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.
standardise_ctd(
ctd,
dataset,
input_species = NULL,
output_species = "human",
non121_strategy = "drop_both_species",
force_new_quantiles = TRUE,
remove_unlabeled_clusters = FALSE,
numberOfBins = 40,
keep_annot = TRUE,
keep_plots = TRUE,
as_sparse = TRUE,
as_DelayedArray = FALSE,
verbose = TRUE
)
Arguments
| ctd |
Input CellTypeData. |
| dataset |
CellTypeData. name. |
| input_species |
Which species the gene names in exp come from. |
| output_species |
Which species' genes names to convert exp to. |
| 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.
|
| force_new_quantiles |
By default, quantile computation is
skipped if they have already been computed.
Set =TRUE to override this and generate new quantiles. |
| 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. |
| verbose |
Print messages. |
Value
Standardised CellTypeDataset.
Examples
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
ctd_std <- standardise_ctd(
ctd = ctd,
input_species = "mouse",
dataset = "Zeisel2016"
)
#> Standardising CellTypeDataset
#> Level: 1
#> Extracting mean_exp
#> Converting to sparse matrix.
#> Matrix dimensions: 13243 x 7
#> Extracting specificity
#> Converting to sparse matrix.
#> Matrix dimensions: 13243 x 7
#> Extracting specificity_quantiles
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Matrix dimensions: 13243 x 7
#> Level: 2
#> Extracting mean_exp
#> Converting to sparse matrix.
#> Matrix dimensions: 13243 x 48
#> Extracting specificity
#> Converting to sparse matrix.
#> Matrix dimensions: 13243 x 48
#> Extracting specificity_quantiles
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Matrix dimensions: 13243 x 48