vignettes/variantBedOverlap.md
In letaylor/variantBedOverlap: Calculates the overlap of variants in LD with features from BED files
variantBedOverlap
Leland Taylor
2018-03-12
This document shows a demo of how to use variantBedOverlap. There is also a command line script.
# get the lib dir for variantBedOverlap
install_dir=$(R --slave -e 'cat(find.package("variantBedOverlap"))')
# see help options of command line script
Rscript "$install_dir/exec/variant_bed_overlap.R" --help
(1) Get variants in LD
Get proxies from the 1000 Genomes Project via proxysnps.
snps_q <- proxysnps::get_proxies(query = "rs2072014", pop = "FIN")
snps <- subset(snps_q, R.squared >= 0.8)
#knitr::kable( head(snps, 10) )
snps # enabled by setting --> df_print: paged
| | CHROM| POS| ID | REF | ALT | MAF| R.squared| D.prime| CHOSEN |
|-----|------:|---------:|:-----------|:----|:----|----------:|----------:|--------:|:-------|
| 799 | 22| 26231688| rs2072014 | G | A | 0.3434343| 1.000000| 1| TRUE |
| 760 | 22| 26228757| rs35045598 | TA | T | 0.3484848| 0.977926| -1| FALSE |
| 730 | 22| 26227039| rs13056293 | T | C | 0.3636364| 0.915385| -1| FALSE |
| 758 | 22| 26228540| rs6004788 | T | C | 0.3636364| 0.915385| -1| FALSE |
| 723 | 22| 26226815| rs12166250 | A | G | 0.3686869| 0.895680| -1| FALSE |
| 727 | 22| 26226935| rs12166358 | A | G | 0.3737374| 0.876507| -1| FALSE |
| 731 | 22| 26227108| rs13056441 | C | T | 0.3737374| 0.876507| -1| FALSE |
| 755 | 22| 26228281| rs9624913 | A | G | 0.3737374| 0.876507| -1| FALSE |
(2) Get BED overlaps
Get the overlaps of each variant (row) and genomic regions from a list of BED files. Here we load example BED files included in this package taken from Varsheny et al. 2017 (https://doi.org/10.1073/pnas.1621192114).
# few pre-packaged bed files from
# https://theparkerlab.med.umich.edu/data/papers/doi/10.1073/pnas.1621192114/
dir <- system.file("extdata", package = "variantBedOverlap", mustWork = TRUE)
# get overlaps with all bed files in directory
snps_overlap <- variantBedOverlap::get_bed_overlaps(
df = snps,
dir = dir,
col_itemRgb = 5
)
processing file: Adipose processing file: InferiorTemporalLobe processing file: Islets processing file: Liver processing file: SkeletalMuscle
snps_overlap
| ID | CHROM| POS| REF | ALT | MAF| R.squared| D.prime| CHOSEN | bed.Adipose | bed.InferiorTemporalLobe | bed.Islets | bed.Liver | bed.SkeletalMuscle |
|:-----------|------:|---------:|:----|:----|----------:|----------:|--------:|:-------|:-------------------------------------------|:---------------------------------------|:---------------------------------------|:-------------------------------------------|:----------------------------------|
| rs12166250 | 22| 26226815| A | G | 0.3686869| 0.895680| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 |
| rs12166358 | 22| 26226935| A | G | 0.3737374| 0.876507| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 |
| rs13056293 | 22| 26227039| T | C | 0.3636364| 0.915385| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 |
| rs13056441 | 22| 26227108| C | T | 0.3737374| 0.876507| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 |
| rs2072014 | 22| 26231688| G | A | 0.3434343| 1.000000| 1| TRUE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 |
| rs35045598 | 22| 26228757| TA | T | 0.3484848| 0.977926| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 8_Genic_enhancer::194,225,5 |
| rs6004788 | 22| 26228540| T | C | 0.3636364| 0.915385| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 8_Genic_enhancer::194,225,5 |
| rs9624913 | 22| 26228281| A | G | 0.3737374| 0.876507| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 8_Genic_enhancer::194,225,5 |
(3) Plot the data
Plot the overlap data.
