| Title: | Learning Sparse Log-Ratios for Compositional Data |
|---|---|
| Description: | In the context of high-throughput genetic data, CoDaCoRe identifies a set of sparse biomarkers that are predictive of a response variable of interest (Gordon-Rodriguez et al., 2021) <doi:10.1093/bioinformatics/btab645>. More generally, CoDaCoRe can be applied to any regression problem where the independent variable is Compositional (CoDa), to derive a set of scale-invariant log-ratios (ILR or SLR) that are maximally associated to a dependent variable. |
| Authors: | Elliott Gordon-Rodriguez [aut, cre], Thomas Quinn [aut] |
| Maintainer: | Elliott Gordon-Rodriguez <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.4 |
| Built: | 2025-03-30 03:10:06 UTC |
| Source: | https://github.com/cran/codacore |
activeInputs
activeInputs.codacore(cdcr)activeInputs.codacore(cdcr)
cdcr |
A codacore object. |
The covariates included in the log-ratios
This function implements the codacore algorithm described by Gordon-Rodriguez et al. 2021 (https://doi.org/10.1101/2021.02.11.430695).
codacore( x, y, logRatioType = "balances", objective = NULL, lambda = 1, offset = NULL, shrinkage = 1, maxBaseLearners = 5, optParams = list(), cvParams = list(), verbose = FALSE, overlap = TRUE, fast = TRUE )codacore( x, y, logRatioType = "balances", objective = NULL, lambda = 1, offset = NULL, shrinkage = 1, maxBaseLearners = 5, optParams = list(), cvParams = list(), verbose = FALSE, overlap = TRUE, fast = TRUE )
x |
A data.frame or matrix of the compositional predictor variables. |
y |
A data.frame, matrix or vector of the response. |
logRatioType |
A string indicating whether to use "balances" or "amalgamations". Also accepts "balance", "B", "ILR", or "amalgam", "A", "SLR". Note that the current implementation for balances is not strictly an ILR, but rather just a collection of balances (which are possibly non-orthogonal in the Aitchison sense). |
objective |
A string indicating "binary classification" or "regression". By default, it is NULL and gets inferred from the values in y. |
lambda |
A numeric. Corresponds to the "lambda-SE" rule. Sets the "regularization strength" used by the algorithm to decide how to harden the ratio. Larger numbers tend to yield fewer, more sparse ratios. |
offset |
A numeric vector of the same length as y. Works similarly to the offset in a glm. |
shrinkage |
A numeric. Shrinkage factor applied to each base learner. Defaults to 1.0, i.e., no shrinkage applied. |
maxBaseLearners |
An integer. The maximum number of log-ratios that the model will
learn before stopping. Automatic stopping based on |
optParams |
A list of named parameters for the optimization of the continuous relaxation. Empty by default. User can override as few or as many of our defaults as desired. Includes adaptiveLR (learning rate under adaptive training scheme), momentum (in the gradient-descent sense), epochs (number of gradient-descent epochs), batchSize (number of observations per minibatch, by default the entire dataset), and vanillaLR (the learning rate to be used if the user does *not* want to use the 'adaptiveLR', to be used at the risk of optimization issues). |
cvParams |
A list of named parameters for the "hardening" procedure using cross-validation. Includes numFolds (number of folds, default=5) and maxCutoffs (number of candidate cutoff values of 'c' to be tested out during CV process, default=20 meaning log-ratios with up to 21 components can be found by codacore). |
verbose |
A boolean. Toggles whether to display intermediate steps. |
overlap |
A boolean. Toggles whether successive log-ratios found by CoDaCoRe may contain repeated input variables. TRUE by default. Changing to FALSE implies that the log-ratios obtained by CoDaCoRe will become orthogonal in the Aitchison sense, analogously to the isometric-log-ratio transformation, while losing a small amount of model flexibility. |
fast |
A boolean. Whether to run in fast or slow mode. TRUE by default. Running in slow mode will take ~x5 the computation time, but may help identify slightly more accurate log-ratios. |
A codacore object.
## Not run: data("Crohn") x <- Crohn[, -ncol(Crohn)] y <- Crohn[, ncol(Crohn)] x <- x + 1 model = codacore(x, y) print(model) plot(model) ## End(Not run)## Not run: data("Crohn") x <- Crohn[, -ncol(Crohn)] y <- Crohn[, ncol(Crohn)] x <- x + 1 model = codacore(x, y) print(model) plot(model) ## End(Not run)
A dataset containing the number of counts of 48 different genera in a group
of 975 samples (including 662 samples of patients with Crohn's disease and
313 controls).
The data.frame is composed by 48 genera and a factor variable
The data.frame is composed by 48 genera and a variable
The first 48 columns, from g_Turicibacter until g_Bilophila referred to different genera.
a factor indicating if the sample corresponds to a case (
CD) or a control (no).
A dataset containing the relative abundances of 7156 metabolites in a group of of 220 samples, together with an additional response variable indicating the corresponding Diagnosis.
The data.frame is composed by metabolite data and Diagnosis
TBD
Indicates if the sample was diagnosed with Crohn's disease (CD), ulcerative colitis (UC), or was a control (Control).
https://www.nature.com/articles/s41564-018-0306-4
A dataset containing the relative abundances of 58 bacteria in a group of of 220 samples, together with an additional response variable indicating the corresponding Diagnosis.
