| Title: | Single Nucleotide Polymorphisms Linkage Disequilibrium Visualizations |
| Version: | 1.2.0 |
| Description: | Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>. |
| Imports: | biomaRt, cowplot, data.table, gdsfmt, ggplot2, ggrepel, grid, grDevices, gtable, knitr, magrittr, methods, parallel, reshape2, SNPRelate, stats, utils |
| Depends: | R (≥ 2.15), GWASTools (≥ 1.10.1) |
| Suggests: | rmarkdown, testthat |
| biocViews: | GeneticVariability, MicroArray, SNP |
| URL: | https://gitlab.com/thomaschln/snplinkage |
| BugReports: | https://gitlab.com/thomaschln/snplinkage/-/issues |
| VignetteBuilder: | knitr |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-09-09 16:07:26 UTC; root |
| Author: | Thomas Charlon 
[aut, cre],
Karl Forner [aut],
Alessandro Di Cara [aut],
Jérôme Wojcik [aut] |
| Maintainer: | Thomas Charlon <charlon@protonmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-09-09 19:10:02 UTC |
snplinkage: Single Nucleotide Polymorphisms Linkage Disequilibrium Visualizations
Description
Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) doi:10.13097/archive-ouverte/unige:161795.
Author(s)
Maintainer: Thomas Charlon charlon@protonmail.com (ORCID)
Authors:
Karl Forner
Alessandro Di Cara
Jérôme Wojcik
See Also
Useful links:
Report bugs at https://gitlab.com/thomaschln/snplinkage/-/issues
Exposition pipe
Description
Expose the names in 'lhs' to the 'rhs' expression. Magrittr imported function, see details and examples in the magrittr package.
Arguments
lhs |
A list, environment, or a data.frame. |
rhs |
An expression where the names in lhs is available. |
Value
Result of rhs applied to one or several names of lhs.
Pipe
Description
Pipe an object forward into a function or call expression. Magrittr imported function, see details and examples in the magrittr package.
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
Result of rhs applied to lhs, see details in magrittr package.
Assignment pipe
Description
Pipe an object forward into a function or call expression and update the 'lhs' object with the resulting value. Magrittr imported function, see details and examples in the magrittr package.
Arguments
lhs |
An object which serves both as the initial value and as target. |
rhs |
a function call using the magrittr semantics. |
Value
None, used to update the value of lhs.
Compute Chi-squared p-values
Description
Compute Chi-squared p-values
Usage
chisq_pvalues(
m_data,
response,
adjust_method = "fdr",
mlog10_transform = TRUE,
n_cores = 1,
...
)
Arguments
m_data |
Data matrix of observations by variables |
response |
Response vector of length the number of observations |
adjust_method |
Multiple testing p-value adjustment method. Passed to stats::p.adjust. 'fdr' by default. |
mlog10_transform |
Logical, transform p-values by minus log10. True by default. |
n_cores |
Number of cores |
... |
Passed to stats::chisq.test |
Value
Chi-squared p-values
Compute Chi-squared p-values on a Genotype data object
Description
Compute Chi-squared p-values on a Genotype data object
Usage
chisq_pvalues_gdata(
gdata,
snp_idxs,
response_column = "region",
response_value = "Europe",
threshold = 2,
...
)
Arguments
gdata |
Genotype data object |
snp_idxs |
SNPs indexes |
response_column |
Response column in gdata scans annotations data frame |
response_value |
Response value. The response vector will be a logical, true if equal to the value, false otherwise. |
threshold |
Keep only associations greater than the threshold |
... |
Passed to chisq_pvalues |
Value
SNPs annotation data frame, chi-squared p-values in column pvalues
Crohn's disease data
Description
The data set consist of 103 common (>5% minor allele frequency) SNPs genotyped in 129 trios from an European-derived population. These SNPs are in a 500-kb region on human chromosome 5q31 implicated as containing a genetic risk factor for Crohn disease.
Imported from the gap R package.
An example use of the data is with the following paper, Kelly M. Burkett, Celia M. T. Greenwood, BradMcNeney, Jinko Graham. Gene genealogies for genetic association mapping, with application to Crohn's disease. Fron Genet 2013, 4(260) doi: 10.3389/fgene.2013.00260
Usage
data(crohn)Format
A data frame containing 387 rows and 212 columns
Source
MJ Daly, JD Rioux, SF Schaffner, TJ Hudson, ES Lander (2001) High-resolution haplotype structure in the human genome Nature Genetics 29:229-232
Get diamond ggplot layer.
