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ag1k_R_example.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Worked examples with Ag 1000 Genomes data\n",
"\n",
"### Preamble"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"library(h5)\n",
"library(ggplot2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"R version 3.2.2 (2015-08-14)\n",
"Platform: x86_64-pc-linux-gnu (64-bit)\n",
"Running under: Ubuntu 14.04.3 LTS\n",
"\n",
"locale:\n",
" [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C \n",
" [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 \n",
" [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 \n",
" [7] LC_PAPER=en_US.UTF-8 LC_NAME=C \n",
" [9] LC_ADDRESS=C LC_TELEPHONE=C \n",
"[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C \n",
"\n",
"attached base packages:\n",
"[1] stats graphics grDevices utils datasets methods base \n",
"\n",
"other attached packages:\n",
"[1] ggplot2_1.0.1 h5_0.9.6 \n",
"\n",
"loaded via a namespace (and not attached):\n",
" [1] Rcpp_0.12.4 digest_0.6.8 MASS_7.3-44 \n",
" [4] IRdisplay_0.3.0.9000 plyr_1.8.3 repr_0.4 \n",
" [7] grid_3.2.2 jsonlite_0.9.17 gtable_0.1.2 \n",
"[10] magrittr_1.5 scales_0.3.0 evaluate_0.8 \n",
"[13] stringi_0.5-5 reshape2_1.4.1 uuid_0.1-2 \n",
"[16] IRkernel_0.4 proto_0.3-10 tools_3.2.2 \n",
"[19] stringr_1.0.0 munsell_0.4.2 colorspace_1.2-6 \n",
"[22] base64enc_0.1-3 rzmq_0.7.8 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sessionInfo()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# specify filename\n",
"fn <- \"/home/ag1000g.phase1.ar3.pass.3L.h5\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create the filehandle to access the data\n",
"fh <- h5file(fn, \"r\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'/3L/calldata/AD'</li>\n",
"\t<li>'/3L/calldata/DP'</li>\n",
"\t<li>'/3L/calldata/GQ'</li>\n",
"\t<li>'/3L/calldata/genotype'</li>\n",
"\t<li>'/3L/calldata/is_called'</li>\n",
"\t<li>'/3L/samples'</li>\n",
"\t<li>'/3L/variants/ABHet'</li>\n",
"\t<li>'/3L/variants/ABHom'</li>\n",
"\t<li>'/3L/variants/AC'</li>\n",
"\t<li>'/3L/variants/AF'</li>\n",
"\t<li>'/3L/variants/ALT'</li>\n",
"\t<li>'/3L/variants/AN'</li>\n",
"\t<li>'/3L/variants/ANN'</li>\n",
"\t<li>'/3L/variants/Accessible'</li>\n",
"\t<li>'/3L/variants/BaseCounts'</li>\n",
"\t<li>'/3L/variants/BaseQRankSum'</li>\n",
"\t<li>'/3L/variants/CHROM'</li>\n",
"\t<li>'/3L/variants/Coverage'</li>\n",
"\t<li>'/3L/variants/CoverageMQ0'</li>\n",
"\t<li>'/3L/variants/DP'</li>\n",
"\t<li>'/3L/variants/DS'</li>\n",
"\t<li>'/3L/variants/Dels'</li>\n",
"\t<li>'/3L/variants/FILTER_FS'</li>\n",
"\t<li>'/3L/variants/FILTER_HRun'</li>\n",
"\t<li>'/3L/variants/FILTER_HighCoverage'</li>\n",
"\t<li>'/3L/variants/FILTER_HighMQ0'</li>\n",
"\t<li>'/3L/variants/FILTER_LowCoverage'</li>\n",
"\t<li>'/3L/variants/FILTER_LowMQ'</li>\n",
