Function reference • grandR

Fundamentals

Loading data

Functions to read data from files or urls

ClassifyGenes(): Build the type column for the gene info table.

Design: A list of predefined names for design vectors

DesignSemantics(): Build the design semantics list

MakeColdata(): Extract an annotation table from a formatted names vector

ReadCounts(): Read a count table

ReadFeatureCounts(): Read featureCounts

ReadGRAND(): Read the output of GRAND-SLAM 2.0 into a grandR object.

ReadGRAND3(): Read the output of GRAND-SLAM 3.0 into a grandR object.

ReadNewTotal(): Read sparse new/total matrices

Semantics.concentration(): Semantics for concentration columns

Semantics.time(): Semantics for time columns

as.Seurat.grandR(): Create Seurat object from a grandR object

Data preprocessing

Functions to preprocess data

ComputeAbsolute(): Compute absolute expression using ERCC spike ins

ComputeNtrPosteriorQuantile() ComputeNtrCI() ComputeNtrPosteriorLower() ComputeNtrPosteriorUpper(): Compute NTR quantiles

FilterGenes(): Filter genes

Normalize() NormalizeFPKM() NormalizeRPM() NormalizeTPM(): Normalization

NormalizeBaseline(): Normalization to a baseline

Scale(): Scale data

Interacting with grandR objects

Retrieve information from grandR objects

Analyses() AddAnalysis() DropAnalysis(): Analysis table functions

Coldata() `Coldata<-`(): Get the column annotation table or add additional columns to it

Condition() `Condition<-`(): Get or set the conditions in the column annotation table.

DefaultSlot() `DefaultSlot<-`(): Get or set the default slot for a grandR object.

GeneInfo() `GeneInfo<-`(): Get the gene annotation table or add additional columns to it

Genes() Columns(): Gene and sample (or cell) names

Plots() AddGenePlot() AddGlobalPlot() PlotGene() PlotGlobal() DropPlots(): Stored plot functions

Slots() DropSlot() AddSlot(): Slot functions

UpdateSymbols(): Update symbols using biomaRt

grandR() Title() IsSparse() dim(<grandR>) is.grandR() dimnames(<grandR>) print(<grandR>) Metadata() subset(<grandR>) split(<grandR>) RenameColumns() SwapColumns() merge(<grandR>): Create a grandR object and retrieve basic information

Data retrieval

Functions and helper functions to retrieve data from grandR objects

ComputeColumnStatistics(): Compute statistics for all columns (i.e. samples or cells)

ComputeExpressionPercentage(): Expression percentage computation

ComputePseudoNtr(): Compute pseudo NTRs from two count matrices

ComputeTotalExpression(): Total expression computation

CreateConvolutionTable(): Create Convolution Table from a Seurat object

CreatePseudobulkTable(): Create Pseudobulk Table from a Seurat object

GetAnalysisTable(): Obtain a table of analysis results values

GetData(): Obtain a tidy table of values for a gene or a small set of genes

GetMatrix(): Obtain a genes x values table as a large matrix

GetSummarizeMatrix(): Create a summarize matrix

GetTable(): Obtain a genes x values table

PoolColumns(): Pool reads across columns

SaveNtrSlot(): Copy the NTR slot and save under new name

Summarize(): Summarize a data matrix

UseNtrSlot(): Copy the NTR slot and save under new name

Analyses

Differential expression

Analyze differential expression (snapshot experiments)

DESeq2BIC(): Compute the Bayesian information criterion (BIC)

EstimateRegulation(): Estimate regulation from snapshot experiments

GetContrasts(): Create a contrast matrix

GetPairContrasts(): Create a contrast matrix for two given conditions

GetSignificantGenes(): Significant genes

LFC(): Estimation of log2 fold changes

LikelihoodRatioTest(): Compute a likelihood ratio test.

Pairwise(): Log2 fold changes and Wald tests for differential expression

PairwiseDESeq2(): Perform Wald tests for differential expression

Progressive labeling timecourses

Infer kinetic parameters from progressive labeling timecourses

ComputeNonConstantParam() EvaluateNonConstantParam(): Compute and evaluate functions for non constant rates

FitKinetics(): Fit kinetic models to all genes.

FitKineticsGeneLeastSquares(): Fit a kinetic model according to non-linear least squares.

FitKineticsGeneLogSpaceLinear(): Fit a kinetic model using a linear model.

FitKineticsGeneNtr(): Fit a kinetic model using the degradation rate transformed NTR posterior distribution.

FitKineticsPulseR(): Fit kinetics using pulseR

PlotSimulation(): Plot simulated data

SimulateKinetics(): Simulate the kinetics of old and new RNA for given parameters.

f.nonconst(): Function to compute the abundance of new or old RNA at time t for non-constant rates.

f.nonconst.linear(): Function to compute the abundance of new or old RNA at time t for non-constant rates.

f.old.equi() f.old.nonequi() f.new(): Functions to compute the abundance of new or old RNA at time t.

Modeling snapshot data

Infer parameters from snapshot data with or without steady state assumptions

ComputeSteadyStateHalfLives(): Steady state half-lives for each sample

FindReferences(): Obtain reference columns (samples or cells) for all columns (samples or cells) in the data set

FitKineticsGeneSnapshot(): Compute the posterior distributions of RNA synthesis and degradation for a particular gene

FitKineticsSnapshot(): Fits RNA kinetics from snapshot experiments

TransformSnapshot(): Estimate parameters for a one-shot experiment.

