README.md

In sdechaumet/ramopls: rAMOPLS: Perform ANOVA-Multiblock Orthogonal Partial Least Square Analysis

Overview

The rAMOPLS package is a tool for helping you to extract relevant information from multivariate experiments with multiple factors. It provides simple functions to perform and assess an Anova Multiblock Orthogonal Partial Least Square analysis (AMOPLS) as described by Boccard and Rudaz (2016) with the specific calculation of Variable Important in the Projection (VIP²) described by González-Ruiz et al. (2017). Since most biological experiments are unbalanced due to a wide range of reasons, the method of stratified subsampling described by Boccard et al. (2019) has also been implemented here.

Most biologist need to extract relevant information (treatment, tissular, organs, genotype, …) from noisy and highly uncontrollable experiments. Most of us will try to use a design-of-experiment to assess any factors which can influence the response (wanted and unwanted variations). But then the tools to assess the effect of each factor and their interactions in multivariate experiments are not widely available. The AMOPLS methods combine ANOVA and k-OPLS (Bylesjö et al. (2008)) statistical methods to address these problems and provide easy-to-use diagnostics parameters to evaluate the effects.

Getting started

Installing

rAMOPLS can be installed using one of the following methods:

• Directly from a remote location using remotes or devtools packages
• By cloning or downloading this project and performing an installation from source (see utils::install.packages())

The AMOPLS method depends on the kopls package which is available here. A copy of the package sources is implemented inside the rAMOPLS and can be installed with rAMOPLS::install_kopls().

Calculate an AMOPLS model

The single run_AMOPLS() function is a wrapper to perform AMOPLS model with permutations and return loadings, scores, variances, saliences and statistics. It take as input two table (as matrix, data.frame or data.table) with the first column being unique identifier for rows and the column names from samplemetadata to study (factors):

• datamatrix: with observations as rows and measurments as columns
• samplemetadata: with observations as rows and descriptor as columns (sample groups)
• factor_names: Column names of samplemetadata with factor to consider

Exemple on data_Ruiz2017 dataset on “Exposure time” and “Dose” with level 1 interaction and 100 permutations:

require(rAMOPLS)
result <- run_AMOPLS(datamatrix = data_Ruiz2017$datamatrix,
                     samplemetadata = data_Ruiz2017$samplemetadata, 
                     factor_names = c("Exposure time", "Dose"))

The following arguments are optional (default value):

• interaction_level (1): Order of interactions to consider (0 for main effect and 1 for first order interaction: Fac_A, Fac_B and Fac_A x Fac_B)
• scaling (T): Logical to perform a variance scaling of the datamatrix
• nb_perm (100): Number of permutation to test for each effect (default to 100)
• nb_compo_orthos (1:3): Number of orthogonal component to consider (1:5 to test 1 to 5)
• parallel (FALSE): If TRUE or a number (y), run permutations in parallel one y core using future and furrr for progress bar

Choose the optimal number of orthogonal component

The optimal numnber parsimony principle is applied in the case of equally performing models with a different number of orthogonal components (Boccard and Rudaz (2016)): The significative model with the smallest number of orthogonal component is to be choosen.

In this example the first model is already significant and should be choosen.

fun_plot_ortho(result)

result_optimal <- result$orthoNb_1

Summary statistics

All graphical output use ggplot2 and can be customized according to ggplot2 nomenclature.

• Summary statistics:

fun_get_summary(result_optimal) %>% knitr::kable(digits = 3, align = 'c')

| Effect | Effect Name | RSS | RSR | RSS p-value | RSR p-value | R2Y p-value | PermNb | Tp1 | Tp2 | Tp3 | Tp4 | Tp5 | To1 | | :-------: | :------------------: | :---: | :---: | :---------: | :---------: | :---------: | :----: | :---: | :---: | :---: | :---: | :---: | :---: | | 1 | Exposure time | 0.230 | 1.741 | 0.01 | 0.01 | 0.01 | 100 | 0.993 | 0.015 | 0.039 | 0.031 | 0.072 | 0.171 | | 2 | Dose | 0.120 | 1.164 | 1.00 | 1.00 | 0.01 | 100 | 0.002 | 0.933 | 0.059 | 0.864 | 0.108 | 0.256 | | 12 | Exposure time x Dose | 0.087 | 1.079 | 1.00 | 1.00 | 0.01 | 100 | 0.002 | 0.025 | 0.833 | 0.050 | 0.693 | 0.276 | | residuals | residuals | 0.562 | 1.000 | NA | NA | NA | NA | 0.002 | 0.027 | 0.068 | 0.054 | 0.126 | 0.298 |

