| Title: | Predicting and Classifying Patient Phenotypes with Multi-Omics Data |
|---|---|
| Description: | Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome. |
| Authors: | Runzhi Zhang [aut, cre], Susmita Datta [aut, ths] |
| Maintainer: | Runzhi Zhang <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.0 |
| Built: | 2024-11-07 06:48:32 UTC |
| Source: | CRAN |
Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.
The DESCRIPTION file:
| Package: | asmbPLS |
| Type: | Package |
| Title: | Predicting and Classifying Patient Phenotypes with Multi-Omics Data |
| Version: | 1.0.0 |
| Date: | 2023-04-13 |
| Authors@R: | c( person("Runzhi", "Zhang", role = c("aut", "cre"), email = "[email protected]"), person("Susmita", "Datta", role = c("aut", "ths"), email = "[email protected]")) |
| Description: | Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome. |
| License: | GPL (>= 2) |
| Encoding: | UTF-8 |
| Depends: | R (>= 3.5.0) |
| Imports: | Rcpp (>= 1.0.8), ggplot2, ggpubr, stats |
| LinkingTo: | Rcpp, RcppArmadillo |
| RoxygenNote: | 7.2.3 |
| LazyData: | true |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| Author: | Runzhi Zhang [aut, cre], Susmita Datta [aut, ths] |
| Maintainer: | Runzhi Zhang <[email protected]> |
| NeedsCompilation: | yes |
| Packaged: | 2023-04-13 23:01:41 UTC; zhang |
| Repository: | CRAN |
| Date/Publication: | 2023-04-17 09:50:05 UTC |
| Config/pak/sysreqs: | cmake make libicu-dev |
Index of help topics:
asmbPLS-package Predicting and Classifying Patient Phenotypes
with Multi-Omics Data
asmbPLS.cv Cross-validation for asmbPLS to find the best
combinations of quantiles for prediction
asmbPLS.example Example data for asmbPLS algorithm
asmbPLS.fit asmbPLS for block-structured data
asmbPLS.predict Using an asmbPLS model for prediction of new
samples
asmbPLSDA.cv Cross-validation for asmbPLS-DA to find the
best combinations of quantiles for
classification
asmbPLSDA.example Example data for asmbPLS-DA algorithm
asmbPLSDA.fit asmbPLS-DA for block-structured data
asmbPLSDA.predict Using an asmbPLS-DA model for classification of
new samples
asmbPLSDA.vote.fit asmbPLS-DA vote model fit
asmbPLSDA.vote.predict
Using an asmbPLS-DA vote model for
classification of new samples
mbPLS.fit mbPLS for block-structured data
meanimp Mean imputation for the survival time
plotCor Graphical output for the asmbPLS-DA framework
plotPLS PLS plot for asmbPLS-DA
plotRelevance Relevance plot for asmbPLS-DA
quantileComb Create the quantile combination set for asmbPLS
and asmbPLS-DA
to.categorical Converts a class vector to a binary class
matrix
Further information is available in the following vignettes:
asmbPLS_tutorial |
asmbPLS_tutorial (source, pdf) |
Runzhi Zhang [aut, cre], Susmita Datta [aut, ths]
Maintainer: Runzhi Zhang <[email protected]>
add later
asmbPLS.fit, asmbPLS.cv, asmbPLS.predict, mbPLS.fit, meanimp
Function to find the best combinations of quantiles used for prediction via
cross-validation. Usually should be conducted before
asmbPLS.fit to obtain the quantile combinations.
asmbPLS.cv( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb.table, Y.indicator, k = 5, ncv = 5, only.observe = TRUE, expected.measure.decrease = 0.05, center = TRUE, scale = TRUE, maxiter = 100 )asmbPLS.cv( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb.table, Y.indicator, k = 5, ncv = 5, only.observe = TRUE, expected.measure.decrease = 0.05, center = TRUE, scale = TRUE, maxiter = 100 )
X.matrix |
Predictors matrix. Samples in rows, variables in columns. |
Y.matrix |
Outcome matrix. Samples in rows, this is a matrix with one
column (continuous variable). The outcome could be imputed survival time or
other types of continuous outcome. For survival time with right-censored
survival time and event indicator, the right censored time could be imputed
by |
PLS.comp |
Number of PLS components in asmbPLS. |
X.dim |
A vector containing the number of predictors in each block (ordered). |
quantile.comb.table |
A matrix containing user-defined quantile combinations used for CV, whose column number equals to the number of blocks. |
Y.indicator |
A vector containing the event indicator for each sample, whose length is equal to the number of samples. This vector allows the ratio of observed/unobserved to be the same in the training set and validation set. Observed = 1, and unobserved = 0. If other types of outcome data rather than survival outcome is used, you can use a vector with all components = 1 instead. |
k |
The number of folds of CV procedure. The default is 5. |
ncv |
The number of repetitions of CV. The default is 5. |
only.observe |
Whether only observed samples in the validation set should be used for calculating the MSE for CV. The default is TRUE. |
expected.measure.decrease |
The measure you expect to decrease by percent after including one more PLS component, which will affect the selection of optimal PLS components. The default is 0.05 (5%). |
center |
A logical value indicating whether mean center should be implemented for X.matrix and Y.matrix. The default is TRUE. |
scale |
A logical value indicating whether scale should be implemented for X.matrix and Y.matrix. The default is TRUE. |
maxiter |
A integer indicating the maximum number of iteration. The default number is 100. |
asmbPLS.cv returns a list containing the following components:
quantile_table_CV |
A matrix containing the selected quantile combination and the corresponding measures of CV for each PLS component. |
optimal_nPLS |
Optimal number of PLS components. |
.
## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim quantile.comb.table.cv = asmbPLS.example$quantile.comb.table.cv Y.indicator = asmbPLS.example$Y.indicator ## cv to find the best quantile combinations for model fitting cv.results <- asmbPLS.cv(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, Y.indicator = Y.indicator, k = 5, ncv = 3) quantile.comb <- cv.results$quantile_table_CV[,1:length(X.dim)] n.PLS <- cv.results$optimal_nPLS ## asmbPLS fit asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = n.PLS, X.dim = X.dim, quantile.comb = quantile.comb)## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim quantile.comb.table.cv = asmbPLS.example$quantile.comb.table.cv Y.indicator = asmbPLS.example$Y.indicator ## cv to find the best quantile combinations for model fitting cv.results <- asmbPLS.cv(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, Y.indicator = Y.indicator, k = 5, ncv = 3) quantile.comb <- cv.results$quantile_table_CV[,1:length(X.dim)] n.PLS <- cv.results$optimal_nPLS ## asmbPLS fit asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = n.PLS, X.dim = X.dim, quantile.comb = quantile.comb)
Simulated data for asmbPLS.
data(asmbPLS.example)data(asmbPLS.example)
A list including 8 components:
1) X.matrix, a matrix with 100 samples (rows) and 400 features (columns, 1-200 are microbial taxa, 201-400 are metabolites);
2) X.matrix.new, a matrix to be predicted with 100 samples (rows) and 400 features (columns, 1-200 are microbial taxa, 201-400 are metabolites);
3) Y.matrix, a matrix with 100 samples (rows) and 1 column (log-transformed survival time);
4) X.dim, dimension of the two blocks in X.matrix;
5) PLS.comp, selected number of PLS components;
6) quantile.comb, selected quantile combinations;
7) quantile.comb.table.cv, pre-defined quantile combinations for cross validation;
8) Y.indicator, a vector containing the event indicator for each sample.
Function to fit the adaptive sparse multi-block partial least square model
(asmbPLS) with several explanatory blocks as our predictors
to explain the outcome Y.
asmbPLS.fit( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb, center = TRUE, scale = TRUE, maxiter = 100 )asmbPLS.fit( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb, center = TRUE, scale = TRUE, maxiter = 100 )
X.matrix |
Predictors matrix. Samples in rows, variables in columns. |
Y.matrix |
Outcome matrix. Samples in rows, this is a matrix with one
column (continuous variable). The outcome could be imputed survival time.
For survival time with right-censored survival time and event indicator, the
right censored time could be imputed by |
PLS.comp |
Number of PLS components in asmbPLS. |
X.dim |
A vector containing the number of predictors in each block (ordered). |
quantile.comb |
A matrix containing quantile combinations used for different PLS components, whose row number equals to the number of PLS components used, column number equals to the number of blocks. |
center |
A logical value indicating whether mean center should be implemented for X.matrix and Y.matrix. The default is TRUE. |
scale |
A logical value indicating whether scale should be implemented for X.matrix and Y.matrix. The default is TRUE. |
maxiter |
A integer indicating the maximum number of iteration. The default number is 100. |
asmbPLS.fit returns a list containing the following components:
X_dim |
A vector containing the number of predictors in each block. |
X_weight |
A list containing the weights of predictors for different blocks in different PLS components. |
X_score |
A list containing the scores of samples in different blocks in different PLS components. |
X_loading |
A list containing the loadings of predictors for different blocks in different PLS components. |
X_super_weight |
A matrix containing the super weights of different blocks for different PLS components. |
X_super_score |
A matrix containing the super scores of samples for different PLS components. |
Y_weight |
A matrix containing the weights of outcome for different PLS components. |
Y_score |
A matrix containing the scores of outcome for different PLS components. |
X_col_mean |
A matrix containing the mean of each predictor for scaling. |
Y_col_mean |
The mean of outcome matrix for scaling. |
X_col_sd |
A matrix containing the standard deviation of each predictor for scaling. Predictor with sd = 0 will be set to 1. |
Y_col_sd |
The standard deviation of outcome matrix for scaling. |
center |
A logical value indicating whether mean center is implemented for X.matrix and Y.matrix. |
scale |
A logical value indicating whether scale is implemented for X.matrix and Y.matrix. |
## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim quantile.comb = asmbPLS.example$quantile.comb ## asmbPLS fit asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb)## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim quantile.comb = asmbPLS.example$quantile.comb ## asmbPLS fit asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb)
Derives predictions for new samples from a model fitted by the function
asmbPLS.fit or mbPLS.fit.