Description
Diamond ggplot layer for ggplot_ld
Usage
diamond_annots(data, x = "x", y = "y", color = "color", size = 0.5)
Arguments
data |
Data frame of 3 columns defining the diamonds |
x |
Name of the column for horizontal positions |
y |
Name of the column for vertical positions |
color |
Name of the column for color values |
size |
Radius of the diamonds |
Value
gglayers
Fetch allele 1 (GdsGenotypeReader object)
Description
Fetch allele 1 (GdsGenotypeReader object)
Usage
## S3 method for class 'GdsGenotypeReader'
fetch_allele1(obj, snps_idx)
Arguments
obj |
GenotypeData object |
snps_idx |
SNPs indexes |
Value
Allele 1
Fetch allele 1 (GenotypeData object)
Description
Fetch allele 1 (GenotypeData object)
Usage
## S3 method for class 'GenotypeData'
fetch_allele1(obj, ...)
Arguments
obj |
GenotypeData object |
... |
Passed to getAlleleA |
Value
Allele 1
Fetch allele 1 (GenotypeDataSubset object)
Description
Fetch allele 1 (GenotypeDataSubset object)
Usage
## S3 method for class 'GenotypeDataSubset'
fetch_allele1(obj, snps_idx)
Arguments
obj |
GenotypeDataSubset object |
snps_idx |
SNPs indexes |
Value
Allele 1
Fetch allele 1 (default object)
Description
Fetch allele 1 (default object)
Usage
## Default S3 method:
fetch_allele1(obj, snps_idx)
Arguments
obj |
Default object |
snps_idx |
SNPs indexes |
Fetch allele 2 (GdsGenotypeReader object)
Description
Fetch allele 2 (GdsGenotypeReader object)
Usage
## S3 method for class 'GdsGenotypeReader'
fetch_allele2(obj, snps_idx)
Arguments
obj |
GenotypeData object |
snps_idx |
SNPs indexes |
Value
Allele 2
Fetch allele 2 (GenotypeData object)
Description
Fetch allele 2 (GenotypeData object)
Usage
## S3 method for class 'GenotypeData'
fetch_allele2(obj, ...)
Arguments
obj |
GenotypeData object |
... |
Passed to getAlleleB |
Value
Allele 2
Fetch allele 1 (GenotypeDataSubset object)
Description
Fetch allele 1 (GenotypeDataSubset object)
Usage
## S3 method for class 'GenotypeDataSubset'
fetch_allele2(obj, snps_idx)
Arguments
obj |
GenotypeDataSubset object |
snps_idx |
SNPs indexes |
Value
Allele 2
Fetch allele 2 (default object)
Description
Fetch allele 2 (default object)
Usage
## Default S3 method:
fetch_allele2(obj, snps_idx)
Arguments
obj |
Default object |
snps_idx |
SNPs indexes |
Fetch GDS (GdsGenotypeReader)
Description
Fetch GDS (GdsGenotypeReader)
Usage
## S3 method for class 'GdsGenotypeReader'
fetch_gds(obj, ...)
Arguments
obj |
GdsGenotypeReader object |
... |
Not passed |
Value
S4 slot 'handler' of obj
Fetch GDS (GenotypeData)
Description
Fetch GDS (GenotypeData)
Usage
## S3 method for class 'GenotypeData'
fetch_gds(obj, ...)
Arguments
obj |
GenotypeData object |
... |
Not passed |
Value
fetch_gds output on S4 slot 'data' of obj
Fetch GDS (GenotypeDataSubset)
Description
Fetch GDS (GenotypeDataSubset)
Usage
## S3 method for class 'GenotypeDataSubset'
fetch_gds(obj, ...)
Arguments
obj |
GenotypeDataSubset object |
... |
Not passed |
Fetch GDS (default)
Description
Fetch GDS (default)
Usage
## Default S3 method:
fetch_gds(obj, ...)
Arguments
obj |
Default object |
... |
Not passed |
gdata_add_gene_annots
Description
Add biomaRt gene annotations to Genotype Data object.
