Package index • PRIMEmodel

All functions

batch_heatmapData(): Batch wrapper for heatmapData that preserves input order
cast_rse_to_granges(): Convert SummarizedExperiment to GRanges with Assay Data
check_python_dependencies(): Check Required Python Dependencies
check_valid_profile_rownames(): Check presence and format of rownames in a profile matrix
choose_required_packages(): Dynamically choose required packages based on numpy version
convert_rowname_to_nostrand(): Convert strand suffix in row names to "*"
convert_strand_to_nostrand_gr(): Convert Strand Information to No Strand for GRanges Object
convert_to_scipy_sparse(): Convert R sparse matrix to compatible SciPy sparse object
create_granges_from_rownames(): Create GRanges object from row names like "chr:start-end;strand"
disambiguate_sample_names(): Disambiguate duplicate sample names from a named result list
extract_rowname_components(): Extract chr, start, end, and strand from row names like "chr:start-end;strand"
extract_sample_label(): Extract sample label from input_basename (internal)
modify_profile_rownames(): Modify row names to no-strand if both strands match
plc_configure_python(): Configure Python environment for PRIMEmodel
plc_coreovl_with_d(): Collapse core regions from a BED file with score-based filtering
plc_create_granges_from_bed(): Create a GRanges object from a BED data.table
plc_create_output_dir(): Create the output directory if it doesn't exist
plc_detect_parallel_plan(): Detect optimal parallel backend (multisession or callr)
plc_error(): Log a message with ERROR level
plc_extend_fromthick(): Extend GRanges from thick regions and trim to fixed width
plc_find_bed_files_by_partial_name(): Find .bed files matching a partial name in a directory
plc_focal_prediction_to_rse(): Convert a directory of focal predictions to a RangedSummarizedExperiment
plc_get_ctss_from_bw(): Extract CAGE Transcription Start Sites (CTSSs) from BigWig Files
plc_get_tcs_and_extend_fromthick(): Get tag clusters and extend from thick positions
plc_load_bed_file_wt_header(): Load a BED file and validate required columns
plc_message(): Log a message with INFO level
plc_profile(): Process profiles for each column in the CTSS dataset and save results
plc_profile_chr(): Profile CTSS counts over strand-merged sliding windows for a single chromosome.
plc_tc_sliding_window(): Perform Parallel Sliding Window Expansion on GRanges Tag Clusters The function performs sliding window expansion parallelly for each chromosome in a sample using tc_sliding_window_chr().
plc_test_scipy_save_npz(): Test Python SciPy Sparse Matrix Export
plc_validate_tc_object(): Validate a TC object
plc_warn(): Log a message with WARN level
predict(): Run the full PRIMEmodel pipeline for regulatory element prediction
predictExample(): Run PRIMEmodel on ctss_rse_chr16to17.rds from extdata
predictFocal(): Run the PRIMEmodel focal Pipeline on CTSS and Identified Regions
predictFocalExample(): Run an Example of the PRIMEmodel focal Pipeline
prep_profile_dir(): Prepare Directory Structure for Profile Output
remove_metadata_and_duplicates(): Remove Metadata and Duplicate Genomic Ranges
selective_merge_cores(): Selectively merge overlapping cores based on score difference.
set_parallel_plan(): Set a robust parallel plan using multisession or callr
setup_log_target(): Set up logging to console or file
setup_tmp_dir(): Set up temporary directory under output directory
tc_sliding_window_chr(): Generate Sliding Windows for a Single Chromosome
write_granges_to_bed_coreovlwithd(): Write a GRanges object to a BED file for coreovlwith-d.