Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets
Efficiently average replicates of nucleotide recoding data and regular...
The 'bakR' package.
bakR Data object helper function for users
Estimating kinetic parameters from nucleotide recoding RNA-seq data
bakRFnData object helper function for users
Curate data in bakRData object for statistical modeling
Correcting for metabolic labeling induced RNA dropout
Construct heatmap for non-steady state (NSS) analysis with improved me...
Efficiently analyze nucleotide recoding data
Curate data in bakRFnData object for statistical modeling
Creating PCA plots with logit(fn) estimates
Creating PCA plots with logit(fn) estimates
Prep GRAND-SLAM output for bakRFnData
Creating a L2FC(kdeg) matrix that can be passed to heatmap functions
bakRData object constructor for internal use
bakRFnData object constructor for internal use
Construct heatmap for non-steady state (NSS) analysis
Creating L2FC(kdeg) MA plot from fit objects
Creating L2FC(kdeg) volcano plot from fit objects
Check data quality and make suggestions to user about what analyses to...
Fit dropout model to quantify dropout frequency
Identify features (e.g., transcripts) with high quality data
Simulating nucleotide recoding data
Simulating nucleotide recoding data with relative count data
Fit 'Stan' models to nucleotide recoding RNA-seq data analysis
bakR Data object validator
bakRFnData object validator
Visualize dropout
Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.