fcor: Fast correlation and mutual rank analysis

In influential: Identification and Classification of the Most Influential Nodes

Fast correlation and mutual rank analysis

Description

This function calculates Pearson/Spearman correlations between all pairs of features in a matrix/dataframe much faster than the base R cor function. It can also calculate correlations between all pairs of features from two input matrices/dataframes when 'data2' is provided. It is also possible to simultaneously calculate mutual rank (MR) of correlations as well as their p-values and adjusted p-values. Additionally, this function can automatically combine and flatten the result matrices. Selecting correlated features using an MR-based threshold rather than based on their correlation coefficients or an arbitrary p-value is more efficient and accurate in inferring functional associations in systems, for example in gene regulatory networks.

Usage

fcor(
  data,
  data2 = NULL,
  na_to_zero = TRUE,
  method = "spearman",
  mutualRank = TRUE,
  mutualRank_mode = "unsigned",
  pvalue = FALSE,
  adjust = "BH",
  flat = TRUE,
  remove_self = TRUE,
  remove_duplicate_pairs = TRUE
)

Arguments

data	a numeric dataframe/matrix with features on columns and samples/observations on rows. If 'data2' is not provided, correlations are calculated between all pairs of features in 'data'.
data2	an optional numeric dataframe/matrix with features on columns and samples/observations on rows. If provided, correlations are calculated between all features in 'data' and all features in 'data2'. 'data' and 'data2' must have the same number of rows, and the rows must correspond to the same samples/observations in the same order. Default is 'NULL'.
na_to_zero	logical, whether to convert NAs to 0 in the output (default) or not.
method	a character string indicating which correlation coefficient is to be computed. One of '"pearson"' or '"spearman"' (default).
mutualRank	logical, whether to calculate mutual ranks of correlations or not.
mutualRank_mode	a character string indicating whether to rank based on '"signed"' or '"unsigned"' (default) correlation values. In the '"unsigned"' mode, only the level of a correlation value is important and not its sign; therefore, the function ranks the absolute values of correlations. Options are '"unsigned"' and '"signed"'.
pvalue	logical, whether to calculate p-values of correlations or not.
adjust	p-value correction method when 'pvalue = TRUE', a character string including any of '"BH"' (default), '"bonferroni"', '"holm"', '"hochberg"', '"hommel"', or '"none"'.
flat	logical, whether to combine and flatten the result matrices or not.
remove_self	logical, whether to remove self-correlations from the flattened output when 'data2' is provided. This is useful when 'data2' contains some or all of the same features as 'data'. Default is 'TRUE'.
remove_duplicate_pairs	logical, whether to remove duplicate undirected feature pairs from the flattened output when 'data2' is provided. This is useful when 'data2' contains the same features as 'data', because pairs such as 'geneA-geneB' and 'geneB-geneA' may otherwise both be returned. Default is 'TRUE'.

Details

When 'data2 = NULL', the function performs the standard all-pairs correlation analysis among the features of 'data'. When 'data2' is provided, the function performs a rectangular correlation analysis between the features of 'data' and the features of 'data2'.

For Spearman correlation with 'data2', the two input matrices are internally combined before rank transformation so that feature-wise ranks are calculated consistently across the same samples/observations.

When 'mutualRank = TRUE' and 'data2' is provided, the calculated MR values are based on the rectangular correlation space between 'data' and 'data2'. Therefore, these MR values are not necessarily identical to MR values obtained from a full all-pairs correlation matrix followed by post hoc filtering.

Value

Depending on the input data and the value of 'flat', a dataframe or list including 'cor' correlation coefficients, 'mr' mutual ranks of correlation coefficients, 'p' p-values of correlation coefficients, and 'p.adj' adjusted p-values. If 'data2' is not provided and 'flat = TRUE', the flattened output contains the upper triangle of the all-pairs correlation matrix. If 'data2' is provided and 'flat = TRUE', the flattened output contains feature pairs between 'data' and 'data2'.

See Also

p.adjust and graph_from_data_frame

Examples

## Not run: 
set.seed(1234)

# All-pairs correlation among features
data <- datasets::attitude
cor <- fcor(data = data)

# Correlation between two sets of features
data1 <- mtcars[, 1:4]
data2 <- mtcars[, 5:11]
cor_rect <- fcor(data = data1, data2 = data2)

# Correlation between selected features and all features
selected_data <- mtcars[, 1:4]
all_data <- mtcars
cor_selected_all <- fcor(data = selected_data, data2 = all_data)

## End(Not run)

influential documentation built on May 28, 2026, 5:07 p.m.