## ----echo=F------------------------------------------------------------------- suppressPackageStartupMessages({ suppressWarnings({ library(DiscoRhythm) }) }) indata <- discoGetSimu() knitr::kable(head(indata[,1:6]),format = "markdown") # Inspect the data ## ----echo=F------------------------------------------------------------------- kableExtra::column_spec(knitr::kable(head(indata[,1:6])), 1, background = "#FDB813") ## ----echo=F------------------------------------------------------------------- kableExtra::column_spec(knitr::kable(head(indata[,1:6])), 2:6, background = "#FDB813") ## ----echo=F------------------------------------------------------------------- kableExtra::column_spec( kableExtra::row_spec( knitr::kable(head(indata[,1:6])), 0, background = "#FDB813"), 1, background = "inherit") ## ----echo=F,message=FALSE----------------------------------------------------- knitr::kable(head(SummarizedExperiment::colData( discoDFtoSE(indata) )), format = "markdown") ## ----interface, echo=F, fig.cap="Screenshot of the initial DiscoRhythm landing page."---- knitr::include_graphics("IntroductionSS.jpg") ## ----echo=FALSE--------------------------------------------------------------- # Figure caption template figcap="Screenshot of the '%s' section of the DiscoRhythm interface." ## ----selectData, echo=FALSE, fig.cap=sprintf(figcap,'Select Data')------------ knitr::include_graphics("selectDataSS.jpg") ## ----corQC, echo=FALSE, fig.cap=sprintf(figcap,'Inter-sample Correlation')---- knitr::include_graphics("IntersampleCorrelationSS.jpg") ## ----pcaQC, echo=F, fig.cap=sprintf(figcap,'PCA')----------------------------- knitr::include_graphics("PCASS.jpg") ## ----filteringSummary, echo=F, fig.cap=sprintf(figcap,'Filtering Summary')---- knitr::include_graphics("FilteringSummarySS.jpg") ## ----repAnalysis, echo=F, fig.cap=sprintf(figcap,'Row Selection')------------- knitr::include_graphics("RowSelectionSS.jpg") ## ----domPer, echo=F, fig.cap=sprintf(figcap,'Period Detection')--------------- knitr::include_graphics("PeriodDetectionSS.jpg") ## ----PCfits, echo=F, fig.cap=sprintf(figcap,'PC Cosinor Fits')---------------- knitr::include_graphics("PCfitsSS.jpg") ## ----detOsc, echo=F, fig.cap=sprintf(figcap,'Oscillation Detection (Preview)')---- knitr::include_graphics("OscillationDetectionPrevSS.jpg") ## ----echo=FALSE--------------------------------------------------------------- mat <- t(DiscoRhythm::discoODAexclusionMatrix) knitr::kable(mat,format = "markdown") ## ----detOscResults, echo=F, fig.cap=sprintf(figcap,'Oscillation Detection')---- knitr::include_graphics("OscillationDetectionSS.jpg") ## ----------------------------------------------------------------------------- library(DiscoRhythm) indata <- discoGetSimu() knitr::kable(head(indata[,1:6]), format = "markdown") # Inspect the data ## ----------------------------------------------------------------------------- se <- discoDFtoSE(indata) ## ----------------------------------------------------------------------------- selectDataSE <- discoCheckInput(se) ## ----message=FALSE------------------------------------------------------------ library(SummarizedExperiment) Metadata <- colData(selectDataSE) knitr::kable(discoDesignSummary(Metadata),format = "markdown") ## ----------------------------------------------------------------------------- CorRes <- discoInterCorOutliers(selectDataSE, cor_method="pearson", threshold=3, thresh_type="sd") ## ----------------------------------------------------------------------------- PCAres <- discoPCAoutliers(selectDataSE, threshold=3, scale=TRUE, pcToCut = c("PC1","PC2","PC3","PC4")) ## ----------------------------------------------------------------------------- discoPCAres <- discoPCA(selectDataSE) ## ----------------------------------------------------------------------------- FilteredSE <- selectDataSE[,!PCAres$outliers & !CorRes$outliers] DT::datatable(as.data.frame( colData(selectDataSE)[PCAres$outliers | CorRes$outliers,] )) knitr::kable(discoDesignSummary(colData(FilteredSE)),format = "markdown") ## ----------------------------------------------------------------------------- ANOVAres <- discoRepAnalysis(FilteredSE, aov_method="Equal Variance", aov_pcut=0.05, aov_Fcut=1, avg_method="Median") FinalSE <- ANOVAres$se ## ----------------------------------------------------------------------------- PeriodRes <- discoPeriodDetection(FinalSE, timeType="linear", main_per=24) ## ----------------------------------------------------------------------------- OVpca <- discoPCA(FinalSE) OVpcaSE <- discoDFtoSE(data.frame("PC"=1:ncol(OVpca$x),t(OVpca$x)), colData(FinalSE)) knitr::kable(discoODAs(OVpcaSE,period = 24,method = "CS")$CS, format = "markdown") ## ----------------------------------------------------------------------------- discoODAres <- discoODAs(FinalSE, period=24, method="CS", ncores=1, circular_t=FALSE) ## ----echo=F------------------------------------------------------------------- batchscript=system.file("", "DiscoRhythm_batch.R", package = "DiscoRhythm", mustWork = TRUE) ## ----code = readLines(batchscript), eval= FALSE------------------------------- # ###################################################################### # # Intended for use by discoBatch or through the DiscoRhythm_report.Rmd # # Includes all R code for the DiscoRhythm data processing # # Expects all arguments to discoBatch in the environment # ##################################################################### # # library(DiscoRhythm) # # # Preprocess inputs # selectDataSE <- discoCheckInput(discoDFtoSE(indata)) # # # Intersample correlations # CorRes <- discoInterCorOutliers(selectDataSE,cor_method, # cor_threshold,cor_threshType) # # # PCA for outlier detection # PCAres <- discoPCAoutliers(selectDataSE,pca_threshold,pca_scale,pca_pcToCut) # PCAresAfter <- discoPCA(selectDataSE[,!PCAres$outliers]) # # # Removing the outliers from the main data.frame and metadata data.frame # FilteredSE <- selectDataSE[,!PCAres$outliers & !CorRes$outliers] # # # Running ANOVA and merging replicates # ANOVAres <- discoRepAnalysis(FilteredSE, aov_method, # aov_pcut, aov_Fcut, avg_method) # # # Data to be used for Period Detection and Oscillation Detection # FinalSE <- ANOVAres$se # # # Perform PCA on the final dataset # OVpca <- discoPCA(FinalSE) # # # Period Detection # PeriodRes <- discoPeriodDetection(FinalSE, # timeType, # main_per) # # # Oscillation Detection # discoODAres <- discoODAs(FinalSE, # circular_t = timeType=="circular", # period=osc_period, # osc_method,ncores) ## ----------------------------------------------------------------------------- sessionInfo()