diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index cd42ce3..e31a813 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-06-04T23:14:11","documenter_version":"1.4.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-06-05T00:28:33","documenter_version":"1.4.1"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index d23f98e..a27734c 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -1,16 +1,16 @@ API · GeneticsMakie.jl

API

GeneticsMakie.findclosestgene — Method
findclosestgene(chr::AbstractString, bp::Real, gencode::DataFrame; start::Bool, proteincoding::Bool)
-findclosestgene(df::DataFrame, gencode::DataFrame; start::Bool, proteincoding::Bool)

Find the closest gene(s) to a genomic coordinate or a list of genomic coordinates using gencode.

Optionally, the closest gene can be defined from the gene start site using start, and only protein coding genes can be considered using proteincoding. The default start and proteincoding are false.

source
GeneticsMakie.findgene — Method
findgene(gene::AbstractString, gencode::DataFrame)

Find chromosome, gene start, and gene stop sites for the gene of interest.

source
GeneticsMakie.findgwasloci — Method
findgwasloci(gwas::DataFrame; p::Real)
-findgwasloci(gwas::Vector{DataFrame}; p::Real)

Find genome-wide significant loci for gwas that are separated from each other by at least 1 Mb.

Alternatively, find genome-wide significant loci across multiple gwas that are all separated by at least 1 Mb. p determines the genome-wide significance threshold, which is 5e-8 by default.

source
GeneticsMakie.labelgenome — Method
labelgenome(g::GridPosition, chromosome::AbstractString, range1::Real, range2::Real)

Label g with a given chromosome and genomic range between range1 and range2.

source
GeneticsMakie.mungesumstats! — Method
mungesumstats!(gwas::DataFrame)
-mungesumstats!(gwas::Vector{DataFrame})

Munge gwas by harmonizing the names of columns, their types, and P values, among others.

source
GeneticsMakie.parsegtf! — Method
parsegtf!(gencode::DataFrame)

Parse gencode by extracting gene_id, gene_name, gene_type, transcript_id, transcript_support_level information from the info column.

source
GeneticsMakie.plotgenes! — Method
plotgenes!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, gencode::DataFrame; kwargs)
+findclosestgene(df::DataFrame, gencode::DataFrame; start::Bool, proteincoding::Bool)

Find the closest gene(s) to a genomic coordinate or a list of genomic coordinates using gencode.

Optionally, the closest gene can be defined from the gene start site using start, and only protein coding genes can be considered using proteincoding. The default start and proteincoding are false.

source
GeneticsMakie.findgene — Method
findgene(gene::AbstractString, gencode::DataFrame)

Find chromosome, gene start, and gene stop sites for the gene of interest.

source
GeneticsMakie.findgwasloci — Method
findgwasloci(gwas::DataFrame; p::Real)
+findgwasloci(gwas::Vector{DataFrame}; p::Real)

Find genome-wide significant loci for gwas that are separated from each other by at least 1 Mb.

Alternatively, find genome-wide significant loci across multiple gwas that are all separated by at least 1 Mb. p determines the genome-wide significance threshold, which is 5e-8 by default.

source
GeneticsMakie.labelgenome — Method
labelgenome(g::GridPosition, chromosome::AbstractString, range1::Real, range2::Real)

Label g with a given chromosome and genomic range between range1 and range2.

source
GeneticsMakie.mungesumstats! — Method
mungesumstats!(gwas::DataFrame)
+mungesumstats!(gwas::Vector{DataFrame})

Munge gwas by harmonizing the names of columns, their types, and P values, among others.

source
GeneticsMakie.parsegtf! — Method
parsegtf!(gencode::DataFrame)

Parse gencode by extracting gene_id, gene_name, gene_type, transcript_id, transcript_support_level information from the info column.

source
GeneticsMakie.plotgenes! — Method
plotgenes!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, gencode::DataFrame; kwargs)
 plotgenes!(ax::Axis, chromosome::AbstractString, bp::Real, gencode::DataFrame; kwargs)
-plotgenes!(ax::Axis, gene::AbstractString, gencode::DataFrame; kwargs)

Plot collapsed gene bodies for genes within a given chromosome and genomic range between range1 and range2.

Alternatively, plot within a given chromosome and a certain window around a genomic coordinate bp or plot within a certain window around gene.

