IsomirDataSeq
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 R2PermutationVecto
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=~group)#>#>pls.ids = isoPLSDA(ids, "group", nperm = 2) cat(paste0("pval:",pls.ids$p.val))#> pval:1cat(paste0("components:",pls.ids$components))#> components:-7.57947253992083 components:-25.3585565000001 components:0.195567502506289 components:13.5423591758866 components:1.61510905023163 components:9.42046263904913 components:-13.0674228461778 components:-4.78896463973935 components:-3.81847501277925 components:0.405139081453592 components:13.2253946082814 components:10.2827695179266 components:9.19292991027107 components:-3.26683994698899 components:-5.99825265983859 components:14.405083119 components:-5.07094449502939 components:2.57649287180775 components:-7.14715807723921 components:8.91744056802121 components:-7.36729558574904 components:-0.0583135167405379 components:0.025273690875176 components:-2.51285436245379 components:1.13738293925962 components:1.33288761471967 components:7.10269041319377 components:-7.34243251982663 components:4.84531251206383 components:-1.54892083215524 components:-11.9360665252263 components:3.49791931757892 components:-1.02908099996298 components:2.19356963786863 components:-4.41559839696736 components:6.29141501081738 components:-2.12025036894569 components:-2.82635176325596 components:-6.22475324422899 components:1.17217868904657 components:0.697330878927742 components:11.4032960844394