asmbPLS.predict(fit.results, X.matrix.new, PLS.comp)asmbPLS.predict(fit.results, X.matrix.new, PLS.comp)
fit.results |
The output of either |
X.matrix.new |
A predictors matrix, whose predictors are the same as the predictors in model fitting. |
PLS.comp |
Number of PLS components used for prediction. |
asmbPLSDA.predict returns a list containing the following components:
Y_pred |
Predicted value for the new sampels. |
NewX_super_score |
Predicted super score for new samples, which can be used as predictors for other regression models. |
## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix X.matrix.new = asmbPLS.example$X.matrix.new Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim quantile.comb = asmbPLS.example$quantile.comb ## asmbPLS fit asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb) ## asmbPLS prediction for the new data, you could use different numbers of ## PLS components for prediction ## Use only the first PLS component Y.pred.1 <- asmbPLS.predict(asmbPLS.results, X.matrix.new, 1) ## Use the first two PLS components Y.pred.2 <- asmbPLS.predict(asmbPLS.results, X.matrix.new, 2)## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix X.matrix.new = asmbPLS.example$X.matrix.new Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim quantile.comb = asmbPLS.example$quantile.comb ## asmbPLS fit asmbPLS.results <- asmbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb) ## asmbPLS prediction for the new data, you could use different numbers of ## PLS components for prediction ## Use only the first PLS component Y.pred.1 <- asmbPLS.predict(asmbPLS.results, X.matrix.new, 1) ## Use the first two PLS components Y.pred.2 <- asmbPLS.predict(asmbPLS.results, X.matrix.new, 2)
Function to find the best combinations of quantiles used for classification via
cross-validation. Usually should be conducted before
asmbPLSDA.fit to obtain the quantile combinations.
asmbPLSDA.cv( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb.table, outcome.type, method = NULL, measure = "B_accuracy", k = 5, ncv = 5, expected.measure.increase = 0.005, center = TRUE, scale = TRUE, maxiter = 100 )asmbPLSDA.cv( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb.table, outcome.type, method = NULL, measure = "B_accuracy", k = 5, ncv = 5, expected.measure.increase = 0.005, center = TRUE, scale = TRUE, maxiter = 100 )
X.matrix |
Predictors matrix. Samples in rows, variables in columns. |
Y.matrix |
Outcome matrix. Samples in rows, this is a matrix with one column (binary) or multiple columns (more than 2 levels, dummy variables). |
PLS.comp |
Number of PLS components in asmbPLS-DA. |
X.dim |
A vector containing the number of predictors in each block (ordered). |
quantile.comb.table |
A matrix containing user-defined quantile combinations used for CV, whose column number equals to the number of blocks. |
outcome.type |
The type of the outcome Y. " |
method |
Decision rule used for CV. For binary outcome, the
methods include " |
measure |
Five measures are available: overall accuracy |
k |
The number of folds of CV procedure. The default is 5. |
ncv |
The number of repetitions of CV. The default is 5. |
expected.measure.increase |
The measure you expect to increase after including one more PLS component, which will affect the selection of optimal PLS components. The default is 0.005. |
center |
A logical value indicating whether weighted mean center should
be implemented for |
scale |
A logical value indicating whether scale should be
implemented for |
maxiter |
A integer indicating the maximum number of iteration. The default number is 100. |
asmbPLSDA.cv returns a list containing the following components:
quantile_table_CV |
A matrix containing the selected quantile combination and the corresponding measures of CV for each PLS component. |
optimal_nPLS |
Optimal number of PLS components. |
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv ## cv to find the best quantile combinations for model fitting (binary outcome) cv.results.binary <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", k = 3, ncv = 3) quantile.comb.binary <- cv.results.binary$quantile_table_CV[,1:length(X.dim)] n.PLS.binary <- cv.results.binary$optimal_nPLS ## asmbPLSDA fit using the selected quantile combination (binary outcome) asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = n.PLS.binary, X.dim = X.dim, quantile.comb = quantile.comb.binary, outcome.type = "binary") ## cv to find the best quantile combinations for model fitting ## (categorical outcome with more than 2 levels) cv.results.multiclass <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "multiclass", k = 3, ncv = 2) quantile.comb.multiclass <- cv.results.multiclass$quantile_table_CV[,1:length(X.dim)] n.PLS.multiclass <- cv.results.multiclass$optimal_nPLS ## asmbPLSDA fit (categorical outcome with more than 2 levels) asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = n.PLS.multiclass, X.dim = X.dim, quantile.comb = quantile.comb.multiclass, outcome.type = "multiclass")## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv ## cv to find the best quantile combinations for model fitting (binary outcome) cv.results.binary <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", k = 3, ncv = 3) quantile.comb.binary <- cv.results.binary$quantile_table_CV[,1:length(X.dim)] n.PLS.binary <- cv.results.binary$optimal_nPLS ## asmbPLSDA fit using the selected quantile combination (binary outcome) asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = n.PLS.binary, X.dim = X.dim, quantile.comb = quantile.comb.binary, outcome.type = "binary") ## cv to find the best quantile combinations for model fitting ## (categorical outcome with more than 2 levels) cv.results.multiclass <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "multiclass", k = 3, ncv = 2) quantile.comb.multiclass <- cv.results.multiclass$quantile_table_CV[,1:length(X.dim)] n.PLS.multiclass <- cv.results.multiclass$optimal_nPLS ## asmbPLSDA fit (categorical outcome with more than 2 levels) asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = n.PLS.multiclass, X.dim = X.dim, quantile.comb = quantile.comb.multiclass, outcome.type = "multiclass")
Simulated data for asmbPLS-DA.
