Estimates 3-dimensional signals (features by observations by sources) from input of mixtures (features by observations), under the assumption of the TCA model that each observation is a mixture of unique source-specific values (in each feature in the data). For example, in the context of tissue-level bulk DNA methylation data coming from a mixture of cell types (i.e. the input is methylation sites by individuals), tensor allows to estimate a tensor of cell-type-specific levels for each individual in each methylation site (i.e. a tensor of methylation sites by individuals by cell types).
tensor(X, tca.mdl, parallel = FALSE, num_cores = NULL,
log_file = "TCA.log", debug = FALSE)An m by n matrix of measurements of m features for n observations. Each column in X is assumed to be a mixture of k different sources. Note that X must include row names and column names and that NA values are currently not supported.
The value returned by applying the function tca to X.
A logical value indicating whether to use parallel computing (possible when using a multi-core machine).
A numeric value indicating the number of cores to use (activated only if parallel == TRUE). If num_cores == NULL then all available cores except for one will be used.
A path to an output log file. Note that if the file log_file already exists then logs will be appended to the end of the file. Set log_file to NULL to prevent output from being saved into a file.
A logical value indicating whether to set the logger to a more detailed debug level; please set debug to TRUE before reporting issues.
A list with the estimated source-specific values. The first element in the list is an m by n matrix (features by observations) corresponding to the estimated values coming from the first source, the second element in the list is another m by n matrix (features by observations) corresponding to the estimated values coming from the second source and so on.
See tca for notations and details about the TCA model. Given estimates of the parameters of the model (given by tca), tensor uses the conditional distribution \(Z_{hj}^i|X_{ji}\) for estimating the \(k\) source-specific levels of each observation \(i\) in each feature \(j\).
Rahmani E, Schweiger R, Rhead B, Criswell LA, Barcellos LF, Eskin E, Rosset S, Sankararaman S, Halperin E. Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology. Nature Communications 2018.
# NOT RUN {
data <- test_data(50, 20, 3, 2, 2, 0.01)
tca.mdl <- tca(data$X, data$W, data$C1, data$C2)
Z_hat <- tensor(data$X, tca.mdl)
# }
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