# xid_solid_line = list of x-axis IDs to add a line through
# varshney_chrhmm = flag to say assume BED file names are from Varshney et al
# 2017. Given that assumption clean up the names to make them
# publication ready.
lst <- variantBedOverlap::plot_overlaps(
df = snps_overlap,
xid_solid_line = c("rs2072014", "rs35045598"),
varshney_chrhmm = TRUE
)
print( lst$plt )
The output of plot_overlaps also contains the data underlying the plot. Note that ID is a factor now, sorted by POS. If varshney_chrhmm == TRUE, bed_feature will also be a factor sorted by chrhmm_state.
lst$df
| ID | POS| bed_file | bed_feature | color | chrhmm_state|
|:-----------|---------:|:---------------------|:----------------------------|:---------|--------------:|
| rs12166250 | 26226815| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs12166358 | 26226935| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs13056293 | 26227039| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs13056441 | 26227108| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs2072014 | 26231688| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs35045598 | 26228757| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs6004788 | 26228540| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs9624913 | 26228281| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs12166250 | 26226815| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs12166358 | 26226935| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs13056293 | 26227039| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs13056441 | 26227108| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs2072014 | 26231688| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs35045598 | 26228757| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs6004788 | 26228540| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs9624913 | 26228281| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18|
| rs12166250 | 26226815| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs12166358 | 26226935| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs13056293 | 26227039| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs13056441 | 26227108| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs2072014 | 26231688| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs35045598 | 26228757| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs6004788 | 26228540| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs9624913 | 26228281| Islets | 18. Quiescent/low signal | #FFFFFF | 18|
| rs12166250 | 26226815| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs12166358 | 26226935| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs13056293 | 26227039| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs13056441 | 26227108| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs2072014 | 26231688| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs35045598 | 26228757| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs6004788 | 26228540| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs9624913 | 26228281| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17|
| rs12166250 | 26226815| SkeletalMuscle | 5. Strong transcription | #008000 | 5|
| rs12166358 | 26226935| SkeletalMuscle | 5. Strong transcription | #008000 | 5|
| rs13056293 | 26227039| SkeletalMuscle | 5. Strong transcription | #008000 | 5|
| rs13056441 | 26227108| SkeletalMuscle | 5. Strong transcription | #008000 | 5|
| rs2072014 | 26231688| SkeletalMuscle | 5. Strong transcription | #008000 | 5|
| rs35045598 | 26228757| SkeletalMuscle | 8. Genic enhancer | #C2E105 | 8|
| rs6004788 | 26228540| SkeletalMuscle | 8. Genic enhancer | #C2E105 | 8|
| rs9624913 | 26228281| SkeletalMuscle | 8. Genic enhancer | #C2E105 | 8|
letaylor/variantBedOverlap documentation built on Dec. 24, 2019, 11:21 a.m.
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variantBedOverlap
Leland Taylor 2018-03-12
This document shows a demo of how to use variantBedOverlap. There is also a command line script.
# get the lib dir for variantBedOverlap
install_dir=$(R --slave -e 'cat(find.package("variantBedOverlap"))')
# see help options of command line script
Rscript "$install_dir/exec/variant_bed_overlap.R" --help
(1) Get variants in LD
Get proxies from the 1000 Genomes Project via proxysnps.
snps_q <- proxysnps::get_proxies(query = "rs2072014", pop = "FIN")
snps <- subset(snps_q, R.squared >= 0.8)
#knitr::kable( head(snps, 10) )
snps # enabled by setting --> df_print: paged
| | CHROM| POS| ID | REF | ALT | MAF| R.squared| D.prime| CHOSEN | |-----|------:|---------:|:-----------|:----|:----|----------:|----------:|--------:|:-------| | 799 | 22| 26231688| rs2072014 | G | A | 0.3434343| 1.000000| 1| TRUE | | 760 | 22| 26228757| rs35045598 | TA | T | 0.3484848| 0.977926| -1| FALSE | | 730 | 22| 26227039| rs13056293 | T | C | 0.3636364| 0.915385| -1| FALSE | | 758 | 22| 26228540| rs6004788 | T | C | 0.3636364| 0.915385| -1| FALSE | | 723 | 22| 26226815| rs12166250 | A | G | 0.3686869| 0.895680| -1| FALSE | | 727 | 22| 26226935| rs12166358 | A | G | 0.3737374| 0.876507| -1| FALSE | | 731 | 22| 26227108| rs13056441 | C | T | 0.3737374| 0.876507| -1| FALSE | | 755 | 22| 26228281| rs9624913 | A | G | 0.3737374| 0.876507| -1| FALSE |
(2) Get BED overlaps
Get the overlaps of each variant (row) and genomic regions from a list of BED files. Here we load example BED files included in this package taken from Varsheny et al. 2017 (https://doi.org/10.1073/pnas.1621192114).