• Optimal score plot:

fun_plot_optimal_scores(result_optimal)

• Optimal loading plot:

fun_plot_optimal_loadings(result_optimal)

• VIP² as described in González-Ruiz et al. (2017):

fun_plot_VIPs(result_optimal, "Dose")

Stratified subsampling for unbalanced data

Unbalanced experimental designs involve non-orthogonal ANOVA submatrices which can alter effects interpretation (Drotleff and Lämmerhofer (2019)). To cope with unbalanced design, the most strategy is to resample the dataset to get a balanced design by either:

• oversampling: complete the group with missing observations by adding existing ones.
• undersampling: delete observations in each group to align with n in the smallest group.

Since oversampling may alter variance decomposition by incorporating identical samples to the model, Boccard et al. (2019) used a strategy based on undersampling strategy. To cope with minimized dataset (and therfore deleted observation), the subsampling is randomly performed a high number of times (1000s) and the models are combined using ensemble-based estimates from balanced models (involving median calculation of each parameters obtained from AMOPLS).

This strategy as been implemented in rAMOPLS and is automatically triggered if the input dataset are unbalanced. The number of subsampling steps can be customized with the subsampling parameter.

Example on the same dataset as Boccard et al. (2019):

require(rAMOPLS)
result_unbalanced <- run_AMOPLS(
  datamatrix = data_Boccard2019$datamatrix,
  samplemetadata = data_Boccard2019$samplemetadata, 
  factor_names = c("Chemical", "Batch"), 
  nb_perm = 100,
  subsampling = 10,
  parallel = 3)
result_optimal <- result_unbalanced[[1]]

• Summary statistics:

fun_get_summary(result_optimal) %>% knitr::kable(digits = 3, align = 'c')

| Effect | Effect Name | RSS | RSR | RSS p-value | RSR p-value | R2Y p-value | PermNb | Tp1 | Tp2 | Tp3 | Tp4 | Tp5 | Tp6 | Tp7 | Tp8 | Tp9 | Tp10 | Tp11 | Tp12 | Tp13 | Tp14 | Tp15 | Tp16 | Tp17 | Tp18 | Tp19 | Tp20 | To1 | | :-------: | :--------------: | :---: | :----: | :---------: | :---------: | :---------: | :----: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | 1 | Chemical | 0.165 | 4.075 | 0.01 | 0.01 | 0.01 | 100 | 0.000 | 0.000 | 0.998 | 0.001 | 0.986 | 0.003 | 0.971 | 0.021 | 0.032 | 0.902 | 0.045 | 0.682 | 0.057 | 0.054 | 0.065 | 0.105 | 0.357 | 0.112 | 0.109 | 0.118 | 0.154 | | 2 | Batch | 0.611 | 17.463 | 0.01 | 0.01 | 0.01 | 100 | 0.999 | 0.998 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.005 | 0.007 | 0.004 | 0.011 | 0.014 | 0.013 | 0.013 | 0.015 | 0.024 | 0.027 | 0.026 | 0.025 | 0.028 | 0.036 | | 12 | Chemical x Batch | 0.157 | 3.410 | 0.01 | 0.01 | 0.01 | 100 | 0.000 | 0.000 | 0.000 | 0.995 | 0.003 | 0.984 | 0.006 | 0.886 | 0.853 | 0.022 | 0.772 | 0.076 | 0.704 | 0.688 | 0.644 | 0.484 | 0.141 | 0.410 | 0.414 | 0.372 | 0.184 | | residuals | residuals | 0.065 | 1.000 | NA | NA | NA | NA | 0.001 | 0.001 | 0.002 | 0.003 | 0.006 | 0.006 | 0.022 | 0.087 | 0.108 | 0.069 | 0.168 | 0.235 | 0.229 | 0.236 | 0.279 | 0.379 | 0.467 | 0.447 | 0.441 | 0.469 | 0.627 |

