## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----install_radiogx, eval=FALSE, message=FALSE------------------------------- # BiocManager::install('RadioGx', version='devel') ## ----load_package, message=FALSE---------------------------------------------- library(RadioGx) ## ----radioset, echo=FALSE, fig.wide=TRUE, fig.cap = "**RadioSet class diagram**. Objects comprising a `RadioSet` are enclosed in boxes. First box indicates type and name of each object. Second box indicates the structure of an object or class. Third box shows accessor methods from `RadioGx` for that specific object. '=>' represents return and specifies what is returned from that item or method."---- knitr::include_graphics('./RadioGxClassDiagram.png') ## ----available_rsets, evel=FALSE, message=FALSE------------------------------- RSets <- availableRSets() ## ----download_rset, eval=FALSE------------------------------------------------ # Cleveland <- downloadRSet('Cleveland', saveDir='.') ## ----printing_a_radioset, hide=TRUE------------------------------------------- data(clevelandSmall) clevelandSmall ## ----radiation_info_accessor-------------------------------------------------- # Get the radiation info data.frame from an RSet radInf <- radiationInfo(clevelandSmall) ## ----print_radinf, fig.wide = TRUE-------------------------------------------- knitr::kable(radInf) ## ----radiation_types_accessor------------------------------------------------- radTypes <- radiationTypes(clevelandSmall) radTypes ## ----molecular_data_slot_accessors-------------------------------------------- # Get the list (equivalent to @molecularProfiles, except that it is robust to changes in RSet structure str(molecularProfilesSlot(clevelandSmall), max.level=2) # Get the names from the list mDataNames(clevelandSmall) ## ----sample_meta_data--------------------------------------------------------- # Get sample metadata phenoInf <- phenoInfo(clevelandSmall, 'cnv') ## ----print_sample_meta_data, echo=FALSE, fig.small=TRUE----------------------- knitr::kable(phenoInf[, 1:5], row.names=FALSE) ## ----feature_meta_data-------------------------------------------------------- # Get feature metadata featInfo <- featureInfo(clevelandSmall, 'rna') ## ----print_feature_meta_data, echo=FALSE, align="center"---------------------- knitr::kable(featInfo[1:3, 1:5], row.names=FALSE) ## ----molecular_data, fig.wide=TRUE-------------------------------------------- # Access the moleclar feature data mProf <- molecularProfiles(clevelandSmall, 'rnaseq') ## ----print_molecular_profile_data, echo=FALSE--------------------------------- knitr::kable(mProf[1:3, 1:5]) ## ----response_data_accessors-------------------------------------------------- # Get sensitivity slot sens <- sensitivitySlot(clevelandSmall) ## ----------------------------------------------------------------------------- # Get sensitivity raw data sensRaw <- sensitivityRaw(clevelandSmall) ## ----------------------------------------------------------------------------- # Get sensitivity profiles sensProf <- sensitivityProfiles(clevelandSmall) ## ----------------------------------------------------------------------------- # Get sensitivity info sensInfo <- sensitivityInfo(clevelandSmall) ## ----model_fit---------------------------------------------------------------- # Extract raw sensitvity data from the RadioSet sensRaw <- sensitivityRaw(clevelandSmall) ## ----structure_sensitivity_raw------------------------------------------------ str(sensRaw) ## ----cellline_names----------------------------------------------------------- # Find a cancer cell-line of interest head(sensitivityInfo(clevelandSmall)$cellid) ## ----selecting_cancer_cell_line----------------------------------------------- cancerCellLine <- sensitivityInfo(clevelandSmall)$cellid[1] print(cancerCellLine) ## ----extracting_dose_response_data-------------------------------------------- # Get the radiation doses and associated survival data from clevelandSmall radiationDoses <- sensRaw[1, , 'Dose'] survivalFractions <- sensRaw[1, , 'Viability'] ## ----fitting_lq_model--------------------------------------------------------- LQmodel <- linearQuadraticModel(D=radiationDoses, SF=survivalFractions) LQmodel ## ----metrics_computed_from_fit_parameters------------------------------------- survFracAfter2Units <- computeSF2(pars=LQmodel) print(survFracAfter2Units) dose10PercentSurv <- computeD10(pars=LQmodel) print(dose10PercentSurv) ## ----metrics_computed_from_dose_response_data--------------------------------- areaUnderDoseRespCurve <- RadioGx::computeAUC(D=radiationDoses, pars=LQmodel, lower=0, upper=1) print(areaUnderDoseRespCurve) ## ----plotting_rad_dose_resp, fig.small=TRUE----------------------------------- doseResponseCurve( Ds=list("Experiment 1" = c(0, 2, 4, 6)), SFs=list("Experiment 1" = c(1,.6,.4,.2)), plot.type="Both" ) ## ----plotting_rad_dose_resp_rSet, fig.small=TRUE------------------------------ doseResponseCurve( rSets=list(clevelandSmall), cellline=cellInfo(clevelandSmall)$cellid[5] ) ## ----summarize_sensitivity---------------------------------------------------- sensSummary <- summarizeSensitivityProfiles(clevelandSmall) ## ----show_summarized_sensivitiy----------------------------------------------- sensSummary[, 1:3] ## ----summarize_molecular_data------------------------------------------------- mprofSummary <- summarizeMolecularProfiles(clevelandSmall, mDataType='rna', summary.stat='median', fill.missing=FALSE) ## ----show_summarized_sensitivity---------------------------------------------- mprofSummary ## ----compute_molecular_signature---------------------------------------------- radSensSig <- radSensitivitySig(clevelandSmall, mDataType='rna', features=fNames(clevelandSmall, 'rna')[2:5], nthread=1) ## ----exploring_molecular_signature-------------------------------------------- radSensSig@.Data