data(asmbPLSDA.example)data(asmbPLSDA.example)
A list including 8 components:
1) X.matrix, a matrix with 100 samples (rows) and 400 features, features 1-200 are from block 1 and features 201-400 are from block 2;
2) X.matrix.new, a matrix to be predicted with 100 samples (rows) and 400 features, features 1-200 are from block 1 and features 201-400 are from block 2;
3) Y.matrix.binary, a matrix with 100 samples (rows) and 1 column;
4) Y.matrix.morethan2levels, a matrix with 100 samples (rows) and 3 columns (3 levels);
5) X.dim, dimension of the two blocks in X.matrix;
6) PLS.comp, selected number of PLS components;
7) quantile.comb, selected quantile combinations;
8) quantile.comb.table.cv, pre-defined quantile combinations for cross validation.
Function to fit the adaptive sparse multi-block partial least square
discriminant analysis (asmbPLS-DA) model with several explanatory blocks
as our predictors to explain the categorical outcome Y.
asmbPLSDA.fit( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb, outcome.type, center = TRUE, scale = TRUE, maxiter = 100 )asmbPLSDA.fit( X.matrix, Y.matrix, PLS.comp, X.dim, quantile.comb, outcome.type, center = TRUE, scale = TRUE, maxiter = 100 )
X.matrix |
Predictors matrix. Samples in rows, variables in columns |
Y.matrix |
Outcome matrix. Samples in rows, this is a matrix with one column (binary) or multiple columns (more than 2 levels, dummy variables). |
PLS.comp |
Number of PLS components in asmbPLS-DA. |
X.dim |
A vector containing the number of predictors in each block (ordered). |
quantile.comb |
A matrix containing quantile combinations used for different PLS components, whose row number equals to the number of PLS components used, column number equals to the number of blocks. |
outcome.type |
The type of the outcome Y. " |
center |
A logical value indicating whether weighted mean center should be implemented for X.matrix and Y.matrix. The default is TRUE. |
scale |
A logical value indicating whether scale should be implemented for X.matrix. The default is TRUE. |
maxiter |
A integer indicating the maximum number of iteration. The default number is 100. |
asmbPLSDA.fit returns a list containing the following components:
X_dim |
A vector containing the number of predictors in each block. |
X_weight |
A list containing the weights of predictors for different blocks in different PLS components. |
X_score |
A list containing the scores of samples in different blocks in different PLS components. |
X_loading |
A list containing the loadings of predictors for different blocks in different PLS components. |
X_super_weight |
A matrix containing the super weights of different blocks for different PLS components. |
X_super_score |
A matrix containing the super scores of samples for different PLS components. |
Y_weight |
A matrix containing the weights of outcome for different PLS components. |
Y_score |
A matrix containing the scores of outcome for different PLS components. |
X_col_mean |
A matrix containing the weighted mean of each predictor for scaling. |
Y_col_mean |
The weighted mean of outcome matrix for scaling. |
X_col_sd |
A matrix containing the standard deviation (sd) of each predictor for scaling. sd for predictors with sd = 0 will be changed to 1. |
center |
A logical value indicating whether weighted mean center is implemented for X.matrix and Y.matrix. |
scale |
A logical value indicating whether scale is implemented for X.matrix. |
Outcome_type |
The type of the outcome Y. " |
Y_group |
Original Y.matrix. |
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass")## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass")
Derives classification for new samples from a model fitted by the function
asmbPLSDA.fit.
