Package loading
library(pander)
library(gridExtra)
library(ggplot2)
library(SummarizedExperiment)
Introduction
The model used in this example is a three-way ANOVA with fixed effects. This document presents all the usual steps of the analysis, from importing the data to visualising the results. Details on the methods used and the package implementation can be found in the articles of Thiel, Féraud, and Govaerts (2017), Guisset, Martin, and Govaerts (2019) and Thiel et al. (2023).
Installation and loading of the limpca package
limpca can be installed from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("limpca")
And then loaded into your R session:
library("limpca")
Data import
In order to use the limpca core functions, the data need to be formatted as a list (informally called an lmpDataList) with the following elements: outcomes (multivariate matrix), design (data.frame) and formula (character string).
The UCH data set is already formatted appropriately and can be loaded from limpca with the data function.
data("UCH")
str(UCH)
List of 3
$ design :'data.frame': 34 obs. of 5 variables:
..$ Hippurate: Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 2 2 2 2 ...
..$ Citrate : Factor w/ 3 levels "0","2","4": 1 1 2 2 3 3 1 1 2 2 ...
..$ Dilution : Factor w/ 1 level "diluted": 1 1 1 1 1 1 1 1 1 1 ...
..$ Day : Factor w/ 2 levels "2","3": 1 1 1 1 1 1 1 1 1 1 ...
..$ Time : Factor w/ 2 levels "1","2": 1 2 1 2 1 2 1 2 1 2 ...
$ outcomes: num [1:34, 1:600] 0.0312 0.0581 0.027 0.0341 0.0406 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:34] "M2C00D2R1" "M2C00D2R2" "M2C02D2R1" "M2C02D2R2" ...
.. ..$ X1: chr [1:600] "9.9917004" "9.9753204" "9.9590624" "9.9427436" ...
$ formula : chr "outcomes ~ Hippurate + Citrate + Time + Hippurate:Citrate + Time:Hippurate + Time:Citrate + Hippurate:Citrate:Time"
Alternatively, the lmpDataList can be created with the function data2LmpDataList :
UCH2 <- data2LmpDataList(outcomes = UCH$outcomes, design = UCH$design,
formula = UCH$formula)
| dim outcomes: 34x600
| formula: ~ Hippurate + Citrate + Time + Hippurate:Citrate + Time:Hippurate + Time:Citrate + Hippurate:Citrate:Time
| design variables (5):
* Hippurate (factor)
* Citrate (factor)
* Dilution (factor)
* Day (factor)
* Time (factor)
- or from a
SummarizedExperiment:
se <- SummarizedExperiment(assays = list(counts = t(UCH$outcomes)),
colData = UCH$design, metadata = list(formula = UCH$formula))
UCH3 <- data2LmpDataList(se, assay_name = "counts")
| dim outcomes: 34x600
| formula: ~ Hippurate + Citrate + Time + Hippurate:Citrate + Time:Hippurate + Time:Citrate + Hippurate:Citrate:Time
| design variables (5):
* Hippurate (factor)
* Citrate (factor)
* Dilution (factor)
* Day (factor)
* Time (factor)
SummarizedExperiment is a generic data container that stores rectangular matrices of experimental results. See Morgan et al. (2023) for more information.
Data exploration
The UCH (Urine-Citrate-Hippurate) data set is described in Thiel, Féraud, and Govaerts (2017) and Guisset, Martin, and Govaerts (2019) and is issued form a metabolomics experiment. In this experiment, 36 samples of a pool of rat urine samples were spiked with two molecules Citrate and Hippurate according to a \(3^2\) full factorial design in the quantities of these two molecules. The spiked samples were analyzed by 1H NMR at two different time after defrozing and over two different days. Two of the spectra where finally missing at the end of the experiment.
The UCH data set is a list containing 2 elements:
- an
outcomes matrix with 34 observations of 600 response variables representing the spectra from the 1H NMR spectroscopy,
- a
design matrix with 34 observations and 4 explanatory variables
A formula with the general linear model to be estimated will be added to this list below.
For the purpose of this example, only 3 factors of interest will be studied : concentrations of Hippurate and Citrate and Time after defrozing.