Gene set tests

Functions to do GSEA,ORA,etc.

AnalyzeGeneSets(): Gene set analysis

ListGeneSets(): List available gene sets

Visualization

Gene plots

Functions to plot data for a single gene

PlotGeneGroupsBars(): Plot gene values as bars

PlotGeneGroupsPoints(): Plot gene groups as points

PlotGeneOldVsNew(): Gene plot comparing old vs new RNA

PlotGeneProgressiveTimecourse(): Plot progressive labeling timecourses

PlotGeneSnapshotTimecourse(): Gene plot for snapshot timecourse data

PlotGeneTotalVsNtr(): Gene plot comparing total RNA vs the NTR

Global plots

Functions to plot data summarizing the whole data set

FormatCorrelation(): Formatting function for correlations

MAPlot(): Make an MA plot

PlotAnalyses(): Convenience function to make the same type of plot for multple analyses.

PlotHeatmap(): Create heatmaps from grandR objects

PlotPCA(): Make a PCA plot

PlotScatter(): Make a scatter plot

PlotTypeDistribution(): Plot the distribution of gene types

Transform.no() Transform.Z() Transform.VST() Transform.logFC(): Transformations for PlotHeatmap

VulcanoPlot(): Make a Vulcano plot

Web interface

Functions related to the shiny web-interface

ServeGrandR(): Serve a shiny web interface

QC

4sU dropout checks

Functions to check for 4sU dropout

ComputeSummaryStatistics(): Compute summary statistics

Findno4sUPairs(): Find equivalent no4sU samples for 4sU samples

Correct4sUDropoutHLFactor() Correct4sUDropoutHLSpline(): Correct for 4sU dropout

Plot4sUDropoutRankAll() Plot4sUDropoutAll() Plot4sUDropoutDeferAll() Plot4sUDropoutRankDeferAll() Plot4sUDropoutRank() Plot4sUDropout(): Perform 4sU dropout tests

Estimate4sUDropoutPercentage() Estimate4sUDropoutPercentageForSample(): Estimate 4sU dropout percentages

Temporal recalibration

Functions for recalibration of labeling times (to get effective labeling from nominal labeling times)

CalibrateEffectiveLabelingTimeKineticFit(): Uses the kinetic model to calibrate the effective labeling time.

CalibrateEffectiveLabelingTimeMatchHalflives(): Calibrate the effective labeling time by matching half-lives to a .reference

GRAND3 quality control plots

Generate quality control plot from the statistics generated by GRAND3

CreatePdfs() CreatePdfsParameters() CreatePdfsComparison() CreatePdfsProfiles(): Convencience methods for creating QC pdfs

GetDiagnosticParameters(): Describe parameters relevant to diagnostics

PlotConversionFreq(): Diagnostic plot for conversion frequencies

PlotMismatchPositionForSample(): Diagnostic plot for mismatch position for columns (by sample)

PlotMismatchPositionForType(): Diagnostic plot for mismatch position for columns (by mismatch type)

PlotModelCompareConv(): Diagnostic plot for estimated models (global conversion rate)

PlotModelCompareErr(): Diagnostic plot for estimated models (global error rate)

PlotModelCompareErrPrior(): Diagnostic plot for estimated models (global error rate)

PlotModelCompareLL(): Diagnostic plot for estimated models (log likelihoods)

PlotModelCompareNtr(): Diagnostic plot for estimated models (global NTR)

PlotModelConv(): Diagnostic plot for estimated models (global conversion rate)

PlotModelErr(): Diagnostic plot for estimated models (global error rate)

PlotModelLabelTimeCourse(): Diagnostic plot for estimated models (4sU increase)

PlotModelNtr(): Diagnostic plot for estimated models (global NTR)

PlotModelShape(): Diagnostic plot for estimated models (global shape parameter)

PlotProfileLikelihood(): Diagnostic plot for estimated models (global error rate)

Misc

Read simulation

Functions for in-silico simulation of nucleotide conversion data

SimulateReadsForSample(): Simulate metabolic labeling - nucleotide conversion RNA-seq data.

SimulateTimeCourse(): Simulate a complete time course of metabolic labeling - nucleotide conversion RNA-seq data.

SimulateTimeCourseNonConstant(): Simulate a complete time course of metabolic labeling - nucleotide conversion RNA-seq data.

Helper functions

ApplyContrasts(): Apply a function over contrasts

Defer(): Defer calling a function

IsParallel(): Checks for parallel execution

RotatateAxisLabels(): Rotate x axis labels

SetParallel(): Set up parallel execution

ToIndex(): Obtain the indices of the given genes

check.analysis() check.slot() check.mode.slot(): Internal functions to check for a valid analysis or slot names.

data.apply(): Internal function to apply functions to all slots etc.

density2d(): Density estimation in 2d

estimate.dispersion(): Estimate dispersion parameters for a count matrix using DESeq2

get.mode.slot(): Internal functions to parse mode.slot strings

psapply() plapply(): Parallel (s/l)apply

structure2vector() kinetics2vector(): Convert a structure into a vector