secom_dist | R Documentation |
Obtain the sparse correlation matrix for distance correlations between taxa.
secom_dist( data, assay_name = "counts", tax_level = NULL, pseudo = 0, prv_cut = 0.5, lib_cut = 1000, corr_cut = 0.5, wins_quant = c(0.05, 0.95), R = 1000, thresh_hard = 0, max_p = 0.005, n_cl = 1 )
data |
a list of the input data. Each element of the list can be a
|
assay_name |
character. Name of the count table in the data object
(only applicable if data object is a |
tax_level |
character. The taxonomic level of interest. The input data
can be agglomerated at different taxonomic levels based on your research
interest. Default is NULL, i.e., do not perform agglomeration, and the
SECOM anlysis will be performed at the lowest taxonomic level of the
input |
pseudo |
numeric. Add pseudo-counts to the data. Default is 0 (no pseudo-counts). |
prv_cut |
a numerical fraction between 0 and 1. Taxa with prevalences
less than |
lib_cut |
a numerical threshold for filtering samples based on library
sizes. Samples with library sizes less than |
corr_cut |
numeric. To prevent false positives due to taxa with
small variances, taxa with Pearson correlation coefficients greater than
|
wins_quant |
a numeric vector of probabilities with values between
0 and 1. Replace extreme values in the abundance data with less
extreme values. Default is |
R |
numeric. The number of replicates in calculating the p-value for
distance correlation. For details, see |
thresh_hard |
Numeric. Set a hard threshold for the correlation matrix.
Pairwise distance correlation less than or equal to |
max_p |
numeric. Obtain the sparse correlation matrix by
p-value filtering. Pairwise correlation coefficient with p-value greater than
|
n_cl |
numeric. The number of nodes to be forked. For details, see
|
The distance correlation, which is a measure of dependence between two random variables, can be used to quantify any dependence, whether linear, monotonic, non-monotonic or nonlinear relationships.
a list
with components:
s_diff_hat
, a numeric vector of estimated
sample-specific biases.
y_hat
, a matrix of bias-corrected abundances
mat_cooccur
, a matrix of taxon-taxon co-occurrence
pattern. The number in each cell represents the number of complete
(nonzero) samples for the corresponding pair of taxa.
dcorr
, the sample distance correlation matrix
computed using the bias-corrected abundances y_hat
.
dcorr_p
, the p-value matrix corresponding to the sample
distance correlation matrix dcorr
.
dcorr_fl
, the sparse correlation matrix obtained by
p-value filtering based on the cutoff specified in max_p
.
Huang Lin
secom_linear
library(ANCOMBC) data(dietswap) # subset to baseline tse = dietswap[, dietswap$timepoint == 1] set.seed(123) res_dist = secom_dist(data = list(tse), assay_name = "counts", tax_level = "Phylum", pseudo = 0, prv_cut = 0.5, lib_cut = 1000, corr_cut = 0.5, wins_quant = c(0.05, 0.95), R = 1000, thresh_hard = 0.3, max_p = 0.005, n_cl = 2) dcorr_fl = res_dist$dcorr_fl
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