"\t<li>'/3L/variants/FILTER_LowQual'</li>\n",
"\t<li>'/3L/variants/FILTER_NoCoverage'</li>\n",
"\t<li>'/3L/variants/FILTER_PASS'</li>\n",
"\t<li>'/3L/variants/FILTER_QD'</li>\n",
"\t<li>'/3L/variants/FILTER_ReadPosRankSum'</li>\n",
"\t<li>'/3L/variants/FILTER_RefN'</li>\n",
"\t<li>'/3L/variants/FILTER_RepeatDUST'</li>\n",
"\t<li>'/3L/variants/FS'</li>\n",
"\t<li>'/3L/variants/HRun'</li>\n",
"\t<li>'/3L/variants/HW'</li>\n",
"\t<li>'/3L/variants/HaplotypeScore'</li>\n",
"\t<li>'/3L/variants/HighCoverage'</li>\n",
"\t<li>'/3L/variants/HighMQ0'</li>\n",
"\t<li>'/3L/variants/InbreedingCoeff'</li>\n",
"\t<li>'/3L/variants/LOF'</li>\n",
"\t<li>'/3L/variants/LowCoverage'</li>\n",
"\t<li>'/3L/variants/LowMQ'</li>\n",
"\t<li>'/3L/variants/LowPairing'</li>\n",
"\t<li>'/3L/variants/MLEAC'</li>\n",
"\t<li>'/3L/variants/MLEAF'</li>\n",
"\t<li>'/3L/variants/MQ'</li>\n",
"\t<li>'/3L/variants/MQ0'</li>\n",
"\t<li>'/3L/variants/MQRankSum'</li>\n",
"\t<li>'/3L/variants/NDA'</li>\n",
"\t<li>'/3L/variants/NMD'</li>\n",
"\t<li>'/3L/variants/NoCoverage'</li>\n",
"\t<li>'/3L/variants/OND'</li>\n",
"\t<li>'/3L/variants/POS'</li>\n",
"\t<li>'/3L/variants/QD'</li>\n",
"\t<li>'/3L/variants/QUAL'</li>\n",
"\t<li>'/3L/variants/REF'</li>\n",
"\t<li>'/3L/variants/RPA'</li>\n",
"\t<li>'/3L/variants/RU'</li>\n",
"\t<li>'/3L/variants/ReadPosRankSum'</li>\n",
"\t<li>'/3L/variants/RefMasked'</li>\n",
"\t<li>'/3L/variants/RefN'</li>\n",
"\t<li>'/3L/variants/RepeatDUST'</li>\n",
"\t<li>'/3L/variants/RepeatMasker'</li>\n",
"\t<li>'/3L/variants/RepeatTRF'</li>\n",
"\t<li>'/3L/variants/STR'</li>\n",
"\t<li>'/3L/variants/VariantType'</li>\n",
"\t<li>'/3L/variants/is_snp'</li>\n",
"\t<li>'/3L/variants/num_alleles'</li>\n",
"\t<li>'/3L/variants/svlen'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item '/3L/calldata/AD'\n",
"\\item '/3L/calldata/DP'\n",
"\\item '/3L/calldata/GQ'\n",
"\\item '/3L/calldata/genotype'\n",
"\\item '/3L/calldata/is_called'\n",
"\\item '/3L/samples'\n",
"\\item '/3L/variants/ABHet'\n",
"\\item '/3L/variants/ABHom'\n",
"\\item '/3L/variants/AC'\n",
"\\item '/3L/variants/AF'\n",
"\\item '/3L/variants/ALT'\n",
"\\item '/3L/variants/AN'\n",
"\\item '/3L/variants/ANN'\n",
"\\item '/3L/variants/Accessible'\n",
"\\item '/3L/variants/BaseCounts'\n",
"\\item '/3L/variants/BaseQRankSum'\n",
"\\item '/3L/variants/CHROM'\n",
"\\item '/3L/variants/Coverage'\n",
"\\item '/3L/variants/CoverageMQ0'\n",
"\\item '/3L/variants/DP'\n",
"\\item '/3L/variants/DS'\n",
"\\item '/3L/variants/Dels'\n",
"\\item '/3L/variants/FILTER_FS'\n",
"\\item '/3L/variants/FILTER_HRun'\n",
"\\item '/3L/variants/FILTER_HighCoverage'\n",
"\\item '/3L/variants/FILTER_HighMQ0'\n",
"\\item '/3L/variants/FILTER_LowCoverage'\n",
"\\item '/3L/variants/FILTER_LowMQ'\n",
"\\item '/3L/variants/FILTER_LowQual'\n",
"\\item '/3L/variants/FILTER_NoCoverage'\n",
