getExperimentCrossCorrelation {mia} | R Documentation

## Calculate cross-correlation

### Description

Calculate cross-correlation

### Usage

    getExperimentCrossCorrelation(x, ...)

    ## S4 method for signature 'MultiAssayExperiment'
    getExperimentCrossCorrelation(
      x,
      experiment1 = 1,
      experiment2 = 2,
      abund_values1 = "counts",
      abund_values2 = "counts",
      method = c("spearman", "categorical", "kendall", "pearson"),
      mode = "table",
      p_adj_method = c("fdr", "BH", "bonferroni", "BY", "hochberg", "holm", "hommel",
        "none"),
      p_adj_threshold = 0.05,
      cor_threshold = NULL,
      sort = FALSE,
      filter_self_correlations = FALSE,
      verbose = TRUE,
      ...
    )

    ## S4 method for signature 'SummarizedExperiment'
    getExperimentCrossCorrelation(x, experiment2 = x, ...)

    testForExperimentCrossCorrelation(x, ...)

    ## S4 method for signature 'ANY'
    testForExperimentCrossCorrelation(x, ...)

### Arguments

`x` | A `MultiAssayExperiment` or `SummarizedExperiment` object.
`...` | Additional arguments: `test_significance`A single boolean value in function `getExperimentCrossCorrelation` for selecting whether to test significance or not. (By default: `test_significance = FALSE`)
`experiment1` | A single character or numeric value for selecting the experiment 1 from `experiments(x)` of `MultiassayExperiment` object. (By default: `experiment1 = 1`)
`experiment2` | A single character or numeric value for selecting the experiment 2 from`experiments(x)` of `MultiAssayExperiment` object or `altExp(x)` of `SummarizedExperiment` object. Alternatively, `experiment2` can also be `SE` object when `x` is `SE` object. (By default: `experiment2 = 2` when `x` is `MAE` and `experiment2 = x` when `x` is `SE`)
`abund_values1` | A single character value for selecting the `assay` of experiment 1 to be transformed. (By default: `abund_values1 = "counts"`)
`abund_values2` | A single character value for selecting the `assay` of experiment 2 to be transformed. (By default: `abund_values2 = "counts"`)
`method` | A single character value for selecting association method ('kendall', pearson', or 'spearman' for continuous/numeric; 'categorical' for discrete) (By default: `method = "spearman"`)
`mode` | A single character value for selecting output format Available formats are 'table' and 'matrix'. (By default: `mode = "table"`)
`p_adj_method` | A single character value for selecting adjustment method of p-values. Passed to `p.adjust` function. (By default: `p_adj_method = "fdr"`)
`p_adj_threshold` | A single numeric value (from 0 to 1) for selecting adjusted p-value threshold. (By default: `p_adj_threshold = 0.05`)
`cor_threshold` | A single numeric absolute value (from 0 to 1]) for selecting correlation threshold to include features. (By default: `cor_threshold = NULL`)
`sort` | A single boolean value for selecting whether to sort features or not in result matrices. Used method is hierarchical clustering. Disabled when `mode = "table"`. (By default: `sort = FALSE`)
`filter_self_correlations` | A single boolean value for selecting whether to filter out correlations between identical items. Applies only when correlation between experiment itself is tested, i.e., when assays are identical. (By default: `filter_self_correlations = FALSE`)
`verbose` | A single boolean value for selecting whether to get messages about progress of calculation.

### Details

These functions calculates associations between features of two experiments. `getExperimentCrossCorrelation` calculates only associations by default. `testForExperimentCrossCorrelation` calculates also significance of associations.

### Value

These functions return associations in table or matrix format. In table format, returned value is a data frame that includes features and associations (and p-values) in columns. In matrix format, returned value is a one matrix when only associations are calculated. If also significances are tested, then returned value is a list of matrices.

### Author(s)

Leo Lahti and Tuomas Borman. Contact: microbiome.github.io

### Examples

    mae <- microbiomeDataSets::peerj32()

    # Subset so that less observations / quicker to run, just for example
    mae[[1]] <- mae[[1]][1:20, 1:10]
    mae[[2]] <- mae[[2]][1:20, 1:10]
    # Calculate cross-correlations
    result <- getExperimentCrossCorrelation(mae, method = "pearson")
    # Show first 5 entries
    head(result, 5)

    # Same can be done with SummarizedExperiment and altExp
    # Create TreeSE with altExp
    tse <- mae[[1]]
    altExp(tse, "exp2") <- mae[[2]]
    # Whe mode = matrix, matrix is returned
    result <- getExperimentCrossCorrelation(tse,
                                            experiment2 = "exp2",
                                            method = "pearson",
                                            mode = "matrix")
    # Show first 5 entries
    head(result, 5)

    # testForExperimentCorrelation returns also significances
    # filter_self_correlations = TRUE filters self correlations
    result <- testForExperimentCrossCorrelation(tse,
                                                experiment2 = tse,
                                                method = "pearson",
                                                filter_self_correlations = TRUE)
    # Show first 5 entries
    head(result, 5)

    # Also getExperimentCrossCorrelation returns significances when
    # test_signicance = TRUE
    result <- getExperimentCrossCorrelation(mae[[1]],
                                            experiment2 = mae[[2]],
                                            method = "pearson",
                                            mode = "matrix",
                                            test_significance = TRUE)
    # Returned value is a list of matrices
    names(result)

[Package mia version 1.2.7 Index]