• Optimal score plot:

fun_plot_optimal_scores(result_optimal)

• Optimal loading plot:

fun_plot_optimal_loadings(result_optimal)

• VIP² as described in González-Ruiz et al. (2017):

fun_plot_VIPs(result_optimal, "Chemical")

Model interpretation

The first step is to ensure the consitency of the ANOVA partitionning, the number of orthogonal components to include in the model and the reliability of the observed effects using the following indices:

• ANOVA consistency by using the Residual Structure Ratio (RSR) associated to each effect. This indicator represents the data structure associated to each effect in regard of the residual. Therefore it is an effect-to-residual ratio that reflects the reliability of each effect. Close or equal to 1 mean no effect.
• Reliability of the observed effects is assessed by checking the Realtive Sum of Square (RSS).
• Optimal number of orthogonal components using R²Y and its associated p-value. In the case of equally performing models, the parsimony principle is applied and the least of significative orthogonal component number is choosen. The R²Y indice represents the explained variation of Y An R²Y parameter closer to 1 means a stronger models (more of the predicted variations are captured by the model).

Random experimental designs are expected to produce low R2Y values (no model) and RSR indices close to one (no effect). When both the AMOPLS model R2Y and individual RSR indices are deemed significant, biochemical information can be extracted from the loadings of the predictive components (Boccard and Rudaz (2016)).

To learn more

You can find more detailled explanation on AMOPLS in the references provided at the end of this page, starting by the original article from Boccard et al. (2010).

Datasets provided

The package is preloaded with the following datasets to test and compare the functionalities with published articles:

• liver.toxicity: Microarray of gene expression from Fisher rats after acetaminophen exposition Bushel et al. (2007) and used in Boccard and Rudaz (2016)
• data_Boccard2016: Metabolomic profiles of A. thaliana leaves from Boccard et al. (2010) used in Boccard and Rudaz (2016)
• data_Ruiz2017: Metabolomic profiles of human neural cells from González-Ruiz et al. (2017)
• data_Boccard2019: Metabolomic profiles used to demonstrate the subsampling strategy in Boccard et al. (2019)

References

**Boccard J, Kalousis A, Hilario M, Lantéri P, Hanafi M, Mazerolles G, Wolfender JL, Carrupt PA, Rudaz S** (2010) Standard machine learning algorithms applied to UPLC-TOF/MS metabolic fingerprinting for the discovery of wound biomarkers in Arabidopsis thaliana. Chemometrics and Intelligent Laboratory Systems **104**: 20–27

**Boccard J, Rudaz S** (2016) Exploring Omics data from designed experiments using analysis of variance multiblock Orthogonal Partial Least Squares. Analytica Chimica Acta **920**: 18–28

**Boccard J, Tonoli D, Strajhar P, Jeanneret F, Odermatt A, Rudaz S** (2019) Removal of batch effects using stratified subsampling of metabolomic data for in vitro endocrine disruptors screening. Talanta **195**: 77–86

**Bushel PR, Wolfinger RD, Gibson G** (2007) Simultaneous clustering of gene expression data with clinical chemistry and pathological evaluations reveals phenotypic prototypes. BMC Systems Biology. doi: [10.1186/1752-0509-1-15](https://doi.org/10.1186/1752-0509-1-15)

**Bylesjö M, Rantalainen M, Nicholson JK, Holmes E, Trygg J** (2008) K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space. doi: [10.1186/1471-2105-9-106](https://doi.org/10.1186/1471-2105-9-106)

**Drotleff B, Lämmerhofer M** (2019) Guidelines for selection of internal standard-based normalization strategies in untargeted lipidomic profiling by LC-HR-MS/MS. Analytical Chemistry **91**: 9836–9843

**González-Ruiz V, Pezzatti J, Roux A, Stoppini L, Boccard J, Rudaz S** (2017) Unravelling the effects of multiple experimental factors in metabolomics, analysis of human neural cells with hydrophilic interaction liquid chromatography hyphenated to high resolution mass spectrometry. Journal of Chromatography A **1527**: 53–60

sdechaumet/ramopls documentation built on Feb. 15, 2024, 6:29 p.m.