"\\item '/3L/variants/FILTER_PASS'\n",
"\\item '/3L/variants/FILTER_QD'\n",
"\\item '/3L/variants/FILTER_ReadPosRankSum'\n",
"\\item '/3L/variants/FILTER_RefN'\n",
"\\item '/3L/variants/FILTER_RepeatDUST'\n",
"\\item '/3L/variants/FS'\n",
"\\item '/3L/variants/HRun'\n",
"\\item '/3L/variants/HW'\n",
"\\item '/3L/variants/HaplotypeScore'\n",
"\\item '/3L/variants/HighCoverage'\n",
"\\item '/3L/variants/HighMQ0'\n",
"\\item '/3L/variants/InbreedingCoeff'\n",
"\\item '/3L/variants/LOF'\n",
"\\item '/3L/variants/LowCoverage'\n",
"\\item '/3L/variants/LowMQ'\n",
"\\item '/3L/variants/LowPairing'\n",
"\\item '/3L/variants/MLEAC'\n",
"\\item '/3L/variants/MLEAF'\n",
"\\item '/3L/variants/MQ'\n",
"\\item '/3L/variants/MQ0'\n",
"\\item '/3L/variants/MQRankSum'\n",
"\\item '/3L/variants/NDA'\n",
"\\item '/3L/variants/NMD'\n",
"\\item '/3L/variants/NoCoverage'\n",
"\\item '/3L/variants/OND'\n",
"\\item '/3L/variants/POS'\n",
"\\item '/3L/variants/QD'\n",
"\\item '/3L/variants/QUAL'\n",
"\\item '/3L/variants/REF'\n",
"\\item '/3L/variants/RPA'\n",
"\\item '/3L/variants/RU'\n",
"\\item '/3L/variants/ReadPosRankSum'\n",
"\\item '/3L/variants/RefMasked'\n",
"\\item '/3L/variants/RefN'\n",
"\\item '/3L/variants/RepeatDUST'\n",
"\\item '/3L/variants/RepeatMasker'\n",
"\\item '/3L/variants/RepeatTRF'\n",
"\\item '/3L/variants/STR'\n",
"\\item '/3L/variants/VariantType'\n",
"\\item '/3L/variants/is_snp'\n",
"\\item '/3L/variants/num_alleles'\n",
"\\item '/3L/variants/svlen'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. '/3L/calldata/AD'\n",
"2. '/3L/calldata/DP'\n",
"3. '/3L/calldata/GQ'\n",
"4. '/3L/calldata/genotype'\n",
"5. '/3L/calldata/is_called'\n",
"6. '/3L/samples'\n",
"7. '/3L/variants/ABHet'\n",
"8. '/3L/variants/ABHom'\n",
"9. '/3L/variants/AC'\n",
"10. '/3L/variants/AF'\n",
"11. '/3L/variants/ALT'\n",
"12. '/3L/variants/AN'\n",
"13. '/3L/variants/ANN'\n",
"14. '/3L/variants/Accessible'\n",
"15. '/3L/variants/BaseCounts'\n",
"16. '/3L/variants/BaseQRankSum'\n",
"17. '/3L/variants/CHROM'\n",
"18. '/3L/variants/Coverage'\n",
"19. '/3L/variants/CoverageMQ0'\n",
"20. '/3L/variants/DP'\n",
"21. '/3L/variants/DS'\n",
"22. '/3L/variants/Dels'\n",
"23. '/3L/variants/FILTER_FS'\n",
"24. '/3L/variants/FILTER_HRun'\n",
"25. '/3L/variants/FILTER_HighCoverage'\n",
"26. '/3L/variants/FILTER_HighMQ0'\n",
"27. '/3L/variants/FILTER_LowCoverage'\n",
"28. '/3L/variants/FILTER_LowMQ'\n",
"29. '/3L/variants/FILTER_LowQual'\n",
"30. '/3L/variants/FILTER_NoCoverage'\n",
"31. '/3L/variants/FILTER_PASS'\n",
"32. '/3L/variants/FILTER_QD'\n",
"33. '/3L/variants/FILTER_ReadPosRankSum'\n",
"34. '/3L/variants/FILTER_RefN'\n",
"35. '/3L/variants/FILTER_RepeatDUST'\n",
"36. '/3L/variants/FS'\n",
"37. '/3L/variants/HRun'\n",
"38. '/3L/variants/HW'\n",
"39. '/3L/variants/HaplotypeScore'\n",
"40. '/3L/variants/HighCoverage'\n",
"41. '/3L/variants/HighMQ0'\n",