Arguments

  • height::Real = 0.25: the height of exons.
  • genecolor = :royalblue: the color of genes.
  • textcolor = :black: the color of gene labels.
  • window::Real = 1e6: the window around bp or gene.
source
GeneticsMakie.plotgenes! — Method
plotgenes!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, highlight::Tuple{AbstractVector, AbstractVector}, gencode::DataFrame; height::Real)
+plotgenes!(ax::Axis, gene::AbstractString, gencode::DataFrame; kwargs)

Plot collapsed gene bodies for genes within a given chromosome and genomic range between range1 and range2.

Alternatively, plot within a given chromosome and a certain window around a genomic coordinate bp or plot within a certain window around gene.

Arguments

  • height::Real = 0.25: the height of exons.
  • genecolor = :royalblue: the color of genes.
  • textcolor = :black: the color of gene labels.
  • window::Real = 1e6: the window around bp or gene.
source
GeneticsMakie.plotgenes! — Method
plotgenes!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, highlight::Tuple{AbstractVector, AbstractVector}, gencode::DataFrame; height::Real)
 plotgenes!(ax::Axis, chromosome::AbstractString, bp::Real, highlight::Tuple{AbstractVector, AbstractVector}, gencode::DataFrame; window::Real, height::Real)
-plotgenes!(ax::Axis, gene::AbstractString, highlight::Tuple{AbstractVector, AbstractVector}, gencode::DataFrame; window::Real, height::Real)

Plot gene bodies with a vector of genes highlighted by a vector of colors via highlight.

source
GeneticsMakie.plotgwas! — Method
plotgwas!(ax::Axis, gwas::DataFrame; ymax::Real, p::Real, sigline::Bool, sigcolor::Bool, build = 37)

Plot gwas results as a Manhattan plot.

Arguments

  • ymax::Real: the maximum value for y axis.
  • p::Real = 5e-8: the genome-wide significance threshold.
  • linecolor = :red2: the color of genome-wide significance line, which can be turned off by setting to nothing.
  • scattercolor = "#4DB069": the color of genome-wide significant variants, which can be turned off by setting to nothing.
  • chromcolors = ["#0D0D66", "#7592C8"]: the colors of even and odd chromosomes.
  • build::Int = 37: the human genome build.
source
GeneticsMakie.plotisoforms! — Method
plotisoforms!(ax::Axis, gene::AbstractString, gencode::DataFrame; kwargs)

Plot each isoform of a given gene on a separate row.

Arguments

  • orderby::Union{Nothing, AbstractVector{<:AbstractString}} = nothing: the order of isoforms.
  • highlight::Union{Nothing, Tuple{AbstractVector, AbstractVector}} = nothing: isoforms to be highlighted and their colors.
  • height::Real = 0.25: the height of exons.
  • isoformcolor = :royalblue: the color of isoforms.
  • textcolor = :black: the color of isoform labels.
  • text::Union{Bool, Symbol} = :top: the position of isoform labels.
source
GeneticsMakie.plotld — Method
plotld(LD::AbstractMatrix; kwargs)
-plotld!(ax::Axis, LD::AbstractMatrix; kwargs)

Heatmap of symmetric correlation matrix LD with the diagonal elements on the x-axis.

Keyword arguments

  • threshold : threshold below which values are ignored (default to 1/9)
  • colormap : colormap of values (default to cgrad(:Blues_9, 9, categorical = true))
  • colorrange : start and end points of colormap (default to (0, 1))
  • strokewidth : width of outline around heatmap boxes (default to 0)
source
GeneticsMakie.plotlocus! — Method
plotlocus!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, gwas::DataFrame; kwargs)
+plotgenes!(ax::Axis, gene::AbstractString, highlight::Tuple{AbstractVector, AbstractVector}, gencode::DataFrame; window::Real, height::Real)

Plot gene bodies with a vector of genes highlighted by a vector of colors via highlight.

source
GeneticsMakie.plotgwas! — Method
plotgwas!(ax::Axis, gwas::DataFrame; ymax::Real, p::Real, sigline::Bool, sigcolor::Bool, build = 37)

Plot gwas results as a Manhattan plot.

Arguments

  • ymax::Real: the maximum value for y axis.
  • p::Real = 5e-8: the genome-wide significance threshold.
  • linecolor = :red2: the color of genome-wide significance line, which can be turned off by setting to nothing.
  • scattercolor = "#4DB069": the color of genome-wide significant variants, which can be turned off by setting to nothing.
  • chromcolors = ["#0D0D66", "#7592C8"]: the colors of even and odd chromosomes.
  • build::Int = 37: the human genome build.
source
GeneticsMakie.plotisoforms! — Method
plotisoforms!(ax::Axis, gene::AbstractString, gencode::DataFrame; kwargs)

Plot each isoform of a given gene on a separate row.