Usage
gdata_add_gene_annots(
gdata,
snp_idxs,
rsids_colname = "probe_id",
biomart_metadb = get_biomart_metadb()
)
Arguments
gdata |
Genotype Data object |
snp_idxs |
SNP indexes |
rsids_colname |
Column of SNP annotation data frame with rs identifiers |
biomart_metadb |
List with slots snpmart and ensembl, corresponding to the biomart databases to query for SNP identifiers and gene names, respectively. See get_biomart_metadb function. |
Value
Genotype Data object
gdata_add_gene_annots_aim_example
Description
Add ancestry informative markers gene annotations to Genotype Data object. Convenience function for the vignette to avoid querying biomaRt on build.
Usage
gdata_add_gene_annots_aim_example(gdata, aim_idxs)
Arguments
gdata |
Genotype Data object |
aim_idxs |
AIM indexes in the example Genotype data object |
Value
Genotype Data object
gdata_add_gene_annots_hladr_example
Description
Add HLA-DR gene annotations to Genotype Data object. Convenience function for the vignette to avoid querying biomaRt on build.
Usage
gdata_add_gene_annots_hladr_example(gdata, hla_dr_idxs)
Arguments
gdata |
Genotype Data object |
hla_dr_idxs |
HLA-DR indexes in the example Genotype data object |
Value
Genotype Data object
gdata_scan_annots
Description
Get scans annotations from a Genotype Data object or a subset.
Usage
gdata_scans_annots(gdata, scan_ids)
Arguments
gdata |
Genotype Data object |
scan_ids |
Scan identifiers to subset |
Value
Scans annotations data frame
gdata_snp_annots
Description
Get SNPs annotations from a Genotype Data object or a subset.
Usage
gdata_snps_annots(gdata, snp_ids = NULL)
Arguments
gdata |
Genotype Data object |
snp_ids |
SNP identifiers to subset |
Value
SNP annotation data frame
get_biomart_metadb
Description
To query gene names of SNPs, it is necessary to retrieve two objects using biomaRt::useMart. First, the object required to map SNP rs identifiers to ENSEMBL identifiers. Second, the object required to map ENSEMBL identifiers to common gene names. The function returns a list of two slots named snpmart and ensembl corresponding to each one, respectively. Once obtained it is saved to a local file.
Usage
get_biomart_metadb(
filepath = extdata_filepath("bmart_meta.rds"),
host = "https://grch37.ensembl.org"
)
Arguments
filepath |
Path to save the biomaRt objects |
host |
BiomaRt Ensembl host, by default https://grch37.ensembl.org |
Value
List of slots snpmart and ensembl as detailed above
Get scans annotations (GenotypeData object)
Description
Get scans annotations (GenotypeData object)
Usage
## S3 method for class 'GenotypeData'
get_scan_annot(obj, ...)
Arguments
obj |
GenotypeData object |
... |
Not passed |
Value
Data frame
Get scans annotations (GenotypeDataSubset object)
Description
Get scans annotations (GenotypeDataSubset object)
Usage
## S3 method for class 'GenotypeDataSubset'
get_scan_annot(obj, ...)
Arguments
obj |
GenotypeDataSubset object |
... |
Not passed |
Value
Data frame
Get SNPs annotations (GenotypeData object)
Description
Get SNPs annotations (GenotypeData object)
Usage
## S3 method for class 'GenotypeData'
get_snp_annot(obj, ...)
Arguments
obj |
GenotypeData object |
... |
Not passed |
Value
Data frame
Get SNPs annotations (GenotypeDataSubset object)
Description
Get SNPs annotations (GenotypeDataSubset object)
Usage
## S3 method for class 'GenotypeDataSubset'
get_snp_annot(obj, ...)
Arguments
obj |
GenotypeDataSubset object |
... |
Not passed |
Value
Data frame
Ggplot associations
Description
Get SNPs associations ggplot, either as points or as a linked area. Optionally add labels to most associated points using ggrepel.
