Package: spiky 1.19.0

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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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spiky/json (API)

# Install 'spiky' in R:

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

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-1.19.0(bioc 3.24)spiky-1.18.0(bioc 3.23)

differentialmethylation dnamethylation normalization preprocessing qualitycontrol sequencing

5.08 score 3 stars 8 scripts 424 downloads 34 exports 72 dependencies

Last updated from:42d312e43d. Checks:8 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	322
linux-devel	NOTE	505
source / vignettes	OK	439
linux-release	NOTE	501
macos-release	NOTE	258
macos-oldrel	NOTE	267
windows-devel	NOTE	353
windows-release	NOTE	366
windows-oldrel	NOTE	352
wasm-release	OK	318

Exports:add_frag_info bam_to_bins bin_pmol convertPairedGRtoGR covg_to_df find_spike_contigs generate_spike_fasta get_base_name get_binned_coverage get_merged_gr get_spike_depth get_spiked_coverage kmax kmers methylation_specificity model_bam_standards model_glm_pmol parse_spike_UMI predict_pmol process_spikes read_bedpe rename_spike_seqlevels rename_spikes scan_genomic_bedpe scan_genomic_contigs scan_methylation_specificity scan_spike_bedpe scan_spike_contigs scan_spike_counts scan_spiked_bam seqinfo_from_header spike_bland_altman_plot spike_counts tile_bins

Dependencies:askpass bamlss BH BiocBaseUtils BiocGenerics BiocIO BiocParallel Biostrings bitops BlandAltmanLeh BSgenome cigarillo cli coda codetools colorspace cpp11 crayon curl distributions3 farver formatR Formula futile.logger futile.options generics GenomeInfoDb GenomicAlignments GenomicRanges ggplot2 glue gtable httr IRanges isoband jsonlite labeling lambda.r lattice lifecycle Matrix matrixStats MBA mgcv mime mvtnorm nlme openssl R6 RColorBrewer RCurl restfulr Rhtslib rjson rlang Rsamtools rtracklayer S4Vectors S7 scales Seqinfo snow sp survival sys UCSC.utils vctrs viridisLite withr XML XVector yaml

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

Introduction | Installation | Load spike database, or create your own with process_spikes(). | Input: A Fasta file, GRanges, or dataframe of spike-in sequences, and a vector of booleans (0 or 1) describing whether each spike-in sequence is methylated. | Output: The output contains a DataFrame with the following columns: | Process the input files | BAM Input | BAM required columns | Output: The output objects will be used downstream in the analysis, including | BEDPE Input | Methylation specificity | Input: The output of the 'scan_spike_contigs' or 'scan_spike_bedpe' functions | Output: methylation specificity mean and median | Example | Fit a Gaussian model to predict the molar amount of DNA sequences | Output: | Calculating molar amount on DNA sequences of interest | Input: The output of the 'scan_genomic_contigs' or 'scan_genomic_bedpe' functions and the Gaussian generalized linear model | Output: sample_pmol_data | Adjusting molar amount to binned genomic windows | Input: output dataframe produced from predict_pmol | Output: sample_binned_data | Session Info | References

Rendered fromspiky_vignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
decode fragment identifiers for spike-in standards	add_frag_info
create a tiled representation of a genome from the BAM/CRAM file	bam_to_bins
Binned estimation of picomoles of DNA present in cfMeDIP assays	bin_pmol
Convert Pairs to GRanges	convertPairedGRtoGR
reshape 'scan_spiked_bam' results into data.frames for model_glm_pmol	covg_to_df
spike-in counts for two samples, as a wide data.frame	dedup
find spike-in seqlevels in an object 'x', where !is.null(seqinfo(x))	find_spike_contigs
various mitochondrial genomes sometimes used as endogenous spike-ins	genbank_mito
for CRAM files, a FASTA reference is required to decode; this builds that	generate_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_seqlevels	get_base_name
tabulate read coverage in predefined bins	get_binned_coverage
get a GRanges of (by default, standard) chromosomes from seqinfo	get_merged_gr
get the (max, median, or mean) coverage for spike-in contigs from a BAM/CRAM	get_spike_depth
tabulate coverage across assembly and spike contig subset in natural order	get_spiked_coverage
simple contig kmer comparisons	kmax
oligonucleotideFrequency, but less letters and more convenient.	kmers
compute methylation specificity for spike-in standards	methylation_specificity
Build a Bayesian additive model from spike-ins to correct bias in *-seq	model_bam_standards
Build a generalized linear model from spike-ins to correct bias in cfMeDIP	model_glm_pmol
parse out the forward and reverse UMIs and contig for a BED/BAM	parse_spike_UMI
lambda and phiX phage sequences, sometimes used as spike-ins	phage
predict picomoles of DNA from a fit and read counts (coverage)	predict_pmol
QC, QA, and processing for a new spike database	process_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 seqlevels	rename_spike_seqlevels
for BAM/CRAM files with renamed contigs, we need to rename 'spike' rows	rename_spikes
Scan genomic BEDPE	scan_genomic_bedpe
scan genomic contigs in a BAM/CRAM file	scan_genomic_contigs
tabulate methylation specificity for multiple spike-in BAM/CRAM files	scan_methylation_specificity
Scan spikes BEDPE	scan_spike_bedpe
pretty much what it says: scan spike contigs from a BAM or CRAM file	scan_spike_contigs
run spike_counts on BAM/CRAM files and shape the results for model_glm_pmol	scan_spike_counts
pretty much what it says: scan standard chroms + spike contigs from a BAM	scan_spiked_bam
create seqinfo (and thus a standard chromosome filter) from a BAM header	seqinfo_from_header
spike-in contig properties for Sam's cfMeDIP spikes	spike
Bland-Altman plot for cfMeDIP spike standards	spike_bland_altman_plot
use the index of a spiked BAM/CRAM file for spike contig coverage	spike_counts
spike-in counts, as a long data.frame	spike_cram_counts
spike-in counts, as a long data.frame	spike_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 missing	plot,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 controls	testGR
Tile the assembly-based contigs of a merged assembly/spike GRanges.	tile_bins

Package: spiky 1.19.0

spiky: Spike-in calibration for cell-free MeDIP

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