Package index • XploR

All functions

AIbinSmooth(): Smooth AI Bin Values by Downsampling

AddQualTag(): Assign Quality Tag to a Segment Based on maf GMM Metrics

AnnotateGene(): Annotate Segments with Overlapping Genes

AnnotateSegments(): Annotate CNV Segments with Cytoband and Gene Information

AssignPriors(): Assign Priors to Major/Minor Allele Combinations Based on Biological Difficulty

BafQC(): Summarise BAF QC from CNV Segment Annotation

BinMaf(): Bin MAF Data and Estimate MAF Metrics per Bin

CRCorrectPurity(): Calculate CNF based on purity and diploid coverage size factor

CalMedian(): Calculate Median Coverage for a Genomic Region

CalSM(): Calculate Segment Mean Adjusted by Scale Factor

CalSegmentLikelihood(): Calculate Marginal Likelihood for a Single Segment

CalbaselineCov(): Calculate Baseline Coverage

Calccf(): Calculate Cancer Cell Fraction (CCF)

CalculateGaps(): Calculate Gaps Between Segment and Cytoband-Defined Chromosome Landmarks

CallMerge(): Merge Segments or AI Rows by Chromosome

CallWTModel(): Assign Copy Number Call With Model (Major/Minor)

Callikelihood(): Calculate Likelihood for Each \(\mu\) and \(\rho\) Combination

CallwoModel(): Assign Copy Number Call Without Model

CcfLOH(): Calculate Cancer Cell Fraction (CCF) from Allelic Imbalance (LOH)

CheckGender(): Check and Adjust Gender in GATK Segmentation Data

Checkmode(): Check and Set Mode-Specific Parameters for CNV Calling

ChooseNbins(): Choose Optimal Number of Depth Strata (Bins) for Panel of Normals

CleanHomalt(): Clean Homozygous AI Bins Based on LOH Segments

ClusterAdjacent(): Cluster Adjacent Values Based on a Threshold

ClusterModels(): Cluster and Select the Best Models Based on Likelihood and Copy Number Distance

CorrectBias(): Correct MAF Bias in Segments Using Panel of Normal Reference

CorrectGender(): Correct Copy Ratio File if GATK and Pipeline Gender Disagree

CorrectPurity(): Correct CNF and MAF According to Purity

CovBin(): Bin and Smooth Copy Number Data from Coverage File

EstimateCovSF(): Estimate Coverage Scale Factor for a Chromosome or Region

EstimateK(): Estimate Beta-Binomial K Parameter for Each Segment

EstimateMAFbyGMM(): Estimate MAF Cluster Mean and Weight Using Gaussian Mixture Modeling

EstimateMinPurity(): Estimate Minimum Tumor Purity from MAF Data

EstimatePurityCov(): Estimate Tumor Purity from Coverage and Segmentation Data

EstimateTheta(): Estimate Beta-Binomial Over-Dispersion Parameter (Theta)

EstimateVariance(): Estimate Standard Deviation of a Numeric Vector

ExtractCall(): Extract Final Calls for a Given (mu, rho) Model

FindCytoband(): Find Cytoband Name for Genomic Position

FindTier1Models(): Find the "Elbow" Point in a Ranked Vector (Tier 1 Model Selection)

FixsegmentMean(): Fix Segment Mean Based on Gender Discrepancy

GenerateCombinations(): Generate All Major/Minor Allele Combinations for a Given Total Copy Number

GenerateISCN(): Generate Cytoband-Based Annotation String for a CNV Segment

Groupvalues(): Group Values by Change Points in the Mean

KeepWhitelistAI(): Keep Only AI Bins in Whitelisted Regions

KeepWhitelistCov(): Keep Only Coverage Bins in Whitelisted Regions

MergeAICheck(): Check Whether Two AI Segments Should Be Merged

MergeAIRow(): Merge Adjacent AI Segments Based on Similarity Criteria

MergeSegCheck(): Check Whether Two Segments Should Be Merged (Copy Number)

MergeSegRow(): Merge Adjacent Copy Number Segments Based on Similarity Criteria

ModelSource(): Determine Model Source Based on MAF and Coverage Information

PONAIprocess(): Process Panel of Normal AI Files and Estimate Beta-binomial Dispersion based on the depth

ParseParm(): Parse and Select Purity and Scale Factor Parameters from Model Grid

PlotCov(): Multi-panel Plot of Copy Number, BAF, Clonality, and Quality Across Chromosomes

PlotCovDisCN(): Plot Coverage Distance to Integer Copy Number vs. Purity

PlotModel(): Plot Model Total Log-Likelihoods as a Heatmap

PlotModelDot(): Plot Model Total Log-Likelihoods as a Dot Plot

PlotModelcluster(): Plot all CNV calls under each Tier1 models

PonProcess(): Generate Whitelist, Blacklist, and Cytoband-Detectable Edge Files from GATK PoN HDF5

ReadAI(): Read and Standardize Allelic Imbalance Data

ReadPonAI(): Read and Combine Panel of Normal AI Files

RefineCalls(): Refine Segment Calls According to the Final Model

RefineCallsSecond(): Second Refinement of Segment Calls for Coverage/AI Profile Mismatches

RefineTier1Models(): Refine Tier 1 Models and Their Segment Calls

RerunCNV(): Rerun CNV Calling with User-Defined Purity and Size Factor or Diploid Region

RoundAI(): Round BAF Values into 20 Bins

RoundCN(): Round Copy Number to Integer Based on Call and Gender

RunAIsegmentation(): Run AI Segmentation Workflow

RunCallikelihood(): Run Likelihood Calculation Across All Purity and Scale Factor Combinations

RunExamplePipeline(): Run XploR Example Pipeline

RunModelLikelihood(): Run Model Likelihood and Copy Number Calling Workflow

RunPlotCNV(): Plot CNV Profile (Copy Number, BAF, Clonality, Quality)

Runcheckgender(): Run automatic gender detection

SearchBreakpoint(): Refine Breakpoints Within a Segment Using MAF Data

SegmentMeanToOriCov(): Convert Segment Mean to Original Coverage

SelectCallpersegment(): Select the Most Likely Call per Segment

SelectFinalModel(): Select and Refine the Final CNV Model and Segment Calls

SelectModelByDis(): Select Model by Distance to Integer Copy Number

SmoothAI(): Smooth AI Bins by Window Size and Downsampling

SummarizeMapQC(): Summarize QC Metrics for a Sample from a DRAGEN run.