Usage
ggplot_associations(
df_snp,
pvalue_colname = "pvalues",
labels_colname = "probe_id",
n_labels = 10,
nudge = c(0, 1),
linked_area = FALSE,
byindex = linked_area,
colors = if (linked_area) snp_position_colors(nrow(df_snp)) else "black"
)
Arguments
df_snp |
SNP annotation data frame with columns chromosome, position, and as specified by parameters pvalue_colname and optionally labels_colname. |
pvalue_colname |
Column name of df_snp with association values |
labels_colname |
Optional column name of df_snp with labels. Set to NULL to remove. |
n_labels |
Number of labels of most associated points to display. |
nudge |
Nudge parameter passed to ggrepel::geom_label_repel. |
linked_area |
Add a linked area to associations points, default FALSE |
byindex |
Display by SNP index or chromosomic position (default) |
colors |
Colors of SNPs |
Value
ggplot
Ggplot linkage disequilibrium
Description
Display SNP r2 correlations using points or diamonds with text.
Usage
ggplot_ld(
df_ld,
diamonds = length(unique(df_ld$SNP_A)) < 40,
point_size = 120/sqrt(nrow(df_ld)),
reverse = FALSE,
reindex = TRUE
)
Arguments
df_ld |
Data frame with columns SNP_A, SNP_B, and R2. As returned by the snprelate_ld function. |
diamonds |
Should the values be displayed as diamonds or points ? Default is TRUE for less than 40 SNPs. |
point_size |
Size for geom_point. Ignored if diamonds is TRUE. |
reverse |
Reverse the display (horizontal symmetry) |
reindex |
If FALSE, SNPs are positionned following their IDs |
Value
ggplot
Ggplot SNPs position
Description
Get SNPs position ggplot with mappings to combine with other ggplots. Optionally add labels and an upper subset.
Usage
ggplot_snp_pos(
df_snp,
upper_subset = NULL,
labels_colname = NULL,
colors = snp_position_colors(nrow(df_snp))
)
Arguments
df_snp |
SNP annotation data frame with a column named position and, if specified, one named as the labels_colname parameter. |
upper_subset |
Subset of df_snp for the positions on the upper side |
labels_colname |
Optional column name of df_snp to use as SNP labels. |
colors |
Colors for each SNP |
Value
ggplot
Gtable of linkage disequilibrium and chromosomic positions
Description
Creates a gtable of linkage disequilibrium and chromosomic positions ggplots. A biplot_subset parameter is available to add a second linkage disequibrium ggplot to visualize the effect of a SNP selection.
Usage
gtable_ld(
df_ld,
df_snp,
biplot_subset = NULL,
labels_colname = NULL,
diamonds = length(unique(df_ld$SNP_A)) < 40,
point_size = ifelse(is.null(biplot_subset), 120, 80)/sqrt(nrow(df_ld)),
title = "",
title_biplot = "",
...
)
Arguments
df_ld |
Data frame returned by snprelate_ld |
df_snp |
SNP annotations with columns snpID and position |
biplot_subset |
SNP indexes of the subset for the second ld plot |
labels_colname |
Column name of df_snp to use as SNP labels |
diamonds |
Display the values as diamonds or as points Default is TRUE for less than 40 SNPs. |
point_size |
Size for geom_point. Ignored if diamonds is TRUE. |
title |
Plot title |
title_biplot |
Optional biplot title |
... |
Passed to ggplot_ld |
Value
gtable of ggplots
Examples
library(snplinkage)
gds_path <- save_hgdp_as_gds()
gdata <- load_gds_as_genotype_data(gds_path)
qc <- snprelate_qc(gdata, tagsnp = .99)
snp_idxs_8p23 <- select_region_idxs(qc$gdata, chromosome = 8,
position_min = 11e6, position_max = 12e6)
df_ld <- snprelate_ld(qc$gdata, snps_idx = snp_idxs_8p23, quiet = TRUE)
plt <- gtable_ld(df_ld, df_snp = gdata_snps_annots(qc$gdata))
Gtable of linkage disequilibrium and associations
Description
Creates a gtable of a linkage disequilibrium, chromosomic positions, and association scores ggplots.
Usage
gtable_ld_associations(
df_assocs,
df_ld,
pvalue_colname = "pvalues",
labels_colname = "probe_id",
n_labels = 5,
diamonds = nrow(df_assocs) <= 40,
linked_area = diamonds,
point_size = 150/nrow(df_assocs),
colors = snp_position_colors(nrow(df_assocs)),
...
