From 7bb131b88a4005575fc6ec60a519251fc9bd3f91 Mon Sep 17 00:00:00 2001 From: Minsoo Date: Mon, 17 Jan 2022 23:16:03 -0800 Subject: [PATCH] add docs --- docs/src/examples/locus.md | 150 +++++++++++++++++++++++ docs/src/examples/summary.md | 3 +- docs/src/figs/JAZF1-locuszoom-ld-snp.png | Bin 0 -> 218511 bytes docs/src/figs/JAZF1-locuszoom-ld.png | Bin 0 -> 216996 bytes docs/src/figs/JAZF1-locuszoom.png | Bin 0 -> 209262 bytes 5 files changed, 152 insertions(+), 1 deletion(-) create mode 100644 docs/src/figs/JAZF1-locuszoom-ld-snp.png create mode 100644 docs/src/figs/JAZF1-locuszoom-ld.png create mode 100644 docs/src/figs/JAZF1-locuszoom.png diff --git a/docs/src/examples/locus.md b/docs/src/examples/locus.md index e69de29..18e8837 100644 --- a/docs/src/examples/locus.md +++ b/docs/src/examples/locus.md @@ -0,0 +1,150 @@ +# Plotting LocusZooom + +After [Parsing GENCODE](@ref) and [Munging summary statistics](@ref), +we can now put the pieces together to draw the backbone of a LocusZoom plot. + +```julia +gene = "JAZF1" +chr, start, stop = GeneticsMakie.findgene(gene, gencode) +range1 = start - 1e6 +range2 = stop + 1e6 + +n = length(df) +titles = ["Height (Yengo et al. 2018)", "Weight (Yengo et al. 2018)"] +f = Figure(resolution = (306, 792)) +axs = [Axis(f[i, 1]) for i in 1:(n + 1)] +for i in 1:n + GeneticsMakie.plotlocus!(axs[i], chr, range1, range2, df[i]) + rowsize!(f.layout, i, 30) + lines!(axs[i], [range1, range2], fill(-log(10, 5e-8), 2), color = (:purple, 0.5), linewidth = 0.5) + Label(f[i, 1, Top()], "$(titles[i])", textsize = 6, halign = :left, padding = (7.5, 0, -5, 0)) +end +rs = GeneticsMakie.plotgenes!(axs[n + 1], chr, range1, range2, gencode; height = 0.1) +rowsize!(f.layout, n + 1, rs) +GeneticsMakie.labelgenome(f[n + 1, 1, Bottom()], chr, range1, range2) +Colorbar(f[1:n, 2], limits = (0, 1), ticks = 0:1:1, height = 20, + colormap = (:gray60, :red2), label = "LD", ticksize = 0, tickwidth = 0, + tickalign = 0, ticklabelsize = 6, flip_vertical_label = true, + labelsize = 6, width = 5, spinewidth = 0.5) +Label(f[1:n, 0], text = "-log[p]", textsize = 6, rotation = pi / 2) +rowgap!(f.layout, 5) +colgap!(f.layout, 5) +for i in 1:(n + 1) + vlines!(axs[i], start, color = (:gold, 0.5), linewidth = 0.5) + vlines!(axs[i], stop, color = (:gold, 0.5), linewidth = 0.5) +end +resize_to_layout!(f) +f +``` +![](../figs/JAZF1-locuszoom.png) + +`GeneticsMakie.plotlocus!` returns a straightforward scatter plot by default. + +To color variants by linkage disequilibrium (LD), we need a reference panel. If we already have +one, we can use [__SnpArrays.jl__](https://openmendel.github.io/SnpArrays.jl/latest/) to +read in PLINK bed files. If not, we can download one as below. For the time being, +we only download and convert a single chromosome. + +```julia +using SnpArrays +# Download 1000 Genomes data for a single chromosome +beagle = "http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a" +url = joinpath(beagle, "b37.vcf/chr$(chr).1kg.phase3.v5a.vcf.gz") +vcf = basename(url) +isdir("data/1kg") || mkdir("data/1kg") +isfile("data/1kg/$(vcf)") || download(url, "data/1kg/$(vcf)") +# Convert vcf file to plink bed file (this step takes a while) +isfile("data/1kg/$(replace(vcf, ".vcf.gz" => ".bed"))") || vcf2plink("data/1kg/$(vcf)", "data/1kg/$(replace(vcf, ".vcf.gz" => ""))") +# Download sample metadata +url = joinpath(beagle, "/sample_info/integrated_call_samples_v3.20130502.ALL.panel") +meta = basename(url) +isfile("data/1kg/$(meta)") || download(url, "data/1kg/$(meta)") +# Subset data to the genomic region of interest and European samples +kgp = SnpData("data/1kg/$(replace(vcf, ".vcf.gz" => ""))") +meta = CSV.read("data/1kg/$(meta)", DataFrame) +eur = meta.sample[meta.super_pop .== "EUR"] +colinds = findall((kgp.snp_info.position .>= range1) .& (kgp.snp_info.position .<= range2)) +rowinds = findall(in(eur), kgp.person_info.iid) +file = replace(vcf, ".vcf.gz" => ".eur") +SnpArrays.filter(kgp, rowinds, colinds; des = "data/1kg/$(file)") +# Apply minor allele frequency > 0.05 filter +kgp = SnpData("data/1kg/$(file)") +colinds = SnpArrays.filter(kgp.snparray; min_maf = 0.05)[2] +file = file * ".maf0.05" +SnpArrays.filter(kgp, trues(size(kgp)[1]), colinds; des = "data/1kg/$(file)") +kgp = SnpData("data/1kg/$(file)") +``` + +```julia +f = Figure(resolution = (306, 792)) +axs = [Axis(f[i, 1]) for i in 1:(n + 1)] +for i in 1:n + GeneticsMakie.plotlocus!(axs[i], chr, range1, range2, df[i], ld = kgp) + rowsize!(f.layout, i, 30) + lines!(axs[i], [range1, range2], fill(-log(10, 5e-8), 2), color = (:purple, 0.5), linewidth = 0.5) + Label(f[i, 1, Top()], "$(titles[i])", textsize = 6, halign = :left, padding = (7.5, 0, -5, 0)) +end +rs = GeneticsMakie.plotgenes!(axs[n + 1], chr, range1, range2, gencode; height = 0.1) +rowsize!(f.layout, n + 1, rs) +GeneticsMakie.labelgenome(f[n + 1, 1, Bottom()], chr, range1, range2) +Colorbar(f[1:n, 2], limits = (0, 1), ticks = 0:1:1, height = 20, + colormap = (:gray60, :red2), label = "LD", ticksize = 0, tickwidth = 0, + tickalign = 0, ticklabelsize = 6, flip_vertical_label = true, + labelsize = 6, width = 5, spinewidth = 0.5) +Label(f[1:n, 0], text = "-log[p]", textsize = 6, rotation = pi / 2) +rowgap!(f.layout, 5) +colgap!(f.layout, 5) +for i in 1:(n + 1) + vlines!(axs[i], start, color = (:gold, 0.5), linewidth = 0.5) + vlines!(axs[i], stop, color = (:gold, 0.5), linewidth = 0.5) +end +resize_to_layout!(f) +f +``` +![](../figs/JAZF1-locuszoom-ld.png) + +We can color variants by LD with the index/sentinel SNP by using the `ld` keyword argument. + +```julia +f = Figure(resolution = (306, 792)) +axs = [Axis(f[i, 1]) for i in 1:(n + 1)] +for i in 1:n + GeneticsMakie.plotlocus!(axs[i], chr, range1, range2, df[i], ld = (kgp, "rs508347")) + rowsize!(f.layout, i, 30) + lines!