Arguments

  • orderby::Union{Nothing, AbstractVector{<:AbstractString}} = nothing: the order of isoforms.
  • highlight::Union{Nothing, Tuple{AbstractVector, AbstractVector}} = nothing: isoforms to be highlighted and their colors.
  • height::Real = 0.25: the height of exons.
  • isoformcolor = :royalblue: the color of isoforms.
  • textcolor = :black: the color of isoform labels.
  • text::Union{Bool, Symbol} = :top: the position of isoform labels.
source
GeneticsMakie.plotld — Method
plotld(LD::AbstractMatrix; kwargs)
+plotld!(ax::Axis, LD::AbstractMatrix; kwargs)

Heatmap of symmetric correlation matrix LD with the diagonal elements on the x-axis.

Keyword arguments

  • threshold : threshold below which values are ignored (default to 1/9)
  • colormap : colormap of values (default to cgrad(:Blues_9, 9, categorical = true))
  • colorrange : start and end points of colormap (default to (0, 1))
  • strokewidth : width of outline around heatmap boxes (default to 0)
source
GeneticsMakie.plotlocus! — Method
plotlocus!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, gwas::DataFrame; kwargs)
 plotlocus!(ax::Axis, chromosome::AbstractString, bp::Real, gwas::DataFrame; kwargs)
-plotlocus!(ax::Axis, gene::AbstractString, gwas::DataFrame, gencode::DataFrame; kwargs)

Plot gwas results within a given chromosome and genomic range between range1 and range2.

Alternatively, plot within a given chromosome and a certain window around a genomic coordinate bp or plot within a certain window around gene.

Arguments

  • ld::Union{Nothing, SnpData, Tuple{SnpData, Union{AbstractString, Tuple{AbstractString, Int}}}} = nothing: the reference panel for which LD is calculated.
  • ymax::Real: the maximum value for y axis.
  • window::Real = 1e6: the window around bp or gene.
source
GeneticsMakie.plotloops! — Method
plotloops!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, loopdf::DataFrame; kwargs)
+plotlocus!(ax::Axis, gene::AbstractString, gwas::DataFrame, gencode::DataFrame; kwargs)

Plot gwas results within a given chromosome and genomic range between range1 and range2.

Alternatively, plot within a given chromosome and a certain window around a genomic coordinate bp or plot within a certain window around gene.

Arguments

  • ld::Union{Nothing, SnpData, Tuple{SnpData, Union{AbstractString, Tuple{AbstractString, Int}}}} = nothing: the reference panel for which LD is calculated.
  • ymax::Real: the maximum value for y axis.
  • window::Real = 1e6: the window around bp or gene.
source
GeneticsMakie.plotloops! — Method
plotloops!(ax::Axis, chromosome::AbstractString, range1::Real, range2::Real, loopdf::DataFrame; kwargs)
 plotloops!(ax::Axis, chromosome::AbstractString, bp::Real, loopdf::DataFrame; kwargs)
-plotloops!(ax::Axis, gene::AbstractString, loopdf::DataFrame, gencode::DataFrame; kwargs)

Plot loops present in loopdf within a given chromosome and genomic range between range1 and range2.

Alternatively, plot within a given chromosome and a certain window around a genomic coordinate bp or plot within a certain window around gene.

Arguments

  • ymax::Real = 102: the maximum value for y axis.
  • linewidth = 0.25: the line width of the loops' arcs.
  • colorarc = "#9658B2": the color of loops' arcs.
  • colorend = ("#FFBB00", 0.5): the color of loops' ends.
  • resolution = 1000: plot resolution points along x-axis within the given range.
source
GeneticsMakie.plotqq! — Method
plotqq!(ax::Axis, gwas::DataFrame; kwargs)
-plotqq!(ax::Axis, P::AbstractVector; kwargs)

Plot QQ plot of P values where the expected distribution is the uniform distribution.

Keyword arguments include xstep::Real and ystep::Real for x and y axes ticks step sizes.

source
GeneticsMakie.plotrg — Method
plotrg(r::AbstractMatrix; kwargs)
-plotrg!(ax::Axis, r::AbstractMatrix; kwargs)

Correlation plot of matrix r.