)
Arguments
df_assocs |
SNP annotation data frame with columns chromosome, position, and as specified by parameters pvalue_colname and optionally labels_colname. |
df_ld |
Data frame with columns SNP_A, SNP_B, and R2, as returned by the snprelate_ld function. |
pvalue_colname |
Column name of df_snp with association values |
labels_colname |
Optional column name of df_snp with labels. Set NULL to remove labels. |
n_labels |
Number of labels of most associated SNPs to display. |
diamonds |
Should the values be displayed as diamonds or points ? Default is TRUE for up to 40 SNPs. |
linked_area |
Add a linked area to associations points. Default same as diamonds. |
point_size |
Point size for ggplot_ld, ignored if diamonds is TRUE. |
colors |
Colors of SNPs |
... |
Passed to ggplot_associations |
Value
gtable
Build gtable by combining ggplots
Description
Build gtable by combining ggplots
Usage
gtable_ld_associations_combine(ggplots, diamonds)
Arguments
ggplots |
List of ggplots |
diamonds |
Does the LD visualization use diamond-type layout |
Value
gtable of ggplots
Examples
library(snplinkage)
# example rnaseq data frame, 20 variables of 20 patients
m_rna = matrix(runif(20 ^ 2), nrow = 20)
# pair-wise correlation matrix
m_ld = cor(m_rna) ^ 2
# keep only upper triangle and reshape to data frame
m_ld[lower.tri(m_ld, diag = TRUE)] = NA
df_ld = reshape2::melt(m_ld) |> na.omit()
# rename for SNPLinkage
names(df_ld) = c('SNP_A', 'SNP_B', 'R2')
# visualize with ggplot_ld
gg_ld = ggplot_ld(df_ld)
# let's imagine the 20 variables came from 3 physically close regions
positions = c(runif(7, 10e5, 15e5), runif(6, 25e5, 30e5),
runif(7, 45e5, 50e5)) |> sort()
# build the dataframe
df_snp_pos = data.frame(position = positions)
df_snp_pos$label = c(rep('HLA-A', 7), rep('HLA-B', 6), rep('HLA-C', 7))
gg_pos_biplot = ggplot_snp_pos(df_snp_pos, labels_colname = 'label',
upper_subset = TRUE)
# let's assume HLA-B is more associated with the outcome than the other genes
pvalues = c(runif(7, 1e-3, 1e-2), runif(6, 1e-8, 1e-6), runif(7, 1e-3, 1e-2))
log10_pvals = -log10(pvalues)
# we can reuse the df_snp_pos object
df_snp_pos$pvalues = log10_pvals
# add the chromosome column
df_snp_pos$chromosome = 6
gg_assocs = ggplot_associations(df_snp_pos, labels_colname = 'label',
linked_area = TRUE, nudge = c(0, 0.5),
n_labels = 12)
l_ggs = list(pos = gg_pos_biplot, ld = gg_ld, pval = gg_assocs)
gt_ld = gtable_ld_associations_combine(l_ggs, diamonds = TRUE)
grid::grid.draw(gt_ld)
Gtable of linkage disequilibrium and associations using a GenotypeData object
Description
Compute linkage disequilibrium using snprelate_ld on the set of SNPs in the associations data frame and call gtable_ld_associations. Creates a gtable of a linkage disequilibrium, chromosomic positions, and association scores ggplots.
Usage
gtable_ld_associations_gdata(
df_assocs,
gdata,
pvalue_colname = "pvalues",
labels_colname = "probe_id",
diamonds = nrow(df_assocs) <= 40,
window = 15,
...