# few pre-packaged bed files from
# https://theparkerlab.med.umich.edu/data/papers/doi/10.1073/pnas.1621192114/
dir <- system.file("extdata", package = "variantBedOverlap", mustWork = TRUE)
# get overlaps with all bed files in directory
snps_overlap <- variantBedOverlap::get_bed_overlaps(
df = snps,
dir = dir,
col_itemRgb = 5
)
processing file: Adipose processing file: InferiorTemporalLobe processing file: Islets processing file: Liver processing file: SkeletalMuscle
snps_overlap
| ID | CHROM| POS| REF | ALT | MAF| R.squared| D.prime| CHOSEN | bed.Adipose | bed.InferiorTemporalLobe | bed.Islets | bed.Liver | bed.SkeletalMuscle | |:-----------|------:|---------:|:----|:----|----------:|----------:|--------:|:-------|:-------------------------------------------|:---------------------------------------|:---------------------------------------|:-------------------------------------------|:----------------------------------| | rs12166250 | 22| 26226815| A | G | 0.3686869| 0.895680| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 | | rs12166358 | 22| 26226935| A | G | 0.3737374| 0.876507| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 | | rs13056293 | 22| 26227039| T | C | 0.3636364| 0.915385| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 | | rs13056441 | 22| 26227108| C | T | 0.3737374| 0.876507| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 | | rs2072014 | 22| 26231688| G | A | 0.3434343| 1.000000| 1| TRUE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 5_Strong_transcription::0,128,0 | | rs35045598 | 22| 26228757| TA | T | 0.3484848| 0.977926| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 8_Genic_enhancer::194,225,5 | | rs6004788 | 22| 26228540| T | C | 0.3636364| 0.915385| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 8_Genic_enhancer::194,225,5 | | rs9624913 | 22| 26228281| A | G | 0.3737374| 0.876507| -1| FALSE | 17_Weak_repressed_polycomb::192,192,192 | 18_Quiescent/low_signal::255,255,255 | 18_Quiescent/low_signal::255,255,255 | 17_Weak_repressed_polycomb::192,192,192 | 8_Genic_enhancer::194,225,5 |
(3) Plot the data
Plot the overlap data.
# xid_solid_line = list of x-axis IDs to add a line through
# varshney_chrhmm = flag to say assume BED file names are from Varshney et al
# 2017. Given that assumption clean up the names to make them
# publication ready.
lst <- variantBedOverlap::plot_overlaps(
df = snps_overlap,
xid_solid_line = c("rs2072014", "rs35045598"),
varshney_chrhmm = TRUE
)
print( lst$plt )
The output of plot_overlaps also contains the data underlying the plot. Note that ID is a factor now, sorted by POS. If varshney_chrhmm == TRUE, bed_feature will also be a factor sorted by chrhmm_state.
lst$df
| ID | POS| bed_file | bed_feature | color | chrhmm_state| |:-----------|---------:|:---------------------|:----------------------------|:---------|--------------:| | rs12166250 | 26226815| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs12166358 | 26226935| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs13056293 | 26227039| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs13056441 | 26227108| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs2072014 | 26231688| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs35045598 | 26228757| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs6004788 | 26228540| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs9624913 | 26228281| Adipose | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs12166250 | 26226815| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs12166358 | 26226935| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs13056293 | 26227039| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs13056441 | 26227108| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs2072014 | 26231688| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs35045598 | 26228757| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs6004788 | 26228540| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs9624913 | 26228281| InferiorTemporalLobe | 18. Quiescent/low signal | #FFFFFF | 18| | rs12166250 | 26226815| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs12166358 | 26226935| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs13056293 | 26227039| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs13056441 | 26227108| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs2072014 | 26231688| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs35045598 | 26228757| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs6004788 | 26228540| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs9624913 | 26228281| Islets | 18. Quiescent/low signal | #FFFFFF | 18| | rs12166250 | 26226815| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs12166358 | 26226935| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs13056293 | 26227039| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs13056441 | 26227108| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs2072014 | 26231688| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs35045598 | 26228757| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs6004788 | 26228540| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs9624913 | 26228281| Liver | 17. Weak repressed polycomb | #C0C0C0 | 17| | rs12166250 | 26226815| SkeletalMuscle | 5. Strong transcription | #008000 | 5| | rs12166358 | 26226935| SkeletalMuscle | 5. Strong transcription | #008000 | 5| | rs13056293 | 26227039| SkeletalMuscle | 5. Strong transcription | #008000 | 5| | rs13056441 | 26227108| SkeletalMuscle | 5. Strong transcription | #008000 | 5| | rs2072014 | 26231688| SkeletalMuscle | 5. Strong transcription | #008000 | 5| | rs35045598 | 26228757| SkeletalMuscle | 8. Genic enhancer | #C2E105 | 8| | rs6004788 | 26228540| SkeletalMuscle | 8. Genic enhancer | #C2E105 | 8| | rs9624913 | 26228281| SkeletalMuscle | 8. Genic enhancer | #C2E105 | 8|
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