Function reference • upbm

Creating PBM Objects Two core classes, PBMExperiment and PBMDesign, are have been constructed for organizing and analyzing PBM data in R. The following set of functions can be used to construct both PBMExperiment and PBMDesign objects from GPR files or data already loaded in R. For more details, see class definitions below.
`gpr2PBMExperiment()`	Load raw PBM scan data as PBMExperiment
`PBMExperiment()`	Create a new PBMExperiment object
`PBMDesign()`	Create a new PBMDesign object
Sample Pre-Processing Before any downstream inference can be performed, individual samples should be pre-processed. A single wrapper function, upbmPreprocess is provided for performing all standard pre-processing steps, as well as individual functions for handling the steps of background subtraction, Cy3 normalization, and within and across replicate normalization. (See Pre-Processing vignette for more details.)
`upbmPreprocess()`	PBM Preprocess
`backgroundSubtract()`	Background subtract intensities
`cy3FitEmpirical()`	Compute Cy3 scaling factors using emprical reference
`cy3FitModel()`	Compute Cy3 scaling factors using regression
`cy3GenerateRef()`	Generate Cy3 empirical reference
`cy3Normalize()`	Perform Cy3 normalization
`spatiallyAdjust()`	Perform spatial adjustment
`normalizeWithinReplicates()`	Normalize within replicates
`normalizeAcrossReplicates()`	Normalize across replicates
Probe/K-mer Summarization Pre-processed and normalized samples can be aggregated across replicates to obtain probe-level and K-mer-level affinity estimates. Additionally, a simple summarization function is provided for computing K-mer summary statistics for individual samples in the absence of replicate data. (See Summarization vignette for more details.)
`probeFit()`	Fit probe models
`kmerFit()`	Fit k-mer probe set models
`summarizeKmers()`	Compute K-mer summary metrics from probe intensities
K-mer-Level Testing Finally, K-mer-level affinity estimates can be used to perform various inference procedures both for individual conditions and for testing across conditions. Three functions to perform different itesting procedures are listed below. (See K-mer-Level Inference vignette for more details.)
`kmerTestAffinity()`	Test for k-mer preferential affinities
`kmerTestContrast()`	Test for k-mer differential affinities
`kmerTestSpecificity()`	Test for k-mer differential specificities
PBM Class Definitions The package defines two new classes, PBMExperiment and PBMDesign which buildon existing Bioconductor infrastructure. (See Class Details vignette for more details.)
`PBMExperiment-class`	PBMExperiment class
`PBMDesign-class`	PBMDesign class
PBM Class Methods and Setters The two PBM classes include special slots specifying probe filtering and trimming criteria. Functions are provided for accessing and applying these filtering and trimming procedures on both PBMExperiment and PBMDesign objects. These and additional slots can be accessed and modified using getter and setting functions.
`pbmFilterProbes()`	Filter PBMExperiment and PBMDesign objects
`pbmTrimProbes()`	Trim probe sequences
`probeFilter()` `probeTrim()` `probeCols()` `probeFilter<-`() `probeTrim<-`() `design<-`(<PBMDesign>,<data.frame>) `design(<PBMDesign>)`	PBM slot accessors and setters
`PBMDesign<-`()	Set design in PBMExperiment object
Helper Functions The package also includes several helper functions for working with PBM data. This includes, most importantly, functions for extracting assay data stored in PBMExperiment objects as tidy tables (see Tidy Data vignette for more details).
`tidy(<PBMExperiment>)`	Tidy PBMExperiment object
`tidy(<SummarizedExperiment>)`	Tidy SummarizedExperiment object
`mapKmers()`	Map K-mer Motifs to PBM Probe Sequences
`uniqueKmers()`	Generate Unique K-mer Sequences

Reference

Creating PBM Objects

Sample Pre-Processing

Probe/K-mer Summarization

K-mer-Level Testing

PBM Class Definitions

PBM Class Methods and Setters

Helper Functions