)
Arguments
df_assocs |
SNP annotation data frame with columns chromosome, position, and as specified by parameters pvalue_colname and optionally labels_colname. |
gdata |
GenotypeData object, as returned by load_gds_as_genotype_data |
pvalue_colname |
Column name of df_snp with association values |
labels_colname |
Optional column name of df_snp with labels. Set NULL to remove labels. |
diamonds |
Should the values be displayed as diamonds or points ? Default is TRUE for up to 40 SNPs. |
window |
Window size for snprelate_ld. Forced to the total number of SNPs if diamonds is FALSE |
... |
Passed to gtable_ld_associations |
Value
gtable
Examples
library(snplinkage)
gds_path <- save_hgdp_as_gds()
gdata <- load_gds_as_genotype_data(gds_path)
qc <- snprelate_qc(gdata, tagsnp = .99)
snp_idxs_mhc <- select_region_idxs(qc$gdata,
chromosome = 6, position_min = 29e6, position_max = 33e6)
df_assocs <- chisq_pvalues_gdata(qc$gdata, snp_idxs_mhc)
df_top_aim <- subset(df_assocs, rank(-pvalues, ties.method = 'first') <= 20)
#qc$gdata <- gdata_add_gene_annots(qc$gdata, rownames(df_top_aim))
qc$gdata <- gdata_add_gene_annots_aim_example(qc$gdata, rownames(df_top_aim))
plt <- gtable_ld_associations_gdata(df_top_aim, qc$gdata,
labels_colname = 'gene')
Gtable of linkage disequilibrium and positions using a GenotypeData object
Description
Compute linkage disequilibrium using snprelate_ld on a set of SNP indexes and call gtable_ld. Two parameters are available to compute and compare minor allele frequency filtering and TagSNP selection by displaying two LD plots with their positions in the center. The maf and r2 parameters are used similarly and as follows: - compare baseline with MAF 5 gtable_ld(gdata, snps_idx, maf = 0.05) - compare baseline with TagSNP r2 = 0.8 gtable_ld(gdata, snps_idx, r2 = 0.8) - compare 5 gtable_ld(gdata, snps_idx, maf = c(0.05, 0.05), r2 = 0.8) - compare MAF 5 gtable_ld(gdata, snps_idx, maf = c(0.05, 0.1), r2 = c(0.8, 0.6))
Usage
gtable_ld_gdata(
gdata,
snps_idx,
maf = NULL,
r2 = NULL,
diamonds = length(snps_idx) < 40,
window = 15,
autotitle = TRUE,
autotitle_bp = TRUE,
double_title = FALSE,
...
)
Arguments
gdata |
GenotypeData object returned by load_gds_as_genotype_data |
snps_idx |
SNPs indexes to select |
maf |
Minor allele frequency threshold(s), see description |
r2 |
TagSNP r2 threshold(s), see description |
diamonds |
Display the values as diamonds or as points Default is TRUE for less than 40 SNPs. |
window |
Window size for snprelate_ld. Forced to the total number of SNPs if diamonds is FALSE |
autotitle |
Set title to feature selection method(s), number of SNPs and chromosome |
autotitle_bp |
Set biplot title to feature selection method(s), number of SNPs and chromosome |
double_title |
Logical, if false (default) keep only biplot title |
... |
Passed to gtable_ld |
Value
gtable of ggplots
Examples
library(snplinkage)
gds_path <- save_hgdp_as_gds()
gdata <- load_gds_as_genotype_data(gds_path)
qc <- snprelate_qc(gdata, tagsnp = .99)
snp_idxs_1p13_large <- select_region_idxs(qc$gdata, chromosome = 1,
position_min = 114e6, n_snps = 100)
plt <- gtable_ld_gdata(qc$gdata, snp_idxs_1p13_large)
Build gtable by combining ggplots
Description
Build gtable by combining ggplots
Usage
gtable_ld_grobs(plots, labels_colname, title)
Arguments
plots |
List of ggplots |
labels_colname |
Does the SNP position plot contain labels |
title |
Title text string |
Value
gtable of ggplots
Examples
library(snplinkage)
# example rnaseq data frame, 20 variables of 20 patients
m_rna = matrix(runif(20 ^ 2), nrow = 20)
# pair-wise correlation matrix
m_ld = cor(m_rna) ^ 2
# keep only upper triangle and reshape to data frame
m_ld[lower.tri(m_ld, diag = TRUE)] = NA
df_ld = reshape2::melt(m_ld) |> na.omit()
# rename for SNPLinkage
names(df_ld) = c('SNP_A', 'SNP_B', 'R2')
# visualize with ggplot_ld
gg_ld = ggplot_ld(df_ld)
# let's imagine the 20 variables came from 3 physically close regions
positions = c(runif(7, 10e5, 15e5), runif(6, 25e5, 30e5),
runif(7, 45e5, 50e5)) |> sort()
# build the dataframe
df_snp_pos = data.frame(position = positions)
df_snp_pos$label = c(rep('HLA-A', 7), rep('HLA-B', 6), rep('HLA-C', 7))
gg_snp_pos = ggplot_snp_pos(df_snp_pos, labels_colname = 'label')
l_ggs = list(snp_pos = gg_snp_pos, ld = gg_ld)
gt_ld = gtable_ld_grobs(l_ggs, labels_colname = TRUE,
title = 'RNASeq correlations')
grid::grid.draw(gt_ld)
Is SNP first dimension (GdsGenotypeReader object)
Description
Is SNP first dimension (GdsGenotypeReader object)
Usage
## S3 method for class 'GdsGenotypeReader'
is_snp_first_dim(obj, ...)