(axs[i], [range1, range2], fill(-log(10, 5e-8), 2), color = (:purple, 0.5), linewidth = 0.5) + Label(f[i, 1, Top()], "$(titles[i])", textsize = 6, halign = :left, padding = (7.5, 0, -5, 0)) +end +rs = GeneticsMakie.plotgenes!(axs[n + 1], chr, range1, range2, gencode; height = 0.1) +rowsize!(f.layout, n + 1, rs) +GeneticsMakie.labelgenome(f[n + 1, 1, Bottom()], chr, range1, range2) +Colorbar(f[1:n, 2], limits = (0, 1), ticks = 0:1:1, height = 20, + colormap = (:gray60, :red2), label = "LD", ticksize = 0, tickwidth = 0, + tickalign = 0, ticklabelsize = 6, flip_vertical_label = true, + labelsize = 6, width = 5, spinewidth = 0.5) +Label(f[1:n, 0], text = "-log[p]", textsize = 6, rotation = pi / 2) +rowgap!(f.layout, 5) +colgap!(f.layout, 5) +for i in 1:(n + 1) + vlines!(axs[i], start, color = (:gold, 0.5), linewidth = 0.5) + vlines!(axs[i], stop, color = (:gold, 0.5), linewidth = 0.5) +end +resize_to_layout!(f) +f +``` +![](../figs/JAZF1-locuszoom-ld-snp.png) +We can also color variants by LD with the same SNP by using the `ld` keyword argument. + +By using [__Makie.jl__](https://makie.juliaplots.org/stable/)'s layout tools, +it becomes easy to draw additional tracks. For example, in a separate track, +the variants could be colored or 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. + +!!! note "Plotting the intersection of SNPs, not 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__](https://github.com/mmkim1210/GeneticsMakie.jl) is designed to + visualize 100s of phenotypes simultaneously in which case such discrepancy is hard to tell. + Hence, for more direct comparison of loci across phenotypes, only the variants that are found in the + reference panel are shown. \ No newline at end of file diff --git a/docs/src/examples/summary.md b/docs/src/examples/summary.md index a3aece4..a7f68e3 100644 --- a/docs/src/examples/summary.md +++ b/docs/src/examples/summary.md @@ -17,7 +17,8 @@ for key in keys(gwas) end ``` -To harmonize summary statistics, we run a single command. +To harmonize summary statistics, we run a single command. For all downstream plotting +functions, we note that GWAS summary statistics should be pre-harmonized. ```julia GeneticsMakie.mungesumstats!(df) ``` diff --git a/docs/src/figs/JAZF1-locuszoom-ld-snp.png b/docs/src/figs/JAZF1-locuszoom-ld-snp.png new file mode 100644 index 0000000000000000000000000000000000000000..b9fd6fc41e3e2377b68fdb48451c22e6a1852680 GIT binary patch literal 218511 zcmeFZWmJ}H7cGp4g&=}R3MeUvbVwsUba#V*v~;(qln6*S2#9nyQi_1^(A|i1ch|RY z?{|Ff8RMKY&N#o%y&YQy!X4Lrt+nQyYpy3iPDT_Hod_KT1qD-F455I6g7ymqz zBVYB-jgWA=48jJz_$%&wWPGH|$va#N?wwbrYq}P8T)YUP3~#}W!0|cDdC>BoFHuUZ zZqi@*=ez26)h=%s1x2vrVIkVzuih9vfBEk(b-ZoB`TISTxNF{2|9r)E8&&e3uT1dJ zqW<}+oBscu5LV*Hi0;s0*il*gjrH{@QsQuNaryZAu8&oO7>-GU z6MVS2y`61mZfd&p;rJqc zYRiyKK5xVl*gSIY)24g!7*;7Z;aCKdi3SR%ZGLG0K);lH0d$ zoA;EzpooZy;(qq*rjL+0=a-4b#LL)N6zet^L4~W~G3w+Fm>G8_#D4npFdE6q!lII| z)jG2k{D5nFDIp!AG*EtlQ=?CdPswQEhy&2*7%KY#u#EM!Vo;4&Y~&Cbpq ztPWFAQBe57gwe<}RG?c>5F8zi-^j${xN6cL>a;riovD4cHJo_ti^W(uGse#8PFK7l z7Yj>oSC=e9biAT^MqdyitI_WUZ;d?1tv^i|jW>OMR903VFVj#``Xk7F^*Ag>Us&Vtj%q&6i^RaF(= z#pzB_QIYTc)YMc0-=nP-vUo@B%LT@35-O=ZnvX}Yd7e6_ZAU&)#gT`1Chf#!