asmbPLSDA.predict(fit.results, X.matrix.new, PLS.comp, method = NULL)asmbPLSDA.predict(fit.results, X.matrix.new, PLS.comp, method = NULL)
fit.results |
The output of |
X.matrix.new |
A predictors matrix, whose predictors are the same as the predictors in model fitting. |
PLS.comp |
Number of PLS components used for prediction. |
method |
Decision rule used for prediction. For binary outcome, the
methods include " |
asmbPLSDA.predict returns a list containing the following components:
Y_pred |
Predicted class for the new sampels. |
Y_pred_numeric |
Predicted Y values for the new samples, different decision rules can be used to obtain different Y_pred. |
NewX_super_score |
Predicted super score for new samples, which can be used as predictors for other classification algorithms. |
method |
Decision rule used for preidction. |
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix X.matrix.new = asmbPLSDA.example$X.matrix.new Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## asmbPLSDA prediction for the new data, you could use different numbers of ## PLS components for prediction ## Use only the first PLS component Y.pred.binary.1 <- asmbPLSDA.predict(asmbPLSDA.fit.binary, X.matrix.new, PLS.comp = 1) ## Use the first two PLS components Y.pred.binary.2 <- asmbPLSDA.predict(asmbPLSDA.fit.binary, X.matrix.new, PLS.comp = 2) ## PLS components for prediction Y.pred.multiclass.1 <- asmbPLSDA.predict(asmbPLSDA.fit.multiclass, X.matrix.new, PLS.comp = 1) ## Use the first two PLS components Y.pred.multiclass.2 <- asmbPLSDA.predict(asmbPLSDA.fit.multiclass, X.matrix.new, PLS.comp = 2)## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix X.matrix.new = asmbPLSDA.example$X.matrix.new Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## asmbPLSDA prediction for the new data, you could use different numbers of ## PLS components for prediction ## Use only the first PLS component Y.pred.binary.1 <- asmbPLSDA.predict(asmbPLSDA.fit.binary, X.matrix.new, PLS.comp = 1) ## Use the first two PLS components Y.pred.binary.2 <- asmbPLSDA.predict(asmbPLSDA.fit.binary, X.matrix.new, PLS.comp = 2) ## PLS components for prediction Y.pred.multiclass.1 <- asmbPLSDA.predict(asmbPLSDA.fit.multiclass, X.matrix.new, PLS.comp = 1) ## Use the first two PLS components Y.pred.multiclass.2 <- asmbPLSDA.predict(asmbPLSDA.fit.multiclass, X.matrix.new, PLS.comp = 2)
Function to fit multiple asmbPLS-DA models using cross validation results with
different decision rules obtained from asmbPLSDA.cv,
the weight for each model are calculated based on cross-validation accuracy,
which can be used for asmbPLSDA.vote.predict to obtain
the final classification.
asmbPLSDA.vote.fit( X.matrix, Y.matrix, X.dim, cv.results.list, nPLS, outcome.type, method = "weighted", measure = NULL, center = TRUE, scale = TRUE )asmbPLSDA.vote.fit( X.matrix, Y.matrix, X.dim, cv.results.list, nPLS, outcome.type, method = "weighted", measure = NULL, center = TRUE, scale = TRUE )
X.matrix |
Predictors matrix. Samples in rows, variables in columns. |
Y.matrix |
Outcome matrix. Samples in rows, this is a matrix with one column (binary) or multiple columns (more than 2 levels, dummy variables). |
X.dim |
A vector containing the number of predictors in each block (ordered). |
cv.results.list |
A list containing |
nPLS |
A vector containing the number of PLS components used for different decision rules. |
outcome.type |
The type of the outcome Y. " |
method |
Vote options. " |
measure |
Measure to be selected when |
center |
A logical value indicating whether weighted mean center should
be implemented for |
scale |
A logical value indicating whether scale should be
implemented for |
asmbPLSDA.vote.fit returns a list of lists, which can be used as the
inputs for asmbPLSDA.vote.predict. Each list contains the
fit information for model with specific decision rule:
fit.model |
A list containing model fit information. |
nPLS |
The number of PLS components used. |
weight |
The weight for this model. |
outcome.type |
The type of the outcome Y. |
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix X.matrix.new = asmbPLSDA.example$X.matrix.new Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv ## Cross validaiton based on fixed cutoff cv.results.cutoff <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "fixed_cutoff", k = 3, ncv = 1) quantile.comb.cutoff <- cv.results.cutoff$quantile_table_CV ## Cross validation using Euclidean distance of X super score cv.results.EDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Euclidean_distance_X", k = 3, ncv = 1) quantile.comb.EDX <- cv.results.EDX$quantile_table_CV ## Cross validation using Mahalanobis distance of X super score cv.results.MDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Mahalanobis_distance_X", k = 3, ncv = 1) quantile.comb.MDX <- cv.results.MDX$quantile_table_CV #### vote list #### cv.results.list = list(fixed_cutoff = quantile.comb.cutoff, Euclidean_distance_X = quantile.comb.EDX, Mahalanobis_distance_X = quantile.comb.MDX) ## vote models fit vote.fit <- asmbPLSDA.vote.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, X.dim = X.dim, nPLS = c(cv.results.cutoff$optimal_nPLS, cv.results.EDX$optimal_nPLS, cv.results.MDX$optimal_nPLS), cv.results.list = cv.results.list, outcome.type = "binary", method = "weighted")## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix X.matrix.new = asmbPLSDA.example$X.matrix.new Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv ## Cross validaiton based on fixed cutoff cv.results.cutoff <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "fixed_cutoff", k = 3, ncv = 1) quantile.comb.cutoff <- cv.results.cutoff$quantile_table_CV ## Cross validation using Euclidean distance of X super score cv.results.EDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Euclidean_distance_X", k = 3, ncv = 1) quantile.comb.EDX <- cv.results.EDX$quantile_table_CV ## Cross validation using Mahalanobis distance of X super score cv.results.MDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Mahalanobis_distance_X", k = 3, ncv = 1) quantile.comb.MDX <- cv.results.MDX$quantile_table_CV #### vote list #### cv.results.list = list(fixed_cutoff = quantile.comb.cutoff, Euclidean_distance_X = quantile.comb.EDX, Mahalanobis_distance_X = quantile.comb.MDX) ## vote models fit vote.fit <- asmbPLSDA.vote.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, X.dim = X.dim, nPLS = c(cv.results.cutoff$optimal_nPLS, cv.results.EDX$optimal_nPLS, cv.results.MDX$optimal_nPLS), cv.results.list = cv.results.list, outcome.type = "binary", method = "weighted")
Function to make the classification using the weights and fitted model
obtained from asmbPLSDA.vote.fit. The final classification
results are the weighted classification using the decision rules included.
