Filter non-orthologs

Source: R/filter_nonorthologs.R

filter_nonorthologs Takes the filenames of CellTypeData files, loads them, drops any genes which don't have a 1:1 orthologs with humans, and then convert the gene to human orthologs. The new files are then saved to disk, appending '_orthologs' to the file name.

filter_nonorthologs(
  filenames,
  input_species = NULL,
  convert_nonhuman_genes = TRUE,
  annot_levels = NULL,
  suffix = "_orthologs",
  method = "homologene",
  non121_strategy = "drop_both_species",
  verbose = TRUE,
  ...
)

Arguments

filenames

List of file names for sct_data saved as .rda files.

input_species

Which species the gene names in exp come from.

convert_nonhuman_genes

Whether to convert the exp row names to human gene names.

annot_levels

[Optional] Names of each annotation level.

suffix

Suffix to add to the file name (right before .rda).

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.

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.

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.

list

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.

output_species

Name of the output species (e.g. "human","chicken"). Use map_species to return a full list of available species.

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).

as_sparse

Convert gene_df to a sparse matrix. Only works if gene_df is one of the following classes:

  • matrix

  • Matrix

  • data.frame

  • data.table

  • tibble

If gene_df is a sparse matrix to begin with, it will be returned as a sparse matrix (so long as gene_output= "rownames" or "colnames").

as_DelayedArray

Convert aggregated matrix to DelayedArray.

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

List of the filtered CellTypeData file names.

Details

Note: This function replaces the original filter_genes_without_1to1_homolog function. filter_genes_without_1to1_homolog is now a wrapper for filter_nonorthologs.

Examples

# Load the single cell data
ctd <- ewceData::ctd()
#> see ?ewceData and browseVignettes('ewceData') for documentation
#> loading from cache
tmp <- tempfile()
save(ctd, file = tmp)
fNames_ALLCELLS_orths <- EWCE::filter_nonorthologs(filenames = tmp)
#> No input_species provided. Setting to 'mouse'
#> + Processing level 1 ...
#> Processing mean_exp
#> Processing median_exp
#> Processing specificity
#> Processing median_specificity
#> Processing specificity_quantiles
#> + Processing level 2 ...
#> Processing mean_exp
#> Processing median_exp
#> Processing specificity
#> Processing median_specificity
#> Processing specificity_quantiles