simplifyGOFromMultipleLists: Perform simplifyGO analysis with multiple lists of GO IDs

In jokergoo/simplifyEnrichment: Simplify Functional Enrichment Results

Perform simplifyGO analysis with multiple lists of GO IDs

Description

Perform simplifyGO analysis with multiple lists of GO IDs

Usage

simplifyGOFromMultipleLists(
  lt,
  go_id_column = NULL,
  padj_column = NULL,
  padj_cutoff = 0.01,
  filter = function(x) any(x < padj_cutoff),
  default = 1,
  ont = NULL,
  db = "org.Hs.eg.db",
  measure = "Sim_XGraSM_2013",
  heatmap_param = list(NULL),
  show_barplot = TRUE,
  method = "binary_cut",
  control = list(),
  min_term = NULL,
  verbose = TRUE,
  column_title = NULL,
  ...
)

Arguments

lt	A data frame, a list of numeric vectors (e.g. adjusted p-values) where each numeric vector has GO IDs as names, or a list of GO IDs.
go_id_column	Column index of GO ID if lt contains a list of data frames.
padj_column	Column index of adjusted p-values if lt contains a list of data frames.
padj_cutoff	Cut off for adjusted p-values.
filter	A self-defined function for filtering GO IDs. By default it requires GO IDs should be significant in at least one list.
default	The default value for the adjusted p-values. See Details.
ont	Pass to GO_similarity().
db	Pass to GO_similarity().
measure	Pass to GO_similarity().
heatmap_param	Parameters for controlling the heatmap, see Details.
show_barplot	Whether draw barplots which shows numbers of significant GO terms in clusters.
method	Pass to simplifyGO().
control	Pass to simplifyGO().
min_term	Pass to simplifyGO().
verbose	Pass to simplifyGO().
column_title	Pass to simplifyGO().
...	Pass to simplifyGO().

Details

The input data can have three types of formats:

• A list of numeric vectors of adjusted p-values where each vector has the GO IDs as names.

• A data frame. The column of the GO IDs can be specified with go_id_column argument and the column of the adjusted p-values can be specified with padj_column argument. If these columns are not specified, they are automatically identified. The GO ID column is found by checking whether a column contains all GO IDs. The adjusted p-value column is found by comparing the column names of the data frame to see whether it might be a column for adjusted p-values. These two columns are used to construct a numeric vector with GO IDs as names.

• A list of character vectors of GO IDs. In this case, each character vector is changed to a numeric vector where all values take 1 and the original GO IDs are used as names of the vector.

Now let's assume there are n GO lists, we first construct a global matrix where columns correspond to the n GO lists and rows correspond to the "union" of all GO IDs in the lists. The value for the ith GO ID and in the jth list are taken from the corresponding numeric vector in lt. If the jth vector in lt does not contain the ith GO ID, the value defined by default argument is taken there (e.g. in most cases the numeric values are adjusted p-values, default is set to 1). Let's call this matrix as M0.

Next step is to filter M0 so that we only take a subset of GO IDs of interest. We define a proper function via argument filter to remove GO IDs that are not important for the analysis. Functions for filter is applied to every row in M0 and filter function needs to return a logical value to decide whether to remove the current GO ID. For example, if the values in lt are adjusted p-values, the filter function can be set as function(x) any(x < padj_cutoff) so that the GO ID is kept as long as it is signfiicant in at least one list. After the filter, let's call the filtered matrix M1.

GO IDs in M1 (row names of M1) are used for clustering. A heatmap of M1 is attached to the left of the GO similarity heatmap so that the group-specific (or list-specific) patterns can be easily observed and to corresponded to GO functions.

Argument heatmap_param controls several parameters for heatmap M1:

• transform: A self-defined function to transform the data for heatmap visualization. The most typical case is to transform adjusted p-values by -log10(x).

• breaks: break values for color interpolation.

• col: The corresponding values for breaks.

• labels: The corresponding labels.

• name: Legend title.

Examples

# perform functional enrichment on the signatures genes from cola anlaysis 
require(cola)
data(golub_cola) 
res = golub_cola["ATC:skmeans"]
require(hu6800.db)
x = hu6800ENTREZID
mapped_probes = mappedkeys(x)
id_mapping = unlist(as.list(x[mapped_probes]))
lt = functional_enrichment(res, k = 3, id_mapping = id_mapping) # you can check the value of `lt`

# a list of data frames
simplifyGOFromMultipleLists(lt, padj_cutoff = 0.001)

# a list of numeric values
lt2 = lapply(lt, function(x) structure(x$p.adjust, names = x$ID))
simplifyGOFromMultipleLists(lt2, padj_cutoff = 0.001)

# a list of GO IDS
lt3 = lapply(lt, function(x) x$ID[x$p.adjust < 0.001])
simplifyGOFromMultipleLists(lt3)

jokergoo/simplifyEnrichment documentation built on Sept. 16, 2024, 8:39 a.m.