adjust_zstat_in_genesOut: Adjust MAGMA Z-statistic from _.genes.out_ files

In neurogenomics/MAGMA_Celltyping: Find Causal Cell-Types Underlying Complex Trait Genetics

Adjust MAGMA Z-statistic from .genes.out files

Description

Used when you want to directly analyse the gene-level Z-scores for a given GWAS while correcting for known confounding variables such as:

• NSNPS : Number of SNPs

• NPARAM : Number of parameters?

• GENELEN : Gene length

• log*** : The logged version of each of the above variables, using the default log function.

Usage

adjust_zstat_in_genesOut(
  magma_GenesOut_file,
  ctd = NULL,
  ctd_species = infer_ctd_species(ctd),
  prepare_ctd = TRUE,
  method = "bonferroni",
  verbose = TRUE,
  ...
)

Arguments

magma_GenesOut_file	A MAGMA .genes.out file generated by map_snps_to_genes.
ctd	Cell type data structure containing specificity_quantiles.
ctd_species	Species name relevant to the CellTypeDataset (ctd). See list_species for all available species. If ctd_species=NULL (default), the ctd species will automatically be inferred using infer_species.
prepare_ctd	Whether to run prepare_quantile_groups on the ctd first.
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.
verbose	Print messages.
...	Arguments passed on to EWCE::standardise_ctd 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. 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.

Examples

myGenesOut <- MAGMA.Celltyping::import_magma_files(
    ids = c("ieu-a-298"),
    file_types = ".genes.out",
    return_dir = FALSE)
ctd <- ewceData::ctd()

magmaGenesOut <- MAGMA.Celltyping::adjust_zstat_in_genesOut(
    ctd = ctd,
    magma_GenesOut_file = myGenesOut,
    ctd_species = "mouse"
)

neurogenomics/MAGMA_Celltyping documentation built on Oct. 12, 2024, 12:36 a.m.