Here are the limpca functions that are available for data exploration.
limpca data exploration functions
Design
The design matrix contains the information about each observation for the four variables: Hippurate, Citrate, Day and Time. Only 3 of these variables are used in the model. The function plotDesign is useful to visualise the design.
pander(head(UCH$design))
plotDesign(design = UCH$design, x = "Hippurate", y = "Citrate",
rows = "Time", title = "Design of the UCH dataset")
This plot confirms that the design is a full 3x3x2 factorial design replicated twice with 2 missing values. Hence, the design is not balanced.
Outcomes visualization
The 600 response (outcomes) variables represent, for each observation, the intensities of the 1H NMR spectra. These spectra can be visualized by the plotLine function.
plotLine function
Here, annotations (cit_peaks and hip_peaks) are added to the ggplot objects in order to highlight the Hippurate (green) and Citrate (red) peaks.
p1 <- plotLine(Y = UCH$outcomes, title = "H-NMR spectrum", rows = c(3),
xlab = "ppm", ylab = "Intensity")
cit_peaks <- annotate("rect", xmin = c(2.509), xmax = c(2.709),
ymin = -Inf, ymax = Inf, alpha = 0.2, fill = c("tomato"))
hip_peaks <- annotate("rect", xmin = c(7.458, 3.881), xmax = c(7.935,
4.041), ymin = -Inf, ymax = Inf, alpha = 0.2, fill = c("yellowgreen"))
p1 <- plotLine(Y = UCH$outcomes, title = "H-NMR spectrum", rows = c(3),
xlab = "ppm", ylab = "Intensity")
p1 + cit_peaks + hip_peaks
plotScatter function
The plotScatter function enables to visualize the values of two outcomes variables with different colors or markers for the values of the design factors. Here, it is used to show that the \(3^2\) factorial design can be recovered from the intensities of the Hippurate and Citrate peaks in the spectra.
# xy corresponds to citrate (453) and hippurate peaks (369)
plotScatter(Y = UCH$outcomes, xy = c(453, 369), design = UCH$design,
color = "Hippurate", shape = "Citrate")
# Or refering to the variables names (ppm values here)
plotScatter(Y = UCH$outcomes, xy = c("2.6092056", "3.9811536"),
design = UCH$design, color = "Hippurate", shape = "Citrate")
plotScatterM function
The plotScatter function allows to visualize the values of a series of outcomes variables with different colors or markers for the values of the design factors. Here, it is done for the 4 peaks of Hippurate and single peak of Citrate.
plotScatterM(Y = UCH$outcomes, cols = c(133, 145, 150, 369, 453),
design = UCH$design, varname.colorup = "Hippurate", varname.colordown = "Citrate")
plotMeans function
The plotMeans represents the mean values of a response variable for different levels of the design factors. Here we show the evolution of the Citrate peak height with respect to the three design factors of interest.
Note that the results of this function must be interpreted with caution for unbalanced designs because simple means are biased estimators of expected means.
plotMeans(Y = UCH$outcomes, design = UCH$design, cols = c(453),
x = c("Citrate"), w = c("Hippurate"), z = c("Time"), ylab = "Intensity",
title = c("Mean reponse for main Citrate peak"))
$`2.6092056`
PCA
The function pcaBySvd is useful to compute a Principal Component Analysis (PCA) decomposition of the outcomes matrix. Several functions can be applied to the output value of pcaBySvd:
pcaScreePlot to obtaine a scree plotpcaLoading1dPlot for the loading plotspcaScorePlot for the score plots
ResPCA <- pcaBySvd(UCH$outcomes)
pcaScreePlot(ResPCA, nPC = 6)
The score plots below indicate that all tree factors from the design affect the spectral profiles, which will be more clearly highlighted by ASCA and APCA.
pcaScorePlot(resPcaBySvd = ResPCA, axes = c(1, 2), title = "PCA scores plot: PC1 and PC2",
design = UCH$design, color = "Hippurate", shape = "Citrate",
points_labs_rn = FALSE)
pcaScorePlot(resPcaBySvd = ResPCA, axes = c(1, 2), title = "PCA scores plot: PC1 and PC2",
design = UCH$design, color = "Time", shape = "Hippurate",
points_labs_rn = FALSE)
pcaScorePlot(resPcaBySvd = ResPCA, axes = c(3, 4), title = "PCA scores plot: PC3 and PC4",
design = UCH$design, color = "Time", shape = "Citrate", points_labs_rn = FALSE)
In the first two loading plots, a mixture of Citrate and Hippurate peaks already appears but they are not separated.
p2 <- pcaLoading1dPlot(resPcaBySvd = ResPCA, axes = c(1, 2),
title = "PCA loadings plot UCH", xlab = "ppm", ylab = "Intensity")
p2 + hip_peaks + cit_peaks
Application of ASCA+ and APCA+
Here below, ASCA+ and APCA+ steps are illustrated on the UCH data set. The following graph represents the steps of the methodology.