Keyword arguments

  • circle : whether to draw cicles instead of rectangles (default to true)
  • diagonal : whether to visualize diagonal elements (default to false)
  • colormap : colormap of values (default to :RdBu_10)
  • colorrange : start and end points of colormap (default to (-1, 1))
  • strokewidth : width of outline around surrounding boxes (default to 0.5)
source
+plotloops!(ax::Axis, gene::AbstractString, loopdf::DataFrame, gencode::DataFrame; kwargs)

Plot loops present in loopdf within a given chromosome and genomic range between range1 and range2.

Alternatively, plot within a given chromosome and a certain window around a genomic coordinate bp or plot within a certain window around gene.

Arguments

source
GeneticsMakie.plotqq! — Method
plotqq!(ax::Axis, gwas::DataFrame; kwargs)
+plotqq!(ax::Axis, P::AbstractVector; kwargs)

Plot QQ plot of P values where the expected distribution is the uniform distribution.

Keyword arguments include xstep::Real and ystep::Real for x and y axes ticks step sizes.

source
GeneticsMakie.plotrg — Method
plotrg(r::AbstractMatrix; kwargs)
+plotrg!(ax::Axis, r::AbstractMatrix; kwargs)

Correlation plot of matrix r.

Keyword arguments

  • circle : whether to draw cicles instead of rectangles (default to true)
  • diagonal : whether to visualize diagonal elements (default to false)
  • colormap : colormap of values (default to :RdBu_10)
  • colorrange : start and end points of colormap (default to (-1, 1))
  • strokewidth : width of outline around surrounding boxes (default to 0.5)
source
diff --git a/dev/examples/genes/index.html b/dev/examples/genes/index.html index 6d62cd2..caa9bd8 100644 --- a/dev/examples/genes/index.html +++ b/dev/examples/genes/index.html @@ -60,4 +60,4 @@ resize_to_layout!(f) vlines!(ax, start, color = (:gold, 0.5), linewidth = 0.5) vlines!(ax, stop, color = (:gold, 0.5), linewidth = 0.5) -f

Then we can save the figure as below.

save("figs/$(gene)-gene.png", f, px_per_unit = 4)
+f

Then we can save the figure as below.

save("figs/$(gene)-gene.png", f, px_per_unit = 4)
diff --git a/dev/examples/gtf/index.html b/dev/examples/gtf/index.html index f8afceb..fa5a3b1 100644 --- a/dev/examples/gtf/index.html +++ b/dev/examples/gtf/index.html @@ -9,4 +9,4 @@ h = ["seqnames", "source", "feature", "start", "end", "score", "strand", "phase", "info"] gencode = CSV.read("data/gencode/$(file)", DataFrame; delim = "\t", comment = "#", header = h)
Human genome build

The latest human genome assembly is GRCh38, but we use an annotation with coordinates from the older version (GRCh37), because a lot of the GWAS results are shared in GRCh37 genomic coordinates. Make sure to use the matching human genome build when visualizing your results.

The ninth column of a GTF file contains rich information about features, so we can parse this column.

GeneticsMakie.parsegtf!(gencode)
Chromosome names

Chromosome names are munged to not contain “chr” prefix, and their type is String, since there could be non-numerical chromosome names, such as sex chromosomes and mitochondrial genome.

To reduce memory intake, we can also subset gencode to most commonly used columns in downstream analyses.

select!(gencode, :seqnames, :feature, :start, :end, :strand, :gene_id, :gene_name, :gene_type, :transcript_id)

To further reduce memory intake, we can instead store and load GENCODE annotation as an Arrow file. 

Arrow.write("data/gencode/$(splitext(file)[1]).arrow", gencode)
 gencode = Arrow.Table("data/gencode/$(splitext(file)[1]).arrow")|> DataFrame

Other transcriptome annotations, such as one from RefSeq, can be used for plotting functions as long as they contain the above columns with the right column names.

Once gencode is ready, we can look up where a gene is on the human genome.

GeneticsMakie.findgene("RBFOX1", gencode)
-GeneticsMakie.findgene("ENSG00000078328", gencode)
Gene names

Make sure to use the correct gene name in case the gene cannot be found. The latest gene names can be looked up in databases such as GeneCards.

