sb.splsicox {Coxmos}

SB.sPLS-ICOX

Description

This function performs a single-block sparse partial least squares individual Cox (SB.sPLS-ICOX). The function returns a Coxmos model with the attribute model as "SB.sPLS-ICOX".

Usage

sb.splsicox(
  X,
  Y,
  n.comp = 4,
  penalty = 1,
  x.center = TRUE,
  x.scale = FALSE,
  remove_near_zero_variance = TRUE,
  remove_zero_variance = TRUE,
  toKeep.zv = NULL,
  remove_non_significant = FALSE,
  alpha = 0.05,
  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).

penalty

Numeric. Penalty for variable selection for the individual cox models. Variables with a lower P-Value than 1 - "penalty" in the individual cox analysis will be keep for the sPLS-ICOX approach (default: 1).

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_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 SB.sPLS-ICOX function is designed to perform a single-block sparse partial least squares individual Cox analysis. This method is particularly suited for high-dimensional datasets where the number of variables (features) significantly exceeds the number of observations. The "single-block" in its name indicates that while the function can handle datasets with multiple blocks, it processes each block individually rather than in a multiblock manner where all blocks are analyzed simultaneously.

By analyzing one block at a time, the function ensures a focused and detailed examination of each block's contribution to the survival outcome. This approach is especially beneficial when different blocks represent distinct types or sources of data, allowing for a granular understanding of each block's significance.

The analysis begins by applying a penalty to select significant variables based on individual Cox models. This step ensures that only the most relevant features from the current block contribute to the subsequent sPLS analysis. The sPLS method then identifies latent components that capture the maximum covariance between the explanatory variables (X) from the block and the response (Y), which are the deviance residuals from the Cox models.

Users have the flexibility to specify various hyperparameters, including the number of latent components and the penalty for variable selection. The function also offers options for data preprocessing, such as centering, scaling, and removing variables with near-zero or zero variance.

The output provides a comprehensive overview of the analysis for the processed block, including normalized data information, survival model details, and the sPLS-ICOX model. Visualization tools and metrics such as AIC and BIC further aid in understanding the model's performance and significance for the given block.

In summary, the SB.sPLS-ICOX function offers a powerful approach for survival analysis in high-dimensional settings, ensuring optimal feature selection, dimensionality reduction, and predictive modeling for each individual block in the dataset.

Value

Instance of class "Coxmos" and model "sb.splsicox". The class contains the following elements: X: List of normalized X data information.

• (data): normalized X matrix

• (weightings): PLS weights

• (weightings_norm): PLS normalize weights

• (W.star): PLS W* vector

• (scores): PLS scores/variates

• (x.mean): mean values for X matrix

• (x.sd): standard deviation for X matrix

Y: List of normalized Y data information.

• (deviance_residuals): deviance residual vector used as Y matrix in the sPLS.

• (dr.mean): mean values for deviance residuals Y matrix

• (dr.sd): standard deviation for deviance residuals Y matrix'

• (data): normalized X matrix

• (y.mean): mean values for Y matrix

• (y.sd): standard deviation for Y matrix'

survival_model: List of survival model information.

• fit: coxph object.

• AIC: AIC of cox model.

• BIC: BIC of cox model.

• lp: linear predictors for train data.

• coef: Coefficients for cox model.

• YChapeau: Y Chapeau residuals.

• Yresidus: Y residuals.

list_spls_models: List of sPLS-ICOX models computed for each block.

n.comp: Number of components selected.

penalty Penalty applied.

call: call function

X_input: X input matrix

Y_input: Y input matrix

nzv: Variables removed by remove_near_zero_variance or remove_zero_variance.

nz_coeffvar: Variables removed by coefficient variation near zero.

class: Model class.

time: time consumed for running the cox analysis.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

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
sb.splsicox(X, Y, n.comp = 2, penalty = 0.5, x.center = TRUE, x.scale = TRUE)