The next graph presents the functions available in limpca for this purpose. They are all illustrated in the next sections.
limpca ASCA/APCA functions
Model estimation and effect matrix decomposition
Model matrix generation
The first step of ASCA+ is to build the (GLM) model matrix from the experimental design matrix and the model. Each factor is reencoded with multiple binary variables using contr.sum coding. The size of the model matrix is 34xp with p being the total number of parameters in the ANOVA model for one response.
The function lmpModelMatrix encodes the design matrix as a model matrix.
resLmpModelMatrix <- lmpModelMatrix(UCH)
pander::pander(head(resLmpModelMatrix$modelMatrix))
Model estimation and effect matrices decomposition
lmpEffectMatrices is the used to estimate the linear model and decomposes the multivariate outcomes into effect matrices for every model term. This function calculates also type III effect contributions (in %) and generates a barpot to visualise these contributions.
resLmpEffectMatrices <- lmpEffectMatrices(resLmpModelMatrix)
Effects importance
The contributions from each effect is outputted from lmpEffectMatrices.
pander(resLmpEffectMatrices$variationPercentages)
resLmpEffectMatrices$varPercentagesPlot
Bootstrap tests and quantification of effects importance
lmpBootstrapTests applies a parametric bootstrap test to determine whether an effect is significant or not. We recommend the user to choose first a small value of nboot (e.g. nboot=100) to develop its code and increase it later on (e.g. nboot=1000) in order to get an accurate value for the p-values.
resLmpBootstrapTests <- lmpBootstrapTests(resLmpEffectMatrices = resLmpEffectMatrices,
nboot = 100)
# Print P-values
pander::pander(t(resLmpBootstrapTests$resultsTable))
ASCA/APCA/ASCA-E decomposition
The ASCA/APCA/ASCA-E decomposition enables to represent the information from the effect matrices in a space of reduced dimension through PCA. The function lmpPcaEffects has a method argument to define which method to use, namely ASCA, APCA or ASCA-E. Combined effects matrices can also be built and visualized by PCA.
ASCA
The ASCA method performs PCA on the pure effect matrices. Here a combined effect matrix Hippurate+Time+Hippurate:Time is also built.
resASCA <- lmpPcaEffects(resLmpEffectMatrices = resLmpEffectMatrices,
method = "ASCA", combineEffects = list(c("Hippurate", "Time",
"Hippurate:Time")))
Contributions
The contribution of each principal component of the effects is calculated and reported in different tables and plots with the function lmpContributions.
resLmpContributions <- lmpContributions(resASCA)
The tables are:
totalContribTable: Table of the contribution of each effect to the total variance in percentage as outputted from lmpEffectMatrices.
pander::pander(resLmpContributions$totalContribTable)
effectTable: Table of the percentage of variance explained by each Principal Component in each model effect decomposition.
pander::pander(resLmpContributions$effectTable)
combinedEffectTable: Equivalent of the previous effectTable but for the combination of effects mentioned in lmpPcaEffects, here for Hippurate+Time+Hippurate:Time.
pander::pander(resLmpContributions$combinedEffectTable)
contribTable: Table of the percentage of variance explained by each Principal Component of each effect reported to the percentage contribution of the given effect to the total variance. This table gives an overview of the importance of each PC regardless of the effects.
pander::pander(resLmpContributions$contribTable)
- Moreover
lmpContributions also produces a barplot either with the ordered contributions per effect (plotTotal) or across all the PCs of the different effects (plotContrib).
pander("Ordered contributions per effect:")
Ordered contributions per effect:
resLmpContributions$plotTotal
pander("For each PC of the different effects:")
For each PC of the different effects:
resLmpContributions$plotContrib
In the following analysis, we will focus only on the first PC of the three main effects, the interaction Hippurate:Time and the residuals since the other PCs and effects account for less than 1% of the total variance.
Scores and loadings Plots
The loadings can be represented either on a line plot with the function lmpLoading1dPlot to conveniently compare them with the original spectral profiles or on a scatterplot with the function lmpLoading2dPlot.