+GeneticsMakie.findgene("ENSG00000078328", gencode)
Gene names

Make sure to use the correct gene name in case the gene cannot be found. The latest gene names can be looked up in databases such as GeneCards.

diff --git a/dev/examples/gwas/index.html b/dev/examples/gwas/index.html index c966ae5..f94c8ca 100644 --- a/dev/examples/gwas/index.html +++ b/dev/examples/gwas/index.html @@ -59,4 +59,4 @@ rowgap!(f.layout, 1, 0) rowgap!(f.layout, 2, 5) resize_to_layout!(f) -f

+f

diff --git a/dev/examples/isoforms/index.html b/dev/examples/isoforms/index.html index 58dbf39..227d37d 100644 --- a/dev/examples/isoforms/index.html +++ b/dev/examples/isoforms/index.html @@ -40,4 +40,4 @@ GeneticsMakie.labelgenome(f[1, 1, Bottom()], chr, range1, range2) rowsize!(f.layout, 1, rs) resize_to_layout!(f) -f

+f

diff --git a/dev/examples/locus/index.html b/dev/examples/locus/index.html index 1fb759c..9b7092b 100644 --- a/dev/examples/locus/index.html +++ b/dev/examples/locus/index.html @@ -86,4 +86,4 @@ vlines!(axs[i], stop, color = (:gold, 0.5), linewidth = 0.5) end resize_to_layout!(f) -f

By using Makie.jl's layout tools, it becomes easy to draw additional tracks. For example, in a separate track, the variants could be colored or could have varying sizes depending on their minor allele frequency. In another example, the variants could be colored based on their inclusion in a credible set post-fine-mapping.

Plotting the intersection of SNPs, not the union

GeneticsMakie.plotlocus! plots only the variants that are present in the reference panel, when the ld keyword argument is specified. Although SNPs that are missing in the reference panel could be plotted differently (e.g. with varying transparency and shape), GeneticsMakie.jl is designed to visualize 100s of phenotypes simultaneously in which case such discrepancy is hard to tell and is confusing. Hence, for more direct comparison of loci across phenotypes, only the variants that are found in the reference panel are shown.

Extremely small P values

There are several GWAS loci that harbor extremely small P values, in which cases the P values will be clamped to the smallest floating point number. Such cases are going to be more common in phenotypes that are reaching saturation in terms of GWAS discovery (e.g. height). In those cases, it is more commonplace to observe allelic heterogneity, and it might be more appropriate to plot alternative measures of strength of association (e.g. Z scores).

Patterns of LD

Oftentimes, chunks of LD blocks hug a single or multiple gene boundaries.

Covering the entire genome

Visualizing 1,500 genomic regions with 2 Mb window will more or less cover the entire human genome. Note that empirically speaking, the probability of an arbitrary 2 Mb window harboring at least one genome-wide significant hit across multiple phenotypes is higher than not harboring any significant association.

Phenome-scale LocusZoom

To visualize 100s of phenotypes simultaneously, summary statistics or other relevant genomic annotations should be converted to memory friendly Arrow.jl or Parquet.jl files.

+f

By using Makie.jl's layout tools, it becomes easy to draw additional tracks. For example, in a separate track, the variants could be colored or could have varying sizes depending on their minor allele frequency. In another example, the variants could be colored based on their inclusion in a credible set post-fine-mapping.

Plotting the intersection of SNPs, not the union

GeneticsMakie.plotlocus! plots only the variants that are present in the reference panel, when the ld keyword argument is specified. Although SNPs that are missing in the reference panel could be plotted differently (e.g. with varying transparency and shape), GeneticsMakie.jl is designed to visualize 100s of phenotypes simultaneously in which case such discrepancy is hard to tell and is confusing. Hence, for more direct comparison of loci across phenotypes, only the variants that are found in the reference panel are shown.

Extremely small P values

There are several GWAS loci that harbor extremely small P values, in which cases the P values will be clamped to the smallest floating point number. Such cases are going to be more common in phenotypes that are reaching saturation in terms of GWAS discovery (e.g. height). In those cases, it is more commonplace to observe allelic heterogneity, and it might be more appropriate to plot alternative measures of strength of association (e.g. Z scores).

Patterns of LD

Oftentimes, chunks of LD blocks hug a single or multiple gene boundaries.

Covering the entire genome

Visualizing 1,500 genomic regions with 2 Mb window will more or less cover the entire human genome. Note that empirically speaking, the probability of an arbitrary 2 Mb window harboring at least one genome-wide significant hit across multiple phenotypes is higher than not harboring any significant association.