"42. '/3L/variants/InbreedingCoeff'\n",
"43. '/3L/variants/LOF'\n",
"44. '/3L/variants/LowCoverage'\n",
"45. '/3L/variants/LowMQ'\n",
"46. '/3L/variants/LowPairing'\n",
"47. '/3L/variants/MLEAC'\n",
"48. '/3L/variants/MLEAF'\n",
"49. '/3L/variants/MQ'\n",
"50. '/3L/variants/MQ0'\n",
"51. '/3L/variants/MQRankSum'\n",
"52. '/3L/variants/NDA'\n",
"53. '/3L/variants/NMD'\n",
"54. '/3L/variants/NoCoverage'\n",
"55. '/3L/variants/OND'\n",
"56. '/3L/variants/POS'\n",
"57. '/3L/variants/QD'\n",
"58. '/3L/variants/QUAL'\n",
"59. '/3L/variants/REF'\n",
"60. '/3L/variants/RPA'\n",
"61. '/3L/variants/RU'\n",
"62. '/3L/variants/ReadPosRankSum'\n",
"63. '/3L/variants/RefMasked'\n",
"64. '/3L/variants/RefN'\n",
"65. '/3L/variants/RepeatDUST'\n",
"66. '/3L/variants/RepeatMasker'\n",
"67. '/3L/variants/RepeatTRF'\n",
"68. '/3L/variants/STR'\n",
"69. '/3L/variants/VariantType'\n",
"70. '/3L/variants/is_snp'\n",
"71. '/3L/variants/num_alleles'\n",
"72. '/3L/variants/svlen'\n",
"\n",
"\n"
],
"text/plain": [
" [1] \"/3L/calldata/AD\" \"/3L/calldata/DP\" \n",
" [3] \"/3L/calldata/GQ\" \"/3L/calldata/genotype\" \n",
" [5] \"/3L/calldata/is_called\" \"/3L/samples\" \n",
" [7] \"/3L/variants/ABHet\" \"/3L/variants/ABHom\" \n",
" [9] \"/3L/variants/AC\" \"/3L/variants/AF\" \n",
"[11] \"/3L/variants/ALT\" \"/3L/variants/AN\" \n",
"[13] \"/3L/variants/ANN\" \"/3L/variants/Accessible\" \n",
"[15] \"/3L/variants/BaseCounts\" \"/3L/variants/BaseQRankSum\" \n",
"[17] \"/3L/variants/CHROM\" \"/3L/variants/Coverage\" \n",
"[19] \"/3L/variants/CoverageMQ0\" \"/3L/variants/DP\" \n",
"[21] \"/3L/variants/DS\" \"/3L/variants/Dels\" \n",
"[23] \"/3L/variants/FILTER_FS\" \"/3L/variants/FILTER_HRun\" \n",
"[25] \"/3L/variants/FILTER_HighCoverage\" \"/3L/variants/FILTER_HighMQ0\" \n",
"[27] \"/3L/variants/FILTER_LowCoverage\" \"/3L/variants/FILTER_LowMQ\" \n",
"[29] \"/3L/variants/FILTER_LowQual\" \"/3L/variants/FILTER_NoCoverage\" \n",
"[31] \"/3L/variants/FILTER_PASS\" \"/3L/variants/FILTER_QD\" \n",
"[33] \"/3L/variants/FILTER_ReadPosRankSum\" \"/3L/variants/FILTER_RefN\" \n",
"[35] \"/3L/variants/FILTER_RepeatDUST\" \"/3L/variants/FS\" \n",
"[37] \"/3L/variants/HRun\" \"/3L/variants/HW\" \n",
"[39] \"/3L/variants/HaplotypeScore\" \"/3L/variants/HighCoverage\" \n",
"[41] \"/3L/variants/HighMQ0\" \"/3L/variants/InbreedingCoeff\" \n",
"[43] \"/3L/variants/LOF\" \"/3L/variants/LowCoverage\" \n",
"[45] \"/3L/variants/LowMQ\" \"/3L/variants/LowPairing\" \n",
"[47] \"/3L/variants/MLEAC\" \"/3L/variants/MLEAF\" \n",
"[49] \"/3L/variants/MQ\" \"/3L/variants/MQ0\" \n",
"[51] \"/3L/variants/MQRankSum\" \"/3L/variants/NDA\" \n",
"[53] \"/3L/variants/NMD\" \"/3L/variants/NoCoverage\" \n",
"[55] \"/3L/variants/OND\" \"/3L/variants/POS\" \n",
"[57] \"/3L/variants/QD\" \"/3L/variants/QUAL\" \n",
"[59] \"/3L/variants/REF\" \"/3L/variants/RPA\" \n",
