes.
"homologene" : Faster but fewer species and genes.
"babelgene" : Faster but fewer species and genes.
Also gives consensus scores for each gene mapping based on a
several different data sources.
|
force_new_quantiles |
By default, quantile computation is
skipped if they have already been computed.
Set =TRUE to override this and generate new quantiles.
|
force_standardise |
If ctd has already been standardised, whether
to rerun standardisation anyway (Default: FALSE).
|
remove_unlabeled_clusters |
Remove any samples that have
numeric column names.
|
numberOfBins |
Number of non-zero quantile bins.
|
keep_annot |
Keep the column annotation data if provided.
|
keep_plots |
Keep the dendrograms if provided.
|
as_sparse |
Convert to sparse matrix.
|
as_DelayedArray |
Convert to DelayedArray.
|
rename_columns |
Remove replace_chars from column names.
|
make_columns_unique |
Rename each columns with the prefix
dataset.species.celltype.
|
verbose |
Print messages.
Set verbose=2 if you want to print all messages
from internal functions as well.
|
... |
Arguments passed on to orthogene::convert_orthologs
gene_dfData object containing the genes
(see gene_input for options on how
the genes can be stored within the object).
Can be one of the following formats:
matrix :
A sparse or dense matrix.
data.frame :
A data.frame,
data.table. or tibble.
codelist :
A list or character vector.
Genes, transcripts, proteins, SNPs, or genomic ranges
can be provided in any format
(HGNC, Ensembl, RefSeq, UniProt, etc.) and will be
automatically converted to gene symbols unless
specified otherwise with the ... arguments.
Note: If you set method="homologene", you
must either supply genes in gene symbol format (e.g. "Sox2")
OR set standardise_genes=TRUE.
gene_inputWhich aspect of gene_df to
get gene names from:
"rownames" :
From row names of data.frame/matrix.
"colnames" :
From column names of data.frame/matrix.
<column name> :
From a column in gene_df,
e.g. "gene_names".
gene_outputHow to return genes.
Options include:
"rownames" :
As row names of gene_df.
"colnames" :
As column names of gene_df.
"columns" :
As new columns "input_gene", "ortholog_gene"
(and "input_gene_standard" if standardise_genes=TRUE)
in gene_df.
"dict" :
As a dictionary (named list) where the names
are input_gene and the values are ortholog_gene.
"dict_rev" :
As a reversed dictionary (named list)
where the names are ortholog_gene and the values are input_gene.
standardise_genesIf TRUE AND
gene_output="columns", a new column "input_gene_standard"
will be added to gene_df containing standardised HGNC symbols
identified by gorth.
drop_nonorthsDrop genes that don't have an ortholog
in the output_species.
agg_funAggregation function passed to
aggregate_mapped_genes.
Set to NULL to skip aggregation step (default).
mthresholdMaximum number of ortholog names per gene to show.
Passed to gorth.
Only used when method="gprofiler" (DEFAULT : Inf).
sort_rowsSort gene_df rows alphanumerically.
gene_mapA data.frame that maps the current gene names
to new gene names.
This function's behaviour will adapt to different situations as follows:
gene_map=<data.frame> :
When a data.frame containing the
gene key:value columns
(specified by input_col and output_col, respectively)
is provided, this will be used to perform aggregation/expansion.
gene_map=NULL and input_species!=output_species :
A gene_map is automatically generated by
map_orthologs to perform inter-species
gene aggregation/expansion.
gene_map=NULL and input_species==output_species :
A gene_map is automatically generated by
map_genes to perform within-species
gene gene symbol standardization and aggregation/expansion.
input_colColumn name within gene_map with gene names matching
the row names of X.
output_colColumn name within gene_map with gene names
that you wish you map the row names of X onto.
|
Value
Standardised CellTypeDataset.
Examples
ctd <- ewceData::ctd()
ctd_std <- EWCE::standardise_ctd(
ctd = ctd,
input_species = "mouse",
dataset = "Zeisel2016"
)
