Package: spiky 1.11.0

Tim Triche

spiky: Spike-in calibration for cell-free MeDIP

spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms.

Authors:Samantha Wilson [aut], Lauren Harmon [aut], Tim Triche [aut, cre]

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NEWS

# Install 'spiky' in R:
install.packages('spiky', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/trichelab/spiky/issues

Datasets:
  • dedup - Spike-in counts for two samples, as a wide data.frame
  • genbank_mito - Various mitochondrial genomes sometimes used as endogenous spike-ins
  • 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.
  • phage - Lambda and phiX phage sequences, sometimes used as spike-ins
  • spike - Spike-in contig properties for Sam's cfMeDIP spikes
  • 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.
  • ssb_res - Scan_spiked_bam results from a merged cfMeDIP CRAM file
  • testGR - A test GRanges with UMI'ed genomic sequences used as controls

On BioConductor:spiky-1.11.0(bioc 3.20)spiky-1.10.0(bioc 3.19)

bioconductor-package

34 exports 0.82 score 85 dependencies

Last updated 2 months agofrom:a8a55d78b9

Exports:add_frag_infobam_to_binsbin_pmolconvertPairedGRtoGRcovg_to_dffind_spike_contigsgenerate_spike_fastaget_base_nameget_binned_coverageget_merged_grget_spike_depthget_spiked_coveragekmaxkmersmethylation_specificitymodel_bam_standardsmodel_glm_pmolparse_spike_UMIpredict_pmolprocess_spikesread_bedperename_spike_seqlevelsrename_spikesscan_genomic_bedpescan_genomic_contigsscan_methylation_specificityscan_spike_bedpescan_spike_contigsscan_spike_countsscan_spiked_bamseqinfo_from_headerspike_bland_altman_plotspike_countstile_bins

Dependencies:abindaskpassbamlssBHBiobaseBiocGenericsBiocIOBiocParallelBiostringsbitopsBlandAltmanLehBSgenomeclicodacodetoolscolorspacecpp11crayoncurlDelayedArraydistributions3ellipsisfansifarverformatRFormulafutile.loggerfutile.optionsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicRangesggplot2gluegtablehttrIRangesisobandjsonlitelabelinglambda.rlatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsMBAmgcvmimemunsellmvtnormnlmeopensslpillarpkgconfigR6RColorBrewerRCurlrestfulrRhtslibrjsonrlangRsamtoolsrtracklayerS4ArraysS4VectorsscalessnowspSparseArraySummarizedExperimentsurvivalsystibbleUCSC.utilsutf8vctrsviridisLitewithrXMLXVectoryamlzlibbioc

Spiky: Analysing cfMeDIP-seq data with spike-in controls

Samantha L Wilson and Lauren M Harmon

Rendered fromspiky_vignette.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2022-09-16
Started: 2020-09-17

Readme and manuals

Help Manual

Help pageTopics
decode fragment identifiers for spike-in standardsadd_frag_info
create a tiled representation of a genome from the BAM/CRAM filebam_to_bins
Binned estimation of picomoles of DNA present in cfMeDIP assaysbin_pmol
Convert Pairs to GRangesconvertPairedGRtoGR
reshape 'scan_spiked_bam' results into data.frames for model_glm_pmolcovg_to_df
spike-in counts for two samples, as a wide data.framededup
find spike-in seqlevels in an object 'x', where !is.null(seqinfo(x))find_spike_contigs
various mitochondrial genomes sometimes used as endogenous spike-insgenbank_mito
for CRAM files, a FASTA reference is required to decode; this builds thatgenerate_spike_fasta
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.genomic_res
refactored out of rename_spikes and rename_spike_seqlevelsget_base_name
tabulate read coverage in predefined binsget_binned_coverage
get a GRanges of (by default, standard) chromosomes from seqinfoget_merged_gr
get the (max, median, or mean) coverage for spike-in contigs from a BAM/CRAMget_spike_depth
tabulate coverage across assembly and spike contig subset in natural orderget_spiked_coverage
simple contig kmer comparisonskmax
oligonucleotideFrequency, but less letters and more convenient.kmers
compute methylation specificity for spike-in standardsmethylation_specificity
Build a Bayesian additive model from spike-ins to correct bias in *-seqmodel_bam_standards
Build a generalized linear model from spike-ins to correct bias in cfMeDIPmodel_glm_pmol
parse out the forward and reverse UMIs and contig for a BED/BAMparse_spike_UMI
lambda and phiX phage sequences, sometimes used as spike-insphage
predict picomoles of DNA from a fit and read counts (coverage)predict_pmol
QC, QA, and processing for a new spike databaseprocess_spikes
read a BEDPE file into Pairs of GRanges (as if a GAlignmentPairs or similar)read_bedpe
for spike-in contigs in GRanges, match to standardized spike seqlevelsrename_spike_seqlevels
for BAM/CRAM files with renamed contigs, we need to rename 'spike' rowsrename_spikes
Scan genomic BEDPEscan_genomic_bedpe
scan genomic contigs in a BAM/CRAM filescan_genomic_contigs
tabulate methylation specificity for multiple spike-in BAM/CRAM filesscan_methylation_specificity
Scan spikes BEDPEscan_spike_bedpe
pretty much what it says: scan spike contigs from a BAM or CRAM filescan_spike_contigs
run spike_counts on BAM/CRAM files and shape the results for model_glm_pmolscan_spike_counts
pretty much what it says: scan standard chroms + spike contigs from a BAMscan_spiked_bam
create seqinfo (and thus a standard chromosome filter) from a BAM headerseqinfo_from_header
spike-in contig properties for Sam's cfMeDIP spikesspike
Bland-Altman plot for cfMeDIP spike standardsspike_bland_altman_plot
use the index of a spiked BAM/CRAM file for spike contig coveragespike_counts
spike-in counts, as a long data.framespike_cram_counts
spike-in counts, as a long data.framespike_read_counts
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.spike_res
A handful of methods that I've always felt were missingplot,Rle,ANY-method plot,SimpleRleList,ANY-method spiky-methods
scan_spiked_bam results from a merged cfMeDIP CRAM file (chr22 and spikes)ssb_res
a test GRanges with UMI'ed genomic sequences used as controlstestGR
Tile the assembly-based contigs of a merged assembly/spike GRanges.tile_bins