asmbPLSDA.vote.predict(fit.results, X.matrix.new)asmbPLSDA.vote.predict(fit.results, X.matrix.new)
fit.results |
The output of |
X.matrix.new |
A predictors matrix, whose predictors are the same as the predictors in model fitting. |
Y_pred |
Predicted class for the new sampels. |
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix X.matrix.new = asmbPLSDA.example$X.matrix.new Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv ## Cross validaiton based on fixed cutoff cv.results.cutoff <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "fixed_cutoff", k = 3, ncv = 1) quantile.comb.cutoff <- cv.results.cutoff$quantile_table_CV ## Cross validation using Euclidean distance of X super score cv.results.EDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Euclidean_distance_X", k = 3, ncv = 1) quantile.comb.EDX <- cv.results.EDX$quantile_table_CV ## Cross validation using Mahalanobis distance of X super score cv.results.MDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Mahalanobis_distance_X", k = 3, ncv = 1) quantile.comb.MDX <- cv.results.MDX$quantile_table_CV #### vote list #### cv.results.list = list(fixed_cutoff = quantile.comb.cutoff, Euclidean_distance_X = quantile.comb.EDX, Mahalanobis_distance_X = quantile.comb.MDX) ## vote models fit vote.fit <- asmbPLSDA.vote.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, X.dim = X.dim, nPLS = c(cv.results.cutoff$optimal_nPLS, cv.results.EDX$optimal_nPLS, cv.results.MDX$optimal_nPLS), cv.results.list = cv.results.list, outcome.type = "binary", method = "weighted") ## classification vote.predict <- asmbPLSDA.vote.predict(vote.fit, X.matrix.new)## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix X.matrix.new = asmbPLSDA.example$X.matrix.new Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb.table.cv = asmbPLSDA.example$quantile.comb.table.cv ## Cross validaiton based on fixed cutoff cv.results.cutoff <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "fixed_cutoff", k = 3, ncv = 1) quantile.comb.cutoff <- cv.results.cutoff$quantile_table_CV ## Cross validation using Euclidean distance of X super score cv.results.EDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Euclidean_distance_X", k = 3, ncv = 1) quantile.comb.EDX <- cv.results.EDX$quantile_table_CV ## Cross validation using Mahalanobis distance of X super score cv.results.MDX <- asmbPLSDA.cv(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb.table = quantile.comb.table.cv, outcome.type = "binary", method = "Mahalanobis_distance_X", k = 3, ncv = 1) quantile.comb.MDX <- cv.results.MDX$quantile_table_CV #### vote list #### cv.results.list = list(fixed_cutoff = quantile.comb.cutoff, Euclidean_distance_X = quantile.comb.EDX, Mahalanobis_distance_X = quantile.comb.MDX) ## vote models fit vote.fit <- asmbPLSDA.vote.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, X.dim = X.dim, nPLS = c(cv.results.cutoff$optimal_nPLS, cv.results.EDX$optimal_nPLS, cv.results.MDX$optimal_nPLS), cv.results.list = cv.results.list, outcome.type = "binary", method = "weighted") ## classification vote.predict <- asmbPLSDA.vote.predict(vote.fit, X.matrix.new)
Function to fit the multi-block partial least square model
(mbPLS) with several explanatory blocks as our predictors
to explain the outcome Y.
mbPLS.fit( X.matrix, Y.matrix, PLS.comp, X.dim, center = TRUE, scale = TRUE, maxiter = 100 )mbPLS.fit( X.matrix, Y.matrix, PLS.comp, X.dim, center = TRUE, scale = TRUE, maxiter = 100 )
X.matrix |
Predictors matrix. Samples in rows, variables in columns. |
Y.matrix |
Outcome matrix. Samples in rows, this is a matrix with one
column (continuous variable). The outcome could be imputed survival time.