Here we create an object including the loading plots (as line plots) for all the effects included in the model as well as the combined effect and the residuals.
all_loadings_pl <- lmpLoading1dPlot(resASCA, effectNames = c("Hippurate",
"Citrate", "Time", "Hippurate:Time", "Hippurate+Time+Hippurate:Time",
"Residuals"), axes = 1, xlab = "ppm")
The score matrices are represented two components at a time on a scatterplot with the function lmpScorePlot.
Main effects
The scores and loadings of the main effects included in the model are represented below.
They show that, thank to the ASCA analysis, Hippurate and Citrate peaks are clearly and separately retrieved in the respective loading plots compared to the classical PCA (see Section 4.3) where peaks of these two chemicals are both present in the two first PCs. The loading profile of the Time effect shows a high peak on the left side of the removed water area.
# Hippurate
hip_scores_pl <- lmpScorePlot(resASCA, effectNames = "Hippurate",
color = "Hippurate", shape = "Hippurate")
hip_loadings_pl <- all_loadings_pl$Hippurate + hip_peaks
grid.arrange(hip_scores_pl, hip_loadings_pl, ncol = 2)
# Citrate
cit_scores_pl <- lmpScorePlot(resASCA, effectNames = "Citrate",
color = "Citrate", shape = "Citrate")
cit_loadings_pl <- all_loadings_pl$Citrate + cit_peaks
grid.arrange(cit_scores_pl, cit_loadings_pl, ncol = 2)
# Time
tim_scores_pl <- lmpScorePlot(resASCA, effectNames = "Time",
color = "Time", shape = "Time")
Warning in FUN(X[[i]], ...): The variance of PC2 is inferior to 1%. Graph
scaled
time_peaks <- annotate("rect", xmin = c(5.955364), xmax = c(6.155364),
ymin = -Inf, ymax = Inf, alpha = 0.2, fill = c("royalblue"))
tim_loadings_pl <- all_loadings_pl$Time + time_peaks
grid.arrange(tim_scores_pl, tim_loadings_pl, ncol = 2)
Interaction Hippurate:Time
The scores and loadings fot the interaction term is represented here. It is not straighforward to interpret the scores plot of such an interaction term but the loadings of PC1 indicate a shift in the spectrum, along the whole spectral profile (but mostly around 3 ppm).
# Hippurate:Time
hiptim_scores_pl <- lmpScorePlot(resASCA, effectNames = "Hippurate:Time",
color = "Hippurate", shape = "Time")
hiptim_loadings_pl <- all_loadings_pl$`Hippurate:Time` + time_peaks +
hip_peaks
grid.arrange(hiptim_scores_pl, hiptim_loadings_pl, ncol = 2)
Combination of effects Hippurate+Time+Hippurate:Time
The scores and the loadings of a combination of effects, in this case "Hippurate+Time+Hippurate:Time" can also be visualised.
# Hippurate+Time+Hippurate:Time
hiptimInter_scores_pl <- lmpScorePlot(resASCA, effectNames = "Hippurate+Time+Hippurate:Time",
color = "Hippurate", shape = "Time")
hiptimInter_loadings_pl <- all_loadings_pl$`Hippurate:Time` +
time_peaks + hip_peaks
grid.arrange(hiptimInter_scores_pl, hiptimInter_loadings_pl,
ncol = 2)
However, note that a better graphical representation is possible with the function lmpEffectPlot (see Section 7.1.4) to depict interaction terms or combinations of effects.
Model residuals
PCA on the model residuals can also be applied to spot outliers or an unexpected underlying structure of the data. The scores plot shows that the effect of Day, which was excluded in our modeling step, could have been included as well as it spans the two first PCs of the residuals.
resid_scores_pl <- lmpScorePlot(resASCA, effectNames = "Residuals",
color = "Day", shape = "Day", drawShapes = "segment")
resid_loadings_pl <- all_loadings_pl$Residuals
grid.arrange(resid_scores_pl, resid_loadings_pl, ncol = 2)
Other graphs
Scores scatter plot
We can also represent the scores as a matrix of plots with lmpScoreScatterPlotM. This graph has the advantage to compare more than 2 variables simultaneously. For example, the PC1 scores of Hippurate and Citrate clearly represent the orthogonal design of this experiment. The interaction Hippurate:Time can also be clearly represented when comparing les PC1s of Hippurate and the interaction term.
