--- title: "Accessing preeclampsia DNAm models with eoPredData" author: - name: Iciar Fernandez Boyano affiliation: University of British Columbia email: iciarfernandez@outlook.com - name: Victor Yuan affiliation: University of British Columbia email: victor.2wy@gmail.com package: eoPredData output: BiocStyle::html_document: self_contained: yes toc: true toc_float: true toc_depth: 2 code_folding: show abstract: | Access pre-trained preeclampsia models from eoPredData + ExperimentHub date: "`r doc_date()`" vignette: | %\VignetteIndexEntry{eoPredData} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r vignetteSetup, echo=FALSE, message=FALSE, warning = FALSE} ## Bib setup library(RefManageR) bib <- c(eoPredData = citation("eoPredData")[1]) ``` # How to access eoPredData with Bioconductor ## Citing `eoPredData` We hope that `r Biocpkg("eoPredData")` will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you! ```{r "citation"} ## Citation info citation("eoPredData") ``` # Quick start to using to `eoPredData` Install the R packages ExperimentHub: ```{r, eval = FALSE} if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("ExperimentHub") BiocManager::install("mixOmics") ``` There are 2 objects associated with eoPredData: **EHEH8090** Pre-trained model object using `mixOmics`. This model object can be used to create predictions on DNA methylation profiles collected from placental samples. Details on training and validation are described in `r Citep(bib[["eoPredData"]])`. **EHEH8403** Placental DNA methylation test data (49 samples, 452 453 CpGs), used to demonstrate prediction of preeclampsia status using eoPred model. A `matrix` [cpg x sample]. ```{r "start", message=FALSE, eval=FALSE} library(ExperimentHub) eh <- ExperimentHub() query(eh, "eoPredData") library(mixOmics) # model object eoPredModel <- eh[['EH8090']] # test object x_test <- eh[['EH8403']] dim(x_test) # 452,453 by 49 x_test <- x_test[rownames(x_test) %in% colnames(eoPredModel$X),] dim(x_test) # 341,281 by 49 # code to predict on x_test predictions <- predict(eoPredModel, t(x_test), dist = "max.dist") ``` # Reproducibility `R` session information. ```{r reproduce3, echo=FALSE} ## Session info sessionInfo() ``` # Bibliography ```{r vignetteBiblio, results="asis", echo=FALSE, warning=FALSE, message=FALSE} RefManageR::PrintBibliography( bib, .opts = list(hyperlink = "to.doc", style = "html")) ```