{Tl^) zZ?Pw>6MdmGk%Bx+DK0)fhf(Pmh&$KHS{g3V#MUvv=8s5OIZ zYisA)9?a7W@c$M&FfhQ+&yT<*Ajnk6l6vwavaO*(kerBF|Gi3%#jJ2CYfx=?$WNa? zb_<;rKk9g#)^qIyzwa*fu@eQ=X;@k^925+ELXdO0?Z5UA)v0sm!sg=U9<17Hi+pnD z&K;2(vpYv_`>V{kc%-E3tE*aaa^3GGufkFkQtqmizmbe)!0riTtTR#KH|~5u-qzWv zLKo>fH!?C3i1&1PylQ75*-K~P3Kl7^7-D*QIw>jXSSSEdY`>&H&h36^9msJZ3+ts> z?|F8-w^CJD?%^^M#Hkj==enC&M!cOdK0b~maB96h+nOd4L{6@$ImYL<&x8>k5mD;6 zrqOpy=6R^6pb%DDd**do{qUWC#vhbd2lo}$VjyrSumLN=E^S^uh zwli5kS6e$VA%WMh4L48aq?y=?;LfWd*30Q}yA+C-OMu@4Vr(9DkVl>C@e`ZEOk=YfW|{>D%RG z8sl;7=Cig(H^|&SGZJ#yQVx}zL2&3)*)|n!r%OgxKAc4|*6;4`ms=~68N;UUHes7- z3As2w*?<^*X<#rjGqcl5lWd(Bbi6gZ(3Kn#5D+Sn-V#Da#%-UeMt=tbgV%92roP_G z#>VE=t1r{cj9QgUuZIgscpSyjdyhNaU`b0$+4z&aP7k+2DMUv{NB7sp4mPJ7Jv}|? zB0snl{M_2viH8{VzIvl_>eia_mZ(xqu-fThF{#Rnrj^V?3iAqh)7&g*QF$gCleGvo z*Ig4|y#wuE7e5A;amF6?3J|xsbfPypFCetE2gHJ#}?;tC}s} zii!#~dPfI`L^jjjEX53rC;gl)+owl6;WV<2PEHjiCB$5|vm5mnumc=?^(uoAFYe#J zkBoY{GE$O(Am7^BQinZxp!22#_KZhDca|cJuO1E#4w1=P#gr${%Z4`@Nv>^$?tNdS z;jthSOxCy@%dE!5#kuUvFWFa{_I<5A-9`r0T3ZG$HU|-hhld+?C9O?QKf(*Ft*xDM zSs;m(G8*MzwGz|U|5M|#v)gw8L1zzZcAQ@!Z(-S=IGe!d<~W$E4x93m7^S15qqn#B zM(u%)scBa1n(3QZ1I%`^=hlneDW5)lIy^j-PZMEh;w;Y0^!MlG6>=CqnT-AJq1Y9=24K9sLL_I`z3v~ON5iPxpK zv$Jzo^kBkiYG5eKE|?16o^TzK?2rWk5mDQ~^t>AX*>2zXsq%|st9!^|$tZf}Q6e%j z-6^V?ll550$$z|dr!ah1aR#8^AafoAWDVBjvOhXg`+$G|rLG4^NL6>SLt2oXorCyh zY($nxYpS{)hD!D3L;3Ff0yH!k=?vYyA2^YASbC)gjoFI@R{ArK9jglFK}y zv9WQe*zDR`?Wv}8pi}!(f2$*&vnuQuRgz)9Tb1=E-_}&6O`7~#mYq&=Xm_{VdLYMrH6ueq z8!j1Y*(CS5w#en><3g*6Tl`I4Uwb{zmJD!Iaw2Xy8&uSy1?fnT4R6){*NC$O6kng`toyTi$S~Eet%Vc z9YG_T#FlL|@3S(tF+X1pLGa^;P^{GcWs30&`uOqnDrS}I9-XSipPvDcQRpHeUz#_4 zxIccS{_s!Wb%ybFj(9Jcy(^RD7GtEm&JFDIGa&+aH_;HK7GoUw8i6;W3Tn2|v87{} zyibN9zj2fK>@mOLpMCQ5>A-@H*~7wD>Kweh3I5kH2=3myM-h!wR8%zXh!aL=e{*E7 zeX8YlezxIN(SJ>Y2vxF#ovCT!aUmOP3T`yDL_>yyq)A6IBi4%8y}bgjGvw~a{r!Dk zMiFUcB_-<_6Cz?Yj!hbfr%^_f86z`af4$hk(dW`baa2_ z%itSOrC4)Y`3@M>3I%objh@+GoO_T%LGa}g#-xpuU`SY*_{PbJW4qLw{&k?e#@ge= zz`y`Ar*`wlZN%<2l96-Ym-k@Y?r#oF+p!!j^!C@%){c}g^cb=un~ANT&_k1Fg;4b&CC&ura@zd)`SBx0U)#m`L4CkuUfvoPRU^Ke z^d0+6___q=uQ}huzqg8z``{0i6JB5yw?^o;^X*vFpouaO;?zl3pvBU8REv@P6cU9>(ucD$ZYyEud4>4Egae{{WTth=c zM@L6cuwlL zib^EbFK*n(?sV$nj0{%G+PIi}GAfA?Cd`*doAz;||yf{198!4&OQun2;d^SG% z1@T2ChuAF@U}uhM!QkK^5uVYjSN&B#&E@3o@}2g)4PJvAL9jtt_RWcnm6J%OUWT%D zS=+=YvstD4^R>U(b5D#})t`O8LAJZt^MWq&4k_;;07(E}M@L8F8)%AC9tRWWXD8Bb z(knaj@m9?(Eq*>edAjwJ?SM%?dpv(Rq7SQOUQk=^eq_s^Oh!nUMHc+)Xn)KqV6i=x zm6tTc#ox`gqqB3Kf4L;a+SF}*;+wHOdOm_4kCWpj1{T)EynLmf1JZd`PE7`K7+^wd z=|t|xqbEgb?Nln_($egFaQ%ZPbaaRAp4QfdJ=AF^9}VIH2S!F>{XY%{FyDw)&4R3q z*-yyp%*6RHN-qW;Xpqgr#DFnDld|tFCyr(^-*7u|Sah_iqT&k&zM;|K;oXg?WdHTa zTFnJv_M>>i;2T(^r+~D-Ic?~anGGCn%~VNo9N%e-+FmKBmx*CYsjhaN{^8@>YFlab zN&c=Ff*xu6C|$l~h5`~CEj|6}51-rE*w|1OBam~Zrl!C#T01%tAzQ=a1})nO$Ne<@;=NysXdvcvVPmUMd)zmPT

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