R: Spike-in calibration for cell-free MeDIP

# Spike-in calibration for cell-free MeDIP

## Documentation for package ‘spiky’ version 1.19.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

add_frag_info | decode fragment identifiers for spike-in standards
bam_to_bins | create a tiled representation of a genome from the BAM/CRAM file
bin_pmol | Binned estimation of picomoles of DNA present in cfMeDIP assays
convertPairedGRtoGR | Convert Pairs to GRanges
covg_to_df | reshape 'scan_spiked_bam' results into data.frames for model_glm_pmol
dedup | spike-in counts for two samples, as a wide data.frame
find_spike_contigs | find spike-in seqlevels in an object 'x', where !is.null(seqinfo(x))
genbank_mito | various mitochondrial genomes sometimes used as endogenous spike-ins
generate_spike_fasta | for CRAM files, a FASTA reference is required to decode; this builds that
genomic_res | A Granges object with genomic coverage from chr21q22, binned every 300bp for the genomic contigs then averaged across the bin. (In other words, the default output of scan_genomic_contigs or scan_genomic_bedpe, restricted to a small enough set of genomic regions to be practical for examples.) This represents what most users will want to generate from their own genomic BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package.
get_base_name | refactored out of rename_spikes and rename_spike_seqlevels
get_binned_coverage | tabulate read coverage in predefined bins
get_merged_gr | get a GRanges of (by default, standard) chromosomes from seqinfo
get_spiked_coverage | tabulate coverage across assembly and spike contig subset in natural order
get_spike_depth | get the (max, median, or mean) coverage for spike-in contigs from a BAM/CRAM
kmax | simple contig kmer comparisons
kmers | oligonucleotideFrequency, but less letters and more convenient.
methylation_specificity | compute methylation specificity for spike-in standards
model_bam_standards | Build a Bayesian additive model from spike-ins to correct bias in *-seq
model_glm_pmol | Build a generalized linear model from spike-ins to correct bias in cfMeDIP
parse_spike_UMI | parse out the forward and reverse UMIs and contig for a BED/BAM
phage | lambda and phiX phage sequences, sometimes used as spike-ins
plot-method | A handful of methods that I've always felt were missing
predict_pmol | predict picomoles of DNA from a fit and read counts (coverage)
process_spikes | QC, QA, and processing for a new spike database
read_bedpe | read a BEDPE file into Pairs of GRanges (as if a GAlignmentPairs or similar)
rename_spikes | for BAM/CRAM files with renamed contigs, we need to rename 'spike' rows
rename_spike_seqlevels | for spike-in contigs in GRanges, match to standardized spike seqlevels
scan_genomic_bedpe | Scan genomic BEDPE
scan_genomic_contigs | scan genomic contigs in a BAM/CRAM file
scan_methylation_specificity | tabulate methylation specificity for multiple spike-in BAM/CRAM files
scan_spiked_bam | pretty much what it says: scan standard chroms + spike contigs from a BAM
scan_spike_bedpe | Scan spikes BEDPE
scan_spike_contigs | pretty much what it says: scan spike contigs from a BAM or CRAM file
scan_spike_counts | run spike_counts on BAM/CRAM files and shape the results for model_glm_pmol
seqinfo_from_header | create seqinfo (and thus a standard chromosome filter) from a BAM header
spike | spike-in contig properties for Sam's cfMeDIP spikes
spike_bland_altman_plot | Bland-Altman plot for cfMeDIP spike standards
spike_counts | use the index of a spiked BAM/CRAM file for spike contig coverage
spike_cram_counts | spike-in counts, as a long data.frame
spike_read_counts | spike-in counts, as a long data.frame
spike_res | A Granges object with spike-in sequence coverage, and summarized for each spike contig as (the default) 'max' coverage. (In other words, the default output of scan_spike_contigs or scan_spike_bedpe) This represents what most users will want to generate from their own spike-in BAMs or BEDPEs, and is used repeatedly in downstream examples throughout the package.
spiky-methods | A handful of methods that I've always felt were missing
ssb_res | scan_spiked_bam results from a merged cfMeDIP CRAM file (chr22 and spikes)
testGR | a test GRanges with UMI'ed genomic sequences used as controls
tile_bins | Tile the assembly-based contigs of a merged assembly/spike GRanges.