"[61] \"/3L/variants/RU\" \"/3L/variants/ReadPosRankSum\" \n",
"[63] \"/3L/variants/RefMasked\" \"/3L/variants/RefN\" \n",
"[65] \"/3L/variants/RepeatDUST\" \"/3L/variants/RepeatMasker\" \n",
"[67] \"/3L/variants/RepeatTRF\" \"/3L/variants/STR\" \n",
"[69] \"/3L/variants/VariantType\" \"/3L/variants/is_snp\" \n",
"[71] \"/3L/variants/num_alleles\" \"/3L/variants/svlen\" "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list.datasets(fh)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DataSet 'samples' (765)\n",
"type: character\n",
"chunksize: NA\n",
"maxdim: 765"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sample names are stored in the first level after chromosome\n",
"samples <- fh[\"/3L/samples\"]\n",
"samples"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'AB0085-C'</li>\n",
"\t<li>'AB0087-C'</li>\n",
"\t<li>'AB0088-C'</li>\n",
"\t<li>'AB0089-C'</li>\n",
"\t<li>'AB0090-C'</li>\n",
"\t<li>'AB0091-C'</li>\n",
"\t<li>'AB0092-C'</li>\n",
"\t<li>'AB0094-C'</li>\n",
"\t<li>'AB0095-C'</li>\n",
"\t<li>'AB0097-C'</li>\n",
"\t<li>'AB0098-C'</li>\n",
"\t<li>'AB0099-C'</li>\n",
"\t<li>'AB0100-C'</li>\n",
"\t<li>'AB0101-C'</li>\n",
"\t<li>'AB0103-C'</li>\n",
"\t<li>'AB0104-C'</li>\n",
"\t<li>'AB0109-C'</li>\n",
"\t<li>'AB0110-C'</li>\n",
"\t<li>'AB0111-C'</li>\n",
"\t<li>'AB0112-C'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'AB0085-C'\n",
"\\item 'AB0087-C'\n",
"\\item 'AB0088-C'\n",
"\\item 'AB0089-C'\n",
"\\item 'AB0090-C'\n",
"\\item 'AB0091-C'\n",
"\\item 'AB0092-C'\n",
"\\item 'AB0094-C'\n",
"\\item 'AB0095-C'\n",
"\\item 'AB0097-C'\n",
"\\item 'AB0098-C'\n",
"\\item 'AB0099-C'\n",
"\\item 'AB0100-C'\n",
"\\item 'AB0101-C'\n",
"\\item 'AB0103-C'\n",
"\\item 'AB0104-C'\n",
"\\item 'AB0109-C'\n",
"\\item 'AB0110-C'\n",
"\\item 'AB0111-C'\n",
"\\item 'AB0112-C'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'AB0085-C'\n",
"2. 'AB0087-C'\n",
"3. 'AB0088-C'\n",
"4. 'AB0089-C'\n",
"5. 'AB0090-C'\n",
"6. 'AB0091-C'\n",
"7. 'AB0092-C'\n",
"8. 'AB0094-C'\n",
"9. 'AB0095-C'\n",
"10. 'AB0097-C'\n",
"11. 'AB0098-C'\n",
"12. 'AB0099-C'\n",
"13. 'AB0100-C'\n",
"14. 'AB0101-C'\n",
"15. 'AB0103-C'\n",
"16. 'AB0104-C'\n",
"17. 'AB0109-C'\n",
"18. 'AB0110-C'\n",
"19. 'AB0111-C'\n",
"20. 'AB0112-C'\n",
"\n",
"\n"
],
"text/plain": [
" [1] \"AB0085-C\" \"AB0087-C\" \"AB0088-C\" \"AB0089-C\" \"AB0090-C\" \"AB0091-C\"\n",
" [7] \"AB0092-C\" \"AB0094-C\" \"AB0095-C\" \"AB0097-C\" \"AB0098-C\" \"AB0099-C\"\n",
"[13] \"AB0100-C\" \"AB0101-C\" \"AB0103-C\" \"AB0104-C\" \"AB0109-C\" \"AB0110-C\"\n",
"[19] \"AB0111-C\" \"AB0112-C\""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"samples[1:20]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DataSet 'POS' (9.6432e+06)\n",
"type: integer\n",
"chunksize: 32768\n",
"maxdim: UNLIMITED\n",
