Group specific features — group_specific

Identify features that are unique to selected groups (present in those groups but not in any others) and annotate them with gene names using the clusterProfiler::bitr() function. The function also provides an option to perform Gene Ontology (GO) enrichment analysis on the identified unique features using the enrichGO_list() function and visualize the results with a dot plot.

Usage

group_specific_features(
  property_random_fc,
  groups = NULL,
  filter_ratio = 0.5,
  group_pct = 1,
  genename = TRUE,
  GO = TRUE,
  OrgDb = NULL,
  keytype = NULL,
  ...
)

Arguments

property_random_fc: A data frame containing the results of the calc_feature_property() function, which includes the feature names, group names, and the proportion of expressed values for each feature in each group. The data frame should have at least the following columns: "Feature", "Group", "Exp_ratio" and "Exp_threshold".
groups: A character vector of group names to be compared. If NULL, all groups in the input data will be used (default is NULL).
filter_ratio: A numeric value between 0 and 1 specifying the minimum proportion of expressed time points (minimum Exp_ratio) for a feature to be considered present (default is 0.5, meaning that a feature must have >=50% values > threshold in a group (>= 0.5 Exp_ratio) to be considered present in that group).
group_pct: A numeric value between 0 and 1 specifying the percentage of groups in which a feature must be present. (default is 1, meaning that a feature must be present in all specified groups).
genename: A logical value indicating whether to output a table of gene names. If TRUE, the function will use the clusterProfiler::bitr() function to annotate the features with gene names based on the specified OrgDb and keytype. (default is TRUE).
GO: A logical value indicating whether to perform Gene Ontology (GO) enrichment analysis on the identified unique features. If TRUE, the function will use the enrichGO_list() function to perform GO enrichment analysis and visualize the results with a dot plot. (default is TRUE).
OrgDb: An OrgDb object from the AnnotationDbi package corresponding to the organism of interest (e.g., org.Hs.eg.db for human, org.Mm.eg.db for mouse). This will be used for gene annotation with the clusterProfiler::bitr() function.
keytype: A character string specifying the type of gene identifiers used in the row names of the assay data (e.g., "SYMBOL", "ENTREZID", "ENSEMBL"). This will be used for gene annotation with the clusterProfiler::bitr() function. Available key types depend on the OrgDb database and can be checked with the AnnotationDbi::keytypes function.
...: Additional arguments to be passed to the enrichGO_list() function for GO enrichment analysis (e.g., pvalueCutoff, qvalueCutoff, etc.).

Value

A named list with element features (character vector of features identified as unique to the specified groups based on the filtering criteria). If genename is TRUE, element genename contains a data.frame of gene annotations. If GO is TRUE and enrichment succeeds, element GO contains a dot plot of GO enrichment results. Returns NULL if no unique features pass the filter.

Examples

data(example_obj)
example_obj <- normalise_to_start(example_obj)
#> Normalising to group baseline at each feature's first non-NA time point.
example_obj_list <- split_groups(example_obj)
example_obj_merged_list <- merge_replicates(example_obj_list)

example_obj_merged_list <-
    calc_feature_property(example_obj_merged_list, threshold = 0)
property_random_fc <- summarise_feature_property(example_obj_merged_list)

group_specific_features(property_random_fc, groups = c("untreated"),
    genename = FALSE, GO = FALSE)
#> Filtering criteria: >=50% values >0 in >=1 of groups: untreated
#> No unique features with the specified filter and groups.
#> NULL