Package 'OmniCorr'

Calculate correlation between two data frames

Description

This function calculates the correlation matrix between the columns of two data frames.

Usage

calculate_correlations(
  df1,
  df2,
  method = "pearson",
  adjust_method = "fdr",
  use = "all.obs",
  show_significance = "stars"
)

Arguments

df1	A data frame or matrix containing the first set of variables to be correlated. Rows are samples, columns are variables (e.g., genes, proteins, metabolites).
df2	A data frame or matrix containing the second set of variables to be correlated. Rows are samples, columns are variables.
method	The correlation method to be used. Default is "pearson". Other options include "spearman" and "kendall".
adjust_method	Method for adjusting the p-values for multiple testing. Default is "fdr". If "none", no adjustment is applied. Supports all methods from p.adjust.methods.
use	Handling of missing data. One of "all.obs" or "pairwise.complete.obs".
show_significance	How to represent significance in the output. One of "stars", "p_value", or "correlation".

Details

Pearson and Spearman correlations are computed using WGCNA::corAndPvalue for efficiency. Kendall correlation uses cor.test in a loop and may be slow for large datasets.

Value

A list containing:

correlation	Matrix of correlation coefficients (rounded to 3 decimals).
p_value	Matrix of raw p-values.
p_value_adj	Matrix of adjusted p-values.
signif_matrix	Matrix of significance stars, p-values, or correlations.

Examples

data(Transcriptomics)
data(Metagenomics)

result_list <- calculate_correlations(
  df1 = Transcriptomics,
  df2 = Metagenomics,
  show_significance = "p_value"
)

---

Check and align sample order between two datasets

Description

Ensures that two data frames or matrices contain identical samples and reorders the second dataset to match the first.

Usage

CheckSampleOrder(df1, df2)

Arguments

df1	A data frame or matrix with samples in rows (reference order)
df2	A data frame or matrix with samples in rows

Value

A list with df1 and df2 reordered to have identical rownames and order

Examples

df1 <- matrix(1:6, nrow = 3,
              dimnames = list(c("S1", "S2", "S3"), c("A", "B")))
df2 <- matrix(7:12, nrow = 3,
              dimnames = list(c("S3", "S1", "S2"), c("C", "D")))
result <- CheckSampleOrder(df1, df2)
result$df2

---

Metadata information

Description

This data frame contains information about the samples used in the study.

Usage

metadata

Format

A data frame with 14 variables:

BW

Body weight of the sample in grams.

Length

Length of the sample in centimeters.

Sex

Sex of the sample.

Gut_struc

Structural condition of the gut of the sample.

Gut_light

Light coloration of the gut of the sample.

Anus_bleed

Presence or absence of anus bleeding in the sample.

Gut_bleed

Presence or absence of gut bleeding in the sample.

Fat_Score

Score of fat deposition in the sample.

Heart_Sco

Score of heart condition in the sample.

softliver

Presence or absence of soft liver in the sample.

patchy_liver

Presence or absence of patchy liver in the sample.

Liver_Sco

Score of liver condition in the sample.

HSI

Hepatosomatic index of the sample.

CSI

Cardiosomatic index of the sample.

Value

A data frame containing sample metadata.

Source

Information was collected from the study samples.

Examples

# View the first few rows of the metadata
head(metadata)

---

Example Metagenomics dataset

Description

A processed metagenomics feature-abundance matrix used as an example input for OmniCorr.

Usage

data(Metagenomics)

Format

A numeric matrix with samples as rows and features as columns.

Source

Derived from published cow rumen multi-omics analysis.

---

Example Metatranscriptomics dataset

Description

A processed metatranscriptomics feature-abundance matrix used as an example input for OmniCorr.

Usage

data(Metatranscriptomics)

Format

A numeric matrix with samples as rows and features as columns.

Source

Derived from published cow rumen multi-omics analysis.

---

Run OmniCorr Multi-Omics Integration

Description

Integrates multiple omics layers by computing correlations between a reference omics dataset and multiple comparison layers and visualizing the results as aligned multi-panel heatmaps using ComplexHeatmap.

The reference layer is displayed as an expression heatmap with optional clustering of features and samples. Correlation heatmaps for comparison layers and metadata are aligned to the same reference feature order to ensure consistent interpretation across panels.

Usage

run_omnicorr(
  reference_layer,
  comparison_layers,
  metadata = NULL,
  reference_name = "Reference Layer",
  comparison_names = NULL,
  metadata_name = "Metadata",
  method = "pearson",
  adjust_method = "fdr",
  use = "all.obs",
  show_significance = "stars",
  cluster_reference_rows = TRUE,
  cluster_reference_columns = TRUE,
  show_row_names = TRUE,
  show_column_names = TRUE,
  font_size = 8,
  star_font_size = 8,
  verbose = TRUE
)

Arguments

reference_layer	Numeric matrix/data.frame (samples x features).
comparison_layers	Named list of numeric matrices/data.frames.
metadata	Optional numeric matrix/data.frame (samples x variables).
reference_name	Title for the reference heatmap.
comparison_names	Optional titles for comparison heatmaps.
metadata_name	Title for metadata heatmap.
method	Correlation method: "pearson", "spearman", "kendall".
adjust_method	P-value correction method (stats::p.adjust.methods).
use	Missing value handling strategy ("all.obs" or "pairwise.complete.obs").
show_significance	"stars", "p_value", or "correlation".
cluster_reference_rows	Cluster reference features.
cluster_reference_columns	Cluster reference samples.
show_row_names	Logical; display row labels.
show_column_names	Logical; display column labels.
font_size	Font size for row/column labels.
star_font_size	Font size for cell annotations.
verbose	Print progress messages.

Value

Invisibly returns a ComplexHeatmap::HeatmapList.

Examples

reference <- matrix(
  rnorm(20),
  nrow = 5,
  dimnames = list(paste0("S", 1:5), paste0("Gene", 1:4))
)
comparison <- matrix(
  rnorm(15),
  nrow = 5,
  dimnames = list(paste0("S", 1:5), paste0("Taxon", 1:3))
)
result <- run_omnicorr(
  reference_layer = reference,
  comparison_layers = list(Microbiome = comparison)
)

---

Example Transcriptomics dataset

Description

A processed transcriptomics expression matrix used as an example input for OmniCorr. Rows correspond to samples and columns to transcript-level features.

Usage

data(Transcriptomics)

Format

A numeric matrix with samples as rows and features as columns.

Source

Derived from published cow rumen multi-omics analysis.

---