lmpScoreScatterPlotM(resASCA, PCdim = c(1, 1, 1, 1, 1, 1, 1,
2), modelAbbrev = TRUE, varname.colorup = "Citrate", varname.colordown = "Time",
varname.pchup = "Hippurate", varname.pchdown = "Time", title = "ASCA scores scatterplot matrix")
2D Loadings
Finally the loadings can also be represented 2-by-2 as a scatterplot with lmpLoading2dPlot. Such graphic is of course of limited interest for spectral data.
lmpLoading2dPlot(resLmpPcaEffects = resASCA, effectNames = c("Hippurate"),
axes = c(1, 2), addRownames = TRUE, pl_n = 10)
Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
# adding manually labels to points for the Hippurate peaks
labels <- substr(colnames(UCH$outcomes), 1, 4)
labels[-c(369, 132, 150, 133, 149, 144, 145, 368, 151)] <- ""
lmpLoading2dPlot(resLmpPcaEffects = resASCA, effectNames = c("Hippurate"),
axes = c(1, 2), points_labs = labels)
Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Effects plot
The lmpEffectPlot function is particularly interesting to visualise an interaction term or a combination of effects. Note that this function can only be applied with the ASCA method.
Main effects for Hippurate
Here the first PC of the Hippurate is represented along the levels of this effect showing an expected linear effect of Hippurate dose on the PC value.
lmpEffectPlot(resASCA, effectName = "Hippurate", x = "Hippurate")
Interaction Hippurate:Time
A more interesting application is the visualization of interaction terms as line plots, here Hippurate:Time along the Hippurate or the Time effect. This representation gives the impression of a quite important interaction effect.
lmpEffectPlot(resASCA, effectName = "Hippurate:Time", x = "Hippurate",
z = "Time")
lmpEffectPlot(resASCA, effectName = "Hippurate:Time", x = "Time",
z = "Hippurate")
Combination of effects Hippurate+Time+Hippurate:Time
An alternative visualisation of this interaction combines the main effects of Hippurate, Time and the interaction Hippurate:Time. Even if significant, the interaction effect is actually quite small compared to the main effects (Hippurate for PC1 and Time for PC2).
lmpEffectPlot(resASCA, effectName = "Hippurate+Time+Hippurate:Time",
x = "Hippurate", z = "Time")
lmpEffectPlot(resASCA, effectName = "Hippurate+Time+Hippurate:Time",
axes = c(2), x = "Time", z = "Hippurate")
APCA
The APCA method performs PCA on the effect matrices augmented by the residuals. The same functions already used for ASCA can be applied.
resAPCA <- lmpPcaEffects(resLmpEffectMatrices = resLmpEffectMatrices,
method = "APCA")
Scores Plot
Different shapes with the drawShapes argument highlight the data structure recovery.
# Hippurate main effect
lmpScorePlot(resAPCA, effectNames = "Hippurate", color = "Hippurate",
shape = "Hippurate", drawShapes = "ellipse")
# Citrate main effect
lmpScorePlot(resAPCA, effectNames = "Citrate", color = "Citrate",
shape = "Citrate", drawShapes = "ellipse")
# Time main effect
lmpScorePlot(resAPCA, effectNames = "Time", color = "Time", shape = "Time",
drawShapes = "ellipse")
lmpScorePlot(resAPCA, effectNames = "Time", color = "Time", shape = "Time",
drawShapes = "polygon")
lmpScorePlot(resAPCA, effectNames = "Time", color = "Time", shape = "Time",
drawShapes = "segment")
# Interaction term
lmpScorePlot(resAPCA, effectNames = "Hippurate:Time", color = "Hippurate",
shape = "Time", drawShapes = "segment")
lmpScorePlot(resAPCA, effectNames = "Hippurate:Time", color = "Hippurate",
shape = "Time", drawShapes = "polygon")
A scatterplot matrix can also be applied to visualise the relationship between the scores profiles of interest.
lmpScoreScatterPlotM(resAPCA, effectNames = c("Hippurate", "Citrate",
"Time", "Hippurate:Time"), modelAbbrev = TRUE, varname.colorup = "Citrate",
varname.colordown = "Time", varname.pchup = "Hippurate",
varname.pchdown = "Time", title = "APCA scores scatterplot matrix")
Loadings plot
Note that the APCA loadings contain the residuals of the model and differ from the ASCA loadings in that respect.
lmpLoading1dPlot(resAPCA, effectNames = c("Hippurate", "Citrate",
"Time", "Hippurate:Time"), axes = 1)
$Hippurate
$Citrate
$Time
$`Hippurate:Time`
ASCA-E
The ASCA-E method performs PCA on the pure effect matrices then projects the effect matrices augmented with residuals on the PC obtained.
resASCAE <- lmpPcaEffects(resLmpEffectMatrices = resLmpEffectMatrices,
method = "ASCA-E")
The contributions and loadings are identical to the ASCA results.