Arguments
obj |
GdsGenotypeReader object |
... |
Not passed |
Value
is_snp_first_dim output on S4 slot 'handler'
Is SNP first dimension (GenotypeData object)
Description
Is SNP first dimension (GenotypeData object)
Usage
## S3 method for class 'GenotypeData'
is_snp_first_dim(obj, ...)
Arguments
obj |
Genotype data object |
... |
Not passed |
Value
is_snp_first_dim output on S4 slot 'data'
Is SNP first dimension (MatrixGenotypeReader object)
Description
Is SNP first dimension (MatrixGenotypeReader object)
Usage
## S3 method for class 'MatrixGenotypeReader'
is_snp_first_dim(obj, ...)
Arguments
obj |
MatrixGenotypeReader object |
... |
Not passed |
Value
TRUE
Is SNP first dimension (NcdfGenotypeReader object)
Description
Is SNP first dimension (NcdfGenotypeReader object)
Usage
## S3 method for class 'NcdfGenotypeReader'
is_snp_first_dim(obj, ...)
Arguments
obj |
NcdfGenotypeReader object |
... |
Not passed |
Value
TRUE
Is SNP first dimension (default)
Description
Is SNP first dimension (default)
Usage
## Default S3 method:
is_snp_first_dim(obj, ...)
Arguments
obj |
Default object |
... |
Not passed |
Value
NA
Is SNP first dimension (GDS object)
Description
Is SNP first dimension (GDS object)
Usage
## S3 method for class 'gds.class'
is_snp_first_dim(obj, ...)
Arguments
obj |
GDS object |
... |
Not passed |
Value
Logical, TRUE if SNP is first dimension
Load GDS as Genotype Data
Description
Open a connection to a snpgds file (cf. SNPRelate package) as a Genotype Data object.
Usage
load_gds_as_genotype_data(
gds_file,
read_snp_annot = TRUE,
read_scan_annot = TRUE
)
Arguments
gds_file |
Path of snpgds file |
read_snp_annot |
Read the SNPs' annotations |
read_scan_annot |
Read the scans' annotations |
Value
Genotype Data object
Examples
library(snplinkage)
gds_path <- save_hgdp_as_gds()
gdata <- load_gds_as_genotype_data(gds_path)
Separate a matrix in a list of matrices of length the number of cores and apply a function on the columns in parallel
Description
Separate a matrix in a list of matrices of length the number of cores and apply a function on the columns in parallel
Usage
parallel_apply(m_data, apply_fun, n_cores = 1, ...)
Arguments
m_data |
Data matrix |
apply_fun |
Function to apply |
n_cores |
Number of cores |
... |
Passed to apply_fun |
Value
apply_fun return
print_qc_as_tex_table
Description
Print information about quality control performed by the snprelate_qc function.
Usage
print_qc_as_tex_table(
gdata_qc,
label = "qc",
caption = paste("Quality control and feature selection of the subset of the",
"human genome diversity project dataset.")
)
Arguments
gdata_qc |
Genotype Data object object returned by snprelate_qc |
label |
Label of the Tex table |
caption |
Caption of the Tex table |
Value
Prints knitr::kable object using cat
save_hgdp_as_gds
Description
Save the HGDP SNP data text file as a Genomic Data Structure file
Usage
save_hgdp_as_gds(paths = hgdp_filepaths(), outpath = tempfile(), ...)