Phenome-scale LocusZoom

To visualize 100s of phenotypes simultaneously, summary statistics or other relevant genomic annotations should be converted to memory friendly Arrow.jl or Parquet.jl files.

diff --git a/dev/examples/loops/index.html b/dev/examples/loops/index.html index 51611a9..e766d4f 100644 --- a/dev/examples/loops/index.html +++ b/dev/examples/loops/index.html @@ -112,4 +112,4 @@ vlines!(axs[i], stop, color = (:gold, 0.5), linewidth = 0.5) end resize_to_layout!(f) -f

As with the LocusZoom plots, by using Makie.jl's layout tools, it becomes easy to draw additional tracks. For example, in a separate track, we can include chromatin interactions present in other samples. In another example, we can include interactions found through other sequencing methods.

+f

As with the LocusZoom plots, by using Makie.jl's layout tools, it becomes easy to draw additional tracks. For example, in a separate track, we can include chromatin interactions present in other samples. In another example, we can include interactions found through other sequencing methods.

diff --git a/dev/examples/peaks/index.html b/dev/examples/peaks/index.html index df2ab6b..ef7f951 100644 --- a/dev/examples/peaks/index.html +++ b/dev/examples/peaks/index.html @@ -195,4 +195,4 @@ resize_to_layout!(f) f save("ss4.png",f, px_per_unit = 4) -end

+end

diff --git a/dev/examples/summary/index.html b/dev/examples/summary/index.html index c8f2430..9079e91 100644 --- a/dev/examples/summary/index.html +++ b/dev/examples/summary/index.html @@ -20,4 +20,4 @@ GeneticsMakie.findclosestgene(loci, gencode; start = true) # closest gene from gene start site GeneticsMakie.findclosestgene(loci, gencode; proteincoding = true) # closest "protein-coding" gene

To reduce memory intake, we can store and load GWAS summary statistics as Arrow files. 

for (i, key) in enumerate(keys(gwas))
     Arrow.write("data/gwas/$(key).arrow", dfs[i])
-end
+end diff --git a/dev/examples/twas/index.html b/dev/examples/twas/index.html index 0744ba4..1bd72b7 100644 --- a/dev/examples/twas/index.html +++ b/dev/examples/twas/index.html @@ -23,4 +23,4 @@ Label(f[1, 1, Top()], text = "SCZ (2022): SCHEMA", fontsize = 8) rowsize!(f.layout, 1, 50) resize_to_layout!(f) -f

+f

diff --git a/dev/index.html b/dev/index.html index 2584b03..c54641b 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · GeneticsMakie.jl

GeneticsMakie

The goal of GeneticsMakie.jl is to permit seamless data visualization and exploratory data analysis of the human genome within the larger Julia data science and OpenMendel ecosystems. The package provides convenient wrapper functions for wrangling genetic association results and plotting them using Makie.jl. Every component of a figure can be easily customized and extended, and the package generates high-quality, publication-ready figures.

"mhc"

Getting started

Please peruse the documentations of Makie.jl, CSV.jl, DataFrames.jl, and SnpArrays.jl. Familiarity with these packages will allow visualization of most types of genetic and genomic data. Makie.jl's default layout tools are particularly useful for plotting different genetic and genomic data modalities as separate layers.

An usage case

If you have run a genome-wide association study (GWAS) at the variant-level, and you would like to eyeball genome-wide significant loci across hundreds of phenotypes, then you are in the right place.

+Home · GeneticsMakie.jl

GeneticsMakie

The goal of GeneticsMakie.jl is to permit seamless data visualization and exploratory data analysis of the human genome within the larger Julia data science and OpenMendel ecosystems. The package provides convenient wrapper functions for wrangling genetic association results and plotting them using Makie.jl. Every component of a figure can be easily customized and extended, and the package generates high-quality, publication-ready figures.

"mhc"

Getting started

Please peruse the documentations of Makie.jl, CSV.jl, DataFrames.jl, and SnpArrays.jl. Familiarity with these packages will allow visualization of most types of genetic and genomic data. Makie.jl's default layout tools are particularly useful for plotting different genetic and genomic data modalities as separate layers.

An usage case

If you have run a genome-wide association study (GWAS) at the variant-level, and you would like to eyeball genome-wide significant loci across hundreds of phenotypes, then you are in the right place.

diff --git a/dev/objects.inv b/dev/objects.inv index 6356b0b..f1df316 100644 Binary files a/dev/objects.inv and b/dev/objects.inv differ