mb.splsdacox {Coxmos}R Documentation

MB.sPLS-DACOX

Description

The MB.sPLS-DACOX function conducts a multi-block sparse partial least squares discriminant analysis Cox (MB.sPLS-DACOX) using a dynamic variable selection approach. This analysis is particularly suited for high-dimensional datasets where the goal is to identify the relationship between explanatory variables and survival outcomes. The function outputs a model of class "Coxmos" with an attribute labeled "MB.sPLS-DACOX".

Usage

mb.splsdacox(
  X,
  Y,
  n.comp = 4,
  vector = NULL,
  MIN_NVAR = 10,
  MAX_NVAR = 10000,
  n.cut_points = 5,
  EVAL_METHOD = "AUC",
  x.center = TRUE,
  x.scale = FALSE,
  remove_near_zero_variance = TRUE,
  remove_zero_variance = TRUE,
  toKeep.zv = NULL,
  remove_non_significant = TRUE,
  alpha = 0.05,
  MIN_AUC_INCREASE = 0.01,
  pred.method = "cenROC",
  max.iter = 200,
  times = NULL,
  max_time_points = 15,
  MIN_EPV = 5,
  returnData = TRUE,
  verbose = FALSE
)

Arguments

X

Numeric matrix or data.frame. Explanatory variables. Qualitative variables must be transform into binary variables.

Y

Numeric matrix or data.frame. Response variables. Object must have two columns named as "time" and "event". For event column, accepted values are: 0/1 or FALSE/TRUE for censored and event observations.

n.comp

Numeric. Number of latent components to compute for the (s)PLS model (default: 10).

vector

Numeric vector or list. Used for computing best number of variables. As many values as components have to be provided. If vector = NULL, an automatic detection is perform (default: NULL). If vector is a list, must be named as the names of X param followed by the number of variables to select.

MIN_NVAR

Numeric. Minimum range size for computing cut points to select the best number of variables to use (default: 10).

MAX_NVAR

Numeric. Maximum range size for computing cut points to select the best number of variables to use (default: 1000).

n.cut_points

Numeric. Number of cut points for searching the optimal number of variables. If only two cut points are selected, minimum and maximum size are used. For MB approaches as many as n.cut_points^n.blocks models will be computed as minimum (default: 5).

EVAL_METHOD

Character. If EVAL_METHOD = "AUC", AUC metric will be use to compute the best number of variables. In other case, c-index metric will be used (default: "AUC").

x.center

Logical. If x.center = TRUE, X matrix is centered to zero means (default: TRUE).

x.scale

Logical. If x.scale = TRUE, X matrix is scaled to unit variances (default: FALSE).

remove_near_zero_variance

Logical. If remove_near_zero_variance = TRUE, near zero variance variables will be removed (default: TRUE).

remove_zero_variance

Logical. If remove_zero_variance = TRUE, zero variance variables will be removed (default: TRUE).

toKeep.zv

Character vector. Name of variables in X to not be deleted by (near) zero variance filtering (default: NULL).

remove_non_significant

Logical. If remove_non_significant = TRUE, non-significant variables/components in final cox model will be removed until all variables are significant by forward selection (default: FALSE).

alpha

Numeric. Numerical values are regarded as significant if they fall below the threshold (default: 0.05).

MIN_AUC_INCREASE

Numeric. Minimum improvement between different cross validation models to continue evaluating higher values in the multiple tested parameters. If it is not reached for next 'MIN_COMP_TO_CHECK' models and the minimum 'MIN_AUC' value is reached, the evaluation stops (default: 0.01).

pred.method

Character. AUC evaluation algorithm method for evaluate the model performance. Must be one of the following: "risksetROC", "survivalROC", "cenROC", "nsROC", "smoothROCtime_C", "smoothROCtime_I" (default: "cenROC").

max.iter

Numeric. Maximum number of iterations for PLS convergence (default: 200).

times

Numeric vector. Time points where the AUC will be evaluated. If NULL, a maximum of 'max_time_points' points will be selected equally distributed (default: NULL).

max_time_points

Numeric. Maximum number of time points to use for evaluating the model (default: 15).

MIN_EPV

Numeric. Minimum number of Events Per Variable (EPV) you want reach for the final cox model. Used to restrict the number of variables/components can be computed in final cox models. If the minimum is not meet, the model cannot be computed (default: 5).

returnData

Logical. Return original and normalized X and Y matrices (default: TRUE).

verbose

Logical. If verbose = TRUE, extra messages could be displayed (default: FALSE).

Details

The MB.sPLS-DACOX methodology is designed to handle multi-block datasets, where each block represents a set of related variables. By employing a sparse partial least squares approach, the function efficiently selects relevant variables from each block, ensuring that the final model is both interpretable and predictive. The Cox proportional hazards model is then applied to the selected variables to assess their association with survival outcomes.

The function offers flexibility in terms of parameter tuning. For instance, users can specify the number of latent components to compute, the range of variables to consider for optimal selection, and the evaluation metric (either AUC or c-index). Additionally, data preprocessing options are available, such as centering and scaling of the explanatory variables, and removal of variables with near-zero or zero variance.

Value

Instance of class "Coxmos" and model "MB.sPLS-DACOX". The class contains the following elements: X: List of normalized X data information.

Y: List of normalized Y data information.

survival_model: List of survival model information.

mb.model: List of sPLS models computed for each block.

n.comp: Number of components selected.

n.varX: Number of variables selected for each block.

call: call function

X_input: X input matrix

Y_input: Y input matrix

B.hat: PLS beta matrix

R2: PLS R2

SCR: PLS SCR

SCT: PLS SCT

nzv: Variables removed by remove_near_zero_variance or remove_zero_variance.

nz_coeffvar: Variables removed by coefficient variation near zero.

time: time consumed for running the cox analysis.

nzv: Variables removed by remove_near_zero_variance or remove_zero_variance.

nz_coeffvar: Variables removed by coefficient variation near zero.

time: time consumed for running the cox analysis.

Author(s)

Pedro Salguero Garcia. Maintainer: pedsalga@upv.edu.es

References

Rohart F, Gautier B, Singh A, Cao KAL (2017). “mixOmics: An R package for ‘omics feature selection and multiple data integration.” PLoS Computational Biology, 13(11). ISSN 15537358, https://pubmed.ncbi.nlm.nih.gov/29099853/.

Examples


data("X_multiomic")
data("Y_multiomic")
X <- X_multiomic
X$mirna <- X$mirna[,1:50]
X$proteomic <- X$proteomic[,1:50]
Y <- Y_multiomic
mb.splsdacox(X, Y, n.comp = 2, vector = NULL, x.center = TRUE, x.scale = TRUE)


[Package Coxmos version 1.0.2 Index]