For survival time with right-censored survival time and event indicator, the
right censored time could be imputed by |
PLS.comp |
Number of PLS components in mbPLS. |
X.dim |
A vector containing the number of predictors in each block (ordered). |
center |
A logical value indicating whether mean center should be implemented for X.matrix and Y.matrix. The default is TRUE. |
scale |
A logical value indicating whether scale should be implemented for X.matrix and Y.matrix. The default is TRUE. |
maxiter |
A integer indicating the maximum number of iteration. The default number is 100. |
mbPLS.fit returns a list containing the following components:
X_dim |
A vector containing the number of predictors in each block. |
X_weight |
A list containing the weights of predictors for different blocks in different PLS components. |
X_score |
A list containing the scores of samples in different blocks in different PLS components. |
X_loading |
A list containing the loadings of predictors for different blocks in different PLS components. |
X_super_weight |
A matrix containing the super weights of different blocks for different PLS components. |
X_super_score |
A matrix containing the super scores of samples for different PLS components. |
Y_weight |
A matrix containing the weights of outcome for different PLS components. |
Y_score |
A matrix containing the scores of outcome for different PLS components. |
X_col_mean |
A matrix containing the mean of each predictor for scaling. |
Y_col_mean |
The mean of outcome matrix for scaling. |
X_col_sd |
A matrix containing the standard deviation of each predictor for scaling. Predictor with sd = 0 will be set to 1. |
Y_col_sd |
The standard deviation of outcome matrix for scaling. |
center |
A logical value indicating whether mean center is implemented for X.matrix and Y.matrix. |
scale |
A logical value indicating whether scale is implemented for X.matrix and Y.matrix. |
## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim ## mbPLS fit mbPLS.results <- mbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim)## Use the example dataset data(asmbPLS.example) X.matrix = asmbPLS.example$X.matrix Y.matrix = asmbPLS.example$Y.matrix PLS.comp = asmbPLS.example$PLS.comp X.dim = asmbPLS.example$X.dim ## mbPLS fit mbPLS.results <- mbPLS.fit(X.matrix = X.matrix, Y.matrix = Y.matrix, PLS.comp = PLS.comp, X.dim = X.dim)
In this approach, can be computed using the familiar sample mean
formula provided the censored values are imputed.
meanimp(survival.data, round = FALSE)meanimp(survival.data, round = FALSE)
survival.data |
A matrix of two columns with the first column indicates the survival time and the second column indicates the event indicator (1 and 0, where 1 indicates observed event and 0 indicates unobserved event). |
round |
Whether survival time should be rounded, default = FALSE. |
meanimp returns a list containing the following components:
imputed_table |
A matrix containing the original survival data and the imputed time. |
KM_table |
Kaplan-Meier estimator of failure times. |
## Generate the survival data data_test <- matrix(c(1, 1, 1, 2.5, 5, 7, 1, 1, 0, 1, 0, 1), ncol = 2) ## Mean imputation meanimp(data_test, round = FALSE)## Generate the survival data data_test <- matrix(c(1, 1, 1, 2.5, 5, 7, 1, 1, 0, 1, 0, 1), ncol = 2) ## Mean imputation meanimp(data_test, round = FALSE)
Function to visualize correlations between PLS components from different blocks
using the model fitted by the function asmbPLSDA.fit.
plotCor( fit.results, ncomp = 1, block.name = NULL, group.name = NULL, legend = TRUE )plotCor( fit.results, ncomp = 1, block.name = NULL, group.name = NULL, legend = TRUE )
fit.results |
The output of |
ncomp |
Which component to plot from each block. Should not be larger
than the number of PLS components used ( |
block.name |
A vector containing the named character for each block. It must be ordered and match each block. |
group.name |
A vector containing the named character for each sample
group. For |
legend |
A logical value indicating whether the legend should be added. The default is TRUE. |
The function returns a plot to show correlations between PLS components from different blocks. The lower triangular panel indicates Pearson's correlation coefficient, and the upper triangular panel the scatter plot.
none
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## visualization with default block.name and group.name using the first PLS component plotCor(asmbPLSDA.fit.binary, 1) plotCor(asmbPLSDA.fit.multiclass, 1) ## custom block.name and group.name plotCor(asmbPLSDA.fit.binary, ncomp = 1, block.name = c("mRNA", "protein"), group.name = c("control", "case")) plotCor(asmbPLSDA.fit.multiclass, ncomp = 1, block.name = c("mRNA", "protein"), group.name = c("healthy", "mild", "severe"))## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## visualization with default block.name and group.name using the first PLS component plotCor(asmbPLSDA.fit.binary, 1) plotCor(asmbPLSDA.fit.multiclass, 1) ## custom block.name and group.name plotCor(asmbPLSDA.fit.binary, ncomp = 1, block.name = c("mRNA", "protein"), group.name = c("control", "case")) plotCor(asmbPLSDA.fit.multiclass, ncomp = 1, block.name = c("mRNA", "protein"), group.name = c("healthy", "mild", "severe"))
Function to visualize cluster of samples using super score of different PLS components.
plotPLS(fit.results, comp.X = 1, comp.Y = 2, group.name = NULL, legend = TRUE)plotPLS(fit.results, comp.X = 1, comp.Y = 2, group.name = NULL, legend = TRUE)
fit.results |
The output of |
comp.X |
A integer indicating which PLS component to be used for the X.axis. The default is 1. |
comp.Y |
A integer indicating which PLS component to be used for the Y.axis. The default is 2. |
group.name |
A vector containing the named character for each sample
group. For |
legend |
A logical value indicating whether the legend should be added. The default is TRUE. |
The function returns a plot to show cluster of samples using super score of different PLS components.