Scores Plot
For the main effects, all score plots show a clear separation between the different levels of the considered effects on the first PC. This separation of the groups suggests a strong effect of those factors, confirmed by their significance.
lmpScorePlot(resASCAE, effectNames = "Hippurate", color = "Hippurate",
shape = "Hippurate")
lmpScorePlot(resASCAE, effectNames = "Citrate", color = "Citrate",
shape = "Citrate")
lmpScorePlot(resASCAE, effectNames = "Time", color = "Time",
shape = "Time")
Warning in FUN(X[[i]], ...): The variance of PC2 is inferior to 1%. Graph
scaled
lmpScorePlot(resASCAE, effectNames = "Hippurate:Time", color = "Hippurate",
shape = "Time")
Again, the scores profiles can be compared 2 by 2 with ASCA-E.
lmpScoreScatterPlotM(resASCAE, effectNames = c("Hippurate", "Citrate",
"Time", "Hippurate:Time"), modelAbbrev = TRUE, varname.colorup = "Citrate",
varname.colordown = "Time", varname.pchup = "Hippurate",
varname.pchdown = "Time", title = "ASCA-E scores scatterplot matrix")
sessionInfo
sessionInfo()
R Under development (unstable) (2024-10-21 r87258)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB LC_COLLATE=C
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] SummarizedExperiment_1.37.0 Biobase_2.67.0
[3] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
[5] IRanges_2.41.0 S4Vectors_0.45.0
[7] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
[9] matrixStats_1.4.1 car_3.1-3
[11] carData_3.0-5 pander_0.6.5
[13] gridExtra_2.3 limpca_1.3.0
[15] ggplot2_3.5.1 BiocStyle_2.35.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.1 dplyr_1.1.4 farver_2.1.2
[4] fastmap_1.2.0 digest_0.6.37 lifecycle_1.0.4
[7] magrittr_2.0.3 compiler_4.5.0 rlang_1.1.4
[10] sass_0.4.9 tools_4.5.0 utf8_1.2.4
[13] yaml_2.3.10 knitr_1.48 S4Arrays_1.7.0
[16] labeling_0.4.3 DelayedArray_0.33.0 plyr_1.8.9
[19] abind_1.4-8 withr_3.0.2 purrr_1.0.2
[22] grid_4.5.0 fansi_1.0.6 colorspace_2.1-1
[25] scales_1.3.0 iterators_1.0.14 cli_3.6.3
[28] rmarkdown_2.28 crayon_1.5.3 generics_0.1.3
[31] httr_1.4.7 reshape2_1.4.4 cachem_1.1.0
[34] stringr_1.5.1 zlibbioc_1.53.0 parallel_4.5.0
[37] BiocManager_1.30.25 formatR_1.14 XVector_0.47.0
[40] vctrs_0.6.5 Matrix_1.7-1 jsonlite_1.8.9
[43] bookdown_0.41 ggrepel_0.9.6 Formula_1.2-5
[46] foreach_1.5.2 jquerylib_0.1.4 tidyr_1.3.1
[49] glue_1.8.0 codetools_0.2-20 stringi_1.8.4
[52] gtable_0.3.6 UCSC.utils_1.3.0 munsell_0.5.1
[55] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1
[58] GenomeInfoDbData_1.2.13 R6_2.5.1 doParallel_1.0.17
[61] evaluate_1.0.1 tidyverse_2.0.0 lattice_0.22-6
[64] highr_0.11 ggsci_3.2.0 bslib_0.8.0
[67] Rcpp_1.0.13 SparseArray_1.7.0 xfun_0.48
[70] pkgconfig_2.0.3
References
Guisset, Séverine, Manon Martin, and Bernadette Govaerts. 2019.
“Comparison of PARAFASCA, AComDim, and AMOPLS Approaches in the Multivariate GLM Modelling of Multi-Factorial Designs.” Chemometrics and Intelligent Laboratory Systems 184: 44–63. https://doi.org/
https://doi.org/10.1016/j.chemolab.2018.11.006.
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