Generate CellTypeData (CTD) file

Source: R/generate_celltype_data.r

generate_celltype_data takes gene expression data and cell type annotations and creates CellTypeData (CTD) files which contain matrices of mean expression and specificity per cell type.

generate_celltype_data(
  exp,
  annotLevels,
  groupName,
  no_cores = 1,
  savePath = tempdir(),
  file_prefix = "ctd",
  as_sparse = TRUE,
  as_DelayedArray = FALSE,
  normSpec = FALSE,
  convert_orths = FALSE,
  input_species = "mouse",
  output_species = "human",
  non121_strategy = "drop_both_species",
  method = "homologene",
  force_new_file = TRUE,
  specificity_quantiles = TRUE,
  numberOfBins = 40,
  dendrograms = TRUE,
  return_ctd = FALSE,
  verbose = TRUE,
  ...
)

Arguments

exp

Numerical matrix with row for each gene and column for each cell. Row names are gene symbols. Column names are cell IDs which can be cross referenced against the annot data frame.

annotLevels

List with arrays of strings containing the cell type names associated with each column in exp.

groupName

A human readable name for referring to the dataset being used.

no_cores

Number of cores that should be used to speedup the computation. NOTE: Use no_cores=1 when using this package in windows system.

savePath

Directory where the CTD file should be saved.

file_prefix

Prefix to add to saved CTD file name.

as_sparse

Convert exp to a sparse Matrix.

as_DelayedArray

Convert exp to DelayedArray.

normSpec

Boolean indicating whether specificity data should be transformed to a normal distribution by cell type, giving equivalent scores across all cell types.

convert_orths

If input_species!=output_species and convert_orths=TRUE, will drop genes without 1:1 output_species orthologs and then convert exp gene names to those of output_species.

input_species

The species that the exp dataset comes from. See list_species for all available species.

output_species

Species to convert exp to (Default: "human"). See list_species for all available species.

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_file

If a file of the same name as the one being created already exists, overwrite it.

specificity_quantiles

Compute specificity quantiles. Recommended to set to TRUE.

numberOfBins

Number of quantile 'bins' to use (40 is recommended).

dendrograms

Add dendrogram plots

return_ctd

Return the CTD object in a list along with the file name, instead of just the file name.

verbose

Print messages.

...

Arguments passed on to orthogene::convert_orthologs

gene_df

Data 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_input

Which 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_output

How 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_genes

If 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_nonorths

Drop genes that don't have an ortholog in the output_species.

agg_fun

Aggregation function passed to aggregate_mapped_genes. Set to NULL to skip aggregation step (default).

mthreshold

Maximum number of ortholog names per gene to show. Passed to gorth. Only used when method="gprofiler" (DEFAULT : Inf).

sort_rows

Sort gene_df rows alphanumerically.

gene_map

A 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_col

Column name within gene_map with gene names matching the row names of X.

output_col

Column name within gene_map with gene names that you wish you map the row names of X onto.

Value

File names for the saved CellTypeData (CTD) files.

Examples

# Load the single cell data
cortex_mrna <- ewceData::cortex_mrna()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
# Use only a subset to keep the example quick
expData <- cortex_mrna$exp[1:100, ]
l1 <- cortex_mrna$annot$level1class
l2 <- cortex_mrna$annot$level2class
annotLevels <- list(l1 = l1, l2 = l2)
fNames_ALLCELLS <- EWCE::generate_celltype_data(
    exp = expData,
    annotLevels = annotLevels,
    groupName = "allKImouse"
)
#> 1 core(s) assigned as workers (3 reserved).
#> Converting to sparse matrix.
#> + Calculating normalized mean expression.
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> + Calculating normalized specificity.
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Converting to sparse matrix.
#> Loading required namespace: ggdendro
#> + Saving results ==>  /tmp/RtmpuXbkZF/ctd_allKImouse.rda