none
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## visualization to show the cluster of samples using the first and the second super score plotPLS(asmbPLSDA.fit.binary, comp.X = 1, comp.Y = 2) plotPLS(asmbPLSDA.fit.multiclass, comp.X = 1, comp.Y = 2) ## custom group.name plotPLS(asmbPLSDA.fit.binary, comp.X = 1, comp.Y = 2, group.name = c("control", "case")) plotPLS(asmbPLSDA.fit.multiclass, comp.X = 1, comp.Y = 2, group.name = c("healthy", "mild", "severe"))## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## visualization to show the cluster of samples using the first and the second super score plotPLS(asmbPLSDA.fit.binary, comp.X = 1, comp.Y = 2) plotPLS(asmbPLSDA.fit.multiclass, comp.X = 1, comp.Y = 2) ## custom group.name plotPLS(asmbPLSDA.fit.binary, comp.X = 1, comp.Y = 2, group.name = c("control", "case")) plotPLS(asmbPLSDA.fit.multiclass, comp.X = 1, comp.Y = 2, group.name = c("healthy", "mild", "severe"))
Function to visualize the most relevant features (relevant to the outcome) in each block.
plotRelevance(fit.results, n.top = 10, ncomp = 1, block.name = NULL)plotRelevance(fit.results, n.top = 10, ncomp = 1, block.name = NULL)
fit.results |
The output of |
n.top |
A integer indicating the number of the most relevant features to
be displayed for each block. The default is 10. If the number of selected
features in the block is smaller than |
ncomp |
Which component to plot from each block. Should not be larger
than the number of PLS components used ( |
block.name |
A vector containing the named character for each block. It must be ordered and match each block. |
The function returns a plot to show the most relevant features for each block.
none
## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## visualization to show the most relevant features in each block plotRelevance(asmbPLSDA.fit.binary) plotRelevance(asmbPLSDA.fit.multiclass) ## custom n.top and block.name plotRelevance(asmbPLSDA.fit.binary, n.top = 5, block.name = c("mRNA", "protein")) plotRelevance(asmbPLSDA.fit.multiclass, n.top = 7, block.name = c("miRNA", "protein"))## Use the example dataset data(asmbPLSDA.example) X.matrix = asmbPLSDA.example$X.matrix Y.matrix.binary = asmbPLSDA.example$Y.matrix.binary Y.matrix.multiclass = asmbPLSDA.example$Y.matrix.morethan2levels X.dim = asmbPLSDA.example$X.dim PLS.comp = asmbPLSDA.example$PLS.comp quantile.comb = asmbPLSDA.example$quantile.comb ## asmbPLSDA fit for binary outcome asmbPLSDA.fit.binary <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.binary, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "binary") ## asmbPLSDA fit for categorical outcome with more than 2 levels asmbPLSDA.fit.multiclass <- asmbPLSDA.fit(X.matrix = X.matrix, Y.matrix = Y.matrix.multiclass, PLS.comp = PLS.comp, X.dim = X.dim, quantile.comb = quantile.comb, outcome.type = "multiclass") ## visualization to show the most relevant features in each block plotRelevance(asmbPLSDA.fit.binary) plotRelevance(asmbPLSDA.fit.multiclass) ## custom n.top and block.name plotRelevance(asmbPLSDA.fit.binary, n.top = 5, block.name = c("mRNA", "protein")) plotRelevance(asmbPLSDA.fit.multiclass, n.top = 7, block.name = c("miRNA", "protein"))
Create the quantile combination set given quantile set for each block.
quantileComb(quantile.list)quantileComb(quantile.list)
quantile.list |
A list containing the quantile set for each block. |
The quantile combination used for asmbPLS and asmbPLS-DA models.
## Generate quantile set for each block ## For example, we have three blocks quantile_1 <- c(0.999, 0.9992, 0.9994, 0.9996, 0.9998) quantile_2 <- c(0.96, 0.97, 0.98, 0.99, 0.995) quantile_3 <- c(0.95, 0.96, 0.97, 0.98, 0.99) quantilelist <- list(quantile_1, quantile_2, quantile_3) quantile.comb <- quantileComb(quantilelist)## Generate quantile set for each block ## For example, we have three blocks quantile_1 <- c(0.999, 0.9992, 0.9994, 0.9996, 0.9998) quantile_2 <- c(0.96, 0.97, 0.98, 0.99, 0.995) quantile_3 <- c(0.95, 0.96, 0.97, 0.98, 0.99) quantilelist <- list(quantile_1, quantile_2, quantile_3) quantile.comb <- quantileComb(quantilelist)
This function converts a class vector to a binary class matrix, with the number of columns equal to the number of levels in the input vector. Each row of the output matrix corresponds to a single observation in the input vector, and the columns represent the different classes in the input vector. A value of 1 in a particular column indicates that the corresponding observation belongs to that class, while a value of 0 indicates that it does not.
to.categorical(categorical.vector)to.categorical(categorical.vector)
categorical.vector |
A factor or character vector representing the class labels. |
A binary class matrix with the number of rows equal to the length of the input vector, and the number of columns equal to the number of unique levels in the input vector. The row and column names of the output matrix are set to the levels of the input vector.
## Generate a class vector vector.test <- factor(c(1,1,1,2,2,2,3,3,3), levels = c(1,2,3)) ## Convert the class vector to binary class matrix output.matrix <- to.categorical(vector.test)## Generate a class vector vector.test <- factor(c(1,1,1,2,2,2,3,3,3), levels = c(1,2,3)) ## Convert the class vector to binary class matrix output.matrix <- to.categorical(vector.test)