IsomirDataSeq
isoPLSDA.Rd
Use PLS-DA method with the normalized count data to detect the most important features (miRNAs/isomiRs) that explain better the group of samples given by the experimental design. It is a supervised clustering method with permutations to calculate the significance of the analysis.
isoPLSDA(ids, group, validation = NULL, learn = NULL, test = NULL, tol = 0.001, nperm = 400, refinment = FALSE, vip = 1.2)
ids | Object of class |
---|---|
group | Column name in |
validation | Type of validation, either NULL or "learntest". Default NULL. |
learn | Optional vector of indexes for a learn-set. Only used when validation="learntest". Default NULL. |
test | Optional vector of indices for a test-set. Only used when validation="learntest". Default NULL |
tol | Tolerance value based on maximum change of cumulative R-squared coefficient for each additional PLS component. Default tol=0.001. |
nperm | Number of permutations to compute the PLD-DA p-value based on R2 magnitude. Default nperm=400. |
refinment | Logical indicating whether a refined model, based on filtering out variables with low VIP values. |
vip | Variance Importance in Projection threshold value when a refinement process is considered. Default vip=1.2 . |
A base::list with the following elements: R2Matrix
(R-squared coefficients of the PLS model),
components
(of the PLS, similar to PCs in a PCA),
vip
(most important isomiRs/miRNAs),
group
(classification of the samples),
p.value
and R2PermutationVector
obtained by the permutations.
If the option refinment
is set to TRUE, then the following
elements will appear:
R2RefinedMatrix
and componentsRefinedModel
(R-squared coefficients
of the PLS model only using the most important miRNAs/isomiRs). As well,
p.valRefined
and R2RefinedPermutationVector
with p-value
and R2 of the
permutations where samples were randomized. And finally,
p.valRefinedFixed
and R2RefinedFixedPermutationVector
with
p-value and R2 of the
permutations where miRNAs/isomiRs were randomized.
Partial Least Squares Discriminant Analysis (PLS-DA) is a technique specifically appropriate for analysis of high dimensionality data sets and multicollinearity (Perez-Enciso, 2013). PLS-DA is a supervised method (i.e. makes use of class labels) with the aim to provide a dimension reduction strategy in a situation where we want to relate a binary response variable (in our case young or old status) to a set of predictor variables. Dimensionality reduction procedure is based on orthogonal transformations of the original variables (miRNAs/isomiRs) into a set of linearly uncorrelated latent variables (usually termed as components) such that maximizes the separation between the different classes in the first few components (Xia, 2011). We used sum of squares captured by the model (R2) as a goodness of fit measure.
We implemented this method using the
DiscriMiner::DiscriMiner-package into isoPLSDA()
function.
The output
p-value of this function will tell about the statistical
significant of the group separation using miRNA/isomiR expression data.
Read more about the parameters related to the PLS-DA directly from
DiscriMiner::plsDA()
function.
Perez-Enciso, Miguel and Tenenhaus, Michel. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Human Genetics. 2003.
Xia, Jianguo and Wishart, David S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols. 2011.
data(mirData) # Only miRNAs with > 10 reads in all samples. ids <- isoCounts(mirData, minc=10, mins=6) ids <- isoNorm(ids, formula=~condition)#>pls.ids = isoPLSDA(ids, "condition", nperm = 2) cat(paste0("pval:",pls.ids$p.val))#> pval:0.5cat(paste0("components:",pls.ids$components))#> components:14.3066596177823 components:-11.7693157710985 components:14.4142576598742 components:6.91586361820465 components:-5.94565855752119 components:7.83823453785904 components:6.37966219439574 components:0.770475923930374 components:-3.48189388212237 components:-2.58942324453082 components:-6.46917735612719 components:-6.47670198370602 components:-8.74767309534566 components:-5.14530966159451 components:0.0106305758827876 components:16.8857520269642 components:0.973708513194808 components:-2.28606297388448 components:6.91222827694606 components:-2.76376982501066 components:-3.37470489249501 components:32.0166670455401 components:-6.71678158379987 components:-7.7512381140994 components:-12.4101001782535 components:-9.53756056665991 components:-3.59033899562634 components:-8.36842930869871 components:0.820624147219758 components:1.51484239096593 components:-3.40291418948103 components:3.67521480115671 components:4.21557888975052 components:-2.77960349911796 components:1.95739528645296 components:-1.2707164697599 components:5.24843961104998 components:4.80080523102723 components:4.8854281287772 components:0.0463860216465434 components:-7.28795535477177 components:-12.4235249949162 components:-7.29467341278599 components:-8.27649379811714 components:-9.45333734441245 components:6.61283427316846 components:5.61779362824376 components:8.0411408840505 components:7.51143466048207 components:4.30709971090002 components:-6.54173107498235 components:-2.82333600010906 components:4.38827637645674 components:-3.97939889128389 components:0.313488016050153 components:1.57690297233916 components:4.48982190579641 components:0.269206441714625 components:-4.79707522438753 components:-1.68513893453166 components:2.72459729931384 components:1.11188636422498 components:-0.872068707808226 components:-1.78913372278832 components:0.523034552958386 components:8.85470563350167 components:-4.49577918178797 components:-8.47439890848983 components:1.04273069806964 components:3.09761178421398