The data.frame is composed by microbiome data and Diagnosis
The first 58 columns.
Indicates if the sample was diagnosed with Crohn's disease (CD), ulcerative colitis (UC), or was a control (Control).
https://www.nature.com/articles/s41564-018-0306-4
getBinaryPartitions
getBinaryPartitions(cdcr)getBinaryPartitions(cdcr)
cdcr |
A codacore object |
A matrix describing whether each component (as rows) is found in the numerator (1) or denominator (-1) of each learned log-ratio (as columns). This format resembles a serial binary partition matrix frequently used in balance analysis.
getDenominatorParts
getDenominatorParts(cdcr, baseLearnerIndex = 1, boolean = TRUE)getDenominatorParts(cdcr, baseLearnerIndex = 1, boolean = TRUE)
cdcr |
A codacore object. |
baseLearnerIndex |
An integer indicating which of the (possibly multiple) log-ratios learned by codacore to be used. |
boolean |
Whether to return the parts in boolean form (a vector of TRUE/FALSE) or to return the column names of those parts directly. |
The covariates in the denominator of the selected log-ratio.
getLogRatios
getLogRatios(cdcr, x = NULL)getLogRatios(cdcr, x = NULL)
cdcr |
A codacore object |
x |
A set of (possibly unseen) compositional data. The covariates must be passed in the same order as for the original codacore() call. |
The learned log-ratio features, computed on input x.
getNumeratorParts
getNumeratorParts(cdcr, baseLearnerIndex = 1, boolean = TRUE)getNumeratorParts(cdcr, baseLearnerIndex = 1, boolean = TRUE)
cdcr |
A codacore object. |
baseLearnerIndex |
An integer indicating which of the (possibly multiple) log-ratios learned by codacore to be used. |
boolean |
Whether to return the parts in boolean form (a vector of TRUE/FALSE) or to return the column names of those parts directly. |
The covariates in the numerator of the selected log-ratio.
getNumLogRatios
getNumLogRatios(cdcr)getNumLogRatios(cdcr)
cdcr |
A codacore object |
The number of log-ratios that codacore found. Typically a small integer. Can be zero if codacore found no predictive log-ratios in the data.
getSlopes
getSlopes(cdcr)getSlopes(cdcr)
cdcr |
A codacore object |
The slopes (i.e., regression coefficients) for each log-ratio.
getTidyTable
getTidyTable(cdcr)getTidyTable(cdcr)
cdcr |
A codacore object |
A table displaying the log-ratios found.
A dataset containing the number of counts of 60 different genera in a group
of 155 samples (including HIV - infected and non - infected patients).
The data.frame is composed by 60 genera and two variables.
The data.frame is composed by 60 genera and 2 variables
The first 60 columns, from g_Prevotella until o_NB1-n_g_unclassified referred to different genera.
a factor determining if the individual is MSM (Men Sex with
Men) or not (nonMSM).
a factor specifying if the individual is infected
(Pos) or not (Neg).
https://pubmed.ncbi.nlm.nih.gov/27077120/
Plots a summary of a fitted codacore model. Credit to the authors of the selbal package (Rivera-Pinto et al., 2018), from whose package these plots were inspired.
## S3 method for class 'codacore' plot(x, index = 1, ...)## S3 method for class 'codacore' plot(x, index = 1, ...)
x |
A codacore object. |
index |
The index of the log-ratio to plot. |
... |
Not used. |
predict
## S3 method for class 'codacore' predict(object, newx, asLogits = TRUE, numLogRatios = NA, ...)## S3 method for class 'codacore' predict(object, newx, asLogits = TRUE, numLogRatios = NA, ...)
object |
A codacore object. |
newx |
A set of inputs to our model. |
asLogits |
Whether to return outputs in logit space (as opposed to probability space). Should always be set to TRUE for regression with continuous outputs, but can be toggled for classification problems. |
numLogRatios |
How many predictive log-ratios to include in the prediction. By default, includes the effects of all log-ratios that were obtained during training. Setting this parameter to an integer k will restrict to using only the top k log-ratios in the model. |
... |
Not used. |
## S3 method for class 'codacore' print(x, ...)## S3 method for class 'codacore' print(x, ...)
x |
A codacore object. |
... |
Not used. |
A dataset containing the number of counts of 60 different genera in a group
of 151 samples (including HIV - infected and non - infected patients).
The data.frame is composed by 60 genera and a numeric variable
The data.frame is composed by 60 genera and a variable
The first 60 columns, from g_Prevotella until o_NB1-n_g_unclassified referred to different genera.
a numeric variable with the value of the inflammation
parameter sCD14 for each sample.
doi:10.1016/j.ebiom.2016.01.032
This function simulates a set of (x, y) pairs. The covariates x are compositional, meaning they only carry relative information. The response y is a binary indicator. The rule linking x and y can be a balance or an amalgamation.
simulateHTS(n, p, outputType = "binary", logratio = "simple")simulateHTS(n, p, outputType = "binary", logratio = "simple")
n |
Number of observations. |
p |
Number of covariates. |
outputType |
A string indicating 'binary' or 'continuous'. |
logratio |
A string indicating 'simple', 'balance', or 'amalgamation'. |
A list containing a matrix of inputs and a vector of outputs