Arguments
paths |
Paths of the zip, txt, and gds files |
outpath |
Output GDS file path |
... |
Passed to save_genotype_data_as_gds |
Value
Path of the saved gds file
select_region_idxs
Description
Select SNP indexes corresponding to a specific genomic region.
Usage
select_region_idxs(
gdata,
chromosome,
position_min = -Inf,
position_max = Inf,
n_snps = 0,
offset = 0
)
Arguments
gdata |
Genotype Data object |
chromosome |
Chromosome to select |
position_min |
Minimum base pair position to select |
position_max |
Maximum base pair position to select |
n_snps |
Maximum number of SNPs to return |
offset |
Number of SNPs to offset |
Value
SNP indexes of Genotype Data object
Compute allele frequencie and snp missing rate
Description
Wrapper over SNPRelate::snpgdsSNPRateFreq
Usage
snprelate_allele_frequencies(
gdata,
snps_idx = NULL,
scans_idx = NULL,
quiet = FALSE
)
Arguments
gdata |
A GenotypeData object |
snps_idx |
Vector of snps indices |
scans_idx |
Vector of scans indices |
quiet |
Whether to be quiet |
Value
A data frame of snps_idx, snps_ids, allele1, allele2, maf, missing where allele1 and allele2 are the rates of the alleles, and maf the minimum of the 2. Missing is the missing rate. N.B: the allele rates are computed on the non missing genotypes, i.e. their sum equals 1.
Wrapper for snpgdsLDMat to compute r2
Description
Wrapper for snpgdsLDMat to compute r2
Usage
snprelate_ld(
gdata,
window_size = 0,
min_r2 = 0,
snps_idx = NULL,
scans_idx = NULL,
threads = 1,
quiet = FALSE
)
Arguments
gdata |
A GenotypeData object |
window_size |
Max number of SNPs in LD window, 0 for no window |
min_r2 |
Minimum r2 value to report |
snps_idx |
Indices of snps to use |
scans_idx |
Indices of scans to use |
threads |
The number of threads to use |
quiet |
Whether to be quiet |
Value
A data frame with columns SNP_A, SNP_B, R2 for r2 >= min_r2
Wrapper for snpgdsLDpruning to select Tag SNPs
Description
The tagged snp set is (by sliding window) representative and strongly not redundant.
Usage
snprelate_ld_select(
gdata,
window_length = 500L,
min_r2,
window_size = NA,
snps_idx = NULL,
scans_idx = NULL,
remove.monosnp = FALSE,
autosome.only = FALSE,
method = "r",
threads = 1,
quiet = FALSE,
...
)
Arguments
gdata |
A GenotypeData object |
window_length |
Max length in kb of the window |
min_r2 |
Minimum r2 value to report |
window_size |
Max number of SNPs in LD window |
snps_idx |
Indices of snps to use |
scans_idx |
Indices of scans to use |
remove.monosnp |
if TRUE, remove monomorphic SNPs |
autosome.only |
if |
method |
"composite", "r", "dprime", "corr", see details |
threads |
The number of threads to use, currently ignored |
quiet |
Whether to be quiet |
... |
Forwarded to SNPRelate::snpgdsLDpruning |
Value
A list of SNP IDs stratified by chromosomes.
snprelate_qc
Description
Quality control using SNPRelate functions.
Usage
snprelate_qc(
gdata,
samples_nas = 0.03,
ibs = 0.99,
keep_ids = NULL,
snps_nas = 0.01,
maf = 0.05,
tagsnp = 0.8,
n_cores = 1
)
Arguments
gdata |
Genotype data object |
samples_nas |
NA threshold for samples, default 3 pct |
ibs |
Samples identity by state threshold, default 99 pct |
keep_ids |
Samples ids to keep even if IBS is higher than threshold. Used for monozygotic twins. |
snps_nas |
NA threshold for SNPs, default 1 pct |
maf |
Minor allele frequency threshold, default 5 pct |
tagsnp |
TagSNP r2 correlation threshold, default 0.8 |
n_cores |
Number of cores |
Value
List of gdata, Genotype data object, and df_qc, QC info data frame
Examples
library(snplinkage)
gds_path <- save_hgdp_as_gds()
gdata <- load_gds_as_genotype_data(gds_path)
qc <- snprelate_qc(gdata, tagsnp = .99)