getExperimentCrossCorrelation {mia} | R Documentation |
Calculate cross-correlation
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, ...)
x |
A
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... |
Additional arguments:
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experiment1 |
A single character or numeric value for selecting the experiment 1
from |
experiment2 |
A single character or numeric value for selecting the experiment 2
from |
abund_values1 |
A single character value for selecting the
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abund_values2 |
A single character value for selecting the
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method |
A single character value for selecting association method
('kendall', pearson', or 'spearman' for continuous/numeric; 'categorical' for discrete)
(By default: |
mode |
A single character value for selecting output format
Available formats are 'table' and 'matrix'. (By default: |
p_adj_method |
A single character value for selecting adjustment method of
p-values. Passed to |
p_adj_threshold |
A single numeric value (from 0 to 1) for selecting
adjusted p-value threshold. (By default: |
cor_threshold |
A single numeric absolute value (from 0 to 1]) for selecting
correlation threshold to include features. (By default: |
sort |
A single boolean value for selecting whether to sort features or not
in result matrices. Used method is hierarchical clustering.
Disabled when |
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: |
verbose |
A single boolean value for selecting whether to get messages about progress of calculation. |
These functions calculates associations between features of two experiments.
getExperimentCrossCorrelation
calculates only associations by default.
testForExperimentCrossCorrelation
calculates also significance of
associations.
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.
Leo Lahti and Tuomas Borman. Contact: microbiome.github.io
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, y = "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, y = 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]], y = mae[[2]], method = "pearson", mode = "matrix", test_significance = TRUE) # Returned value is a list of matrices names(result)