"compression: H5Z_FILTER_DEFLATE"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# positions are stored in the \"variants\" group\n",
"fh[\"/3L/variants/POS\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Function to apply a function f, to values based on windows of positions\n",
"# NOTE: The final incomplete window is not included\n",
"windowed_analysis <- function(positions, values, f, win_size=100000, ...){\n",
"\n",
" windows <- seq(0, positions[attr(positions, \"dim\")], by=100000)\n",
" ix = findInterval(windows, positions[], all.inside=TRUE)\n",
" x = rep(NA, length(windows) - 1)\n",
"\n",
" for (i in 1:(length(ix)-1)){\n",
" start = ix[i]\n",
" stop = ix[i + 1]\n",
"\n",
" v = values[start:stop]\n",
" x[i] = f(v, ...)\n",
" }\n",
" return (data.frame(\"values\"=x,\n",
" \"start\"=windows[1:length(windows) -1],\n",
" \"stop\"=windows[2:length(windows)]))\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot mean mq0 in windows across the genome"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mq0_variants <- windowed_analysis(fh[\"/3L/variants/POS\"], \n",
" fh[\"/3L/variants/CoverageMQ0\"], \n",
" mean)\n",
"\n",
"# use the midpoint of the window instead of the start/stop\n",
"mq0_variants$position <- with(mq0_variants, (start + stop)/2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"# plot in windows\n",
"p <- ggplot(mq0_variants, aes(x=position, y=values)) + ylab(\"Coverage MQ0\")\n",
"p + geom_line() + theme(aspect.ratio=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot count of variants across the genome\n",
"\n",
"NOTE: Accessibility likely to differ between windows, so this is a combined measure of accessibility and diversity"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# as we are just taking the length use pos as the values\n",
"count_variants <- windowed_analysis(fh[\"/3L/variants/POS\"], \n",
" fh[\"/3L/variants/POS\"], \n",
" length)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"count_variants$position <- with(count_variants, (start + stop)/2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"# plot in windows\n",
"p <- ggplot(count_variants, aes(x=position, y=values)) + ylab(\"n variants\")\n",
"\n",
"p + geom_line() + theme(aspect.ratio=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate Fst between two populations\n",
"\n",
"For this we will need some metadata.\n",
"\n",
"We compute the allele counts and use these to calculate Fst. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The downloaded source packages are in\n",
"\t‘/tmp/RtmpKvunCW/downloaded_packages’\n"
]
}
],
"source": [
"install.packages(\"BEDASSLE\", repos=\"https://www.stats.bris.ac.uk/R\", \n",
" lib=\"/usr/local/lib/R/site-library\")\n",
"library(BEDASSLE)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read in the metadata file\n",
"metadata <- read.table(\"/home/samples.meta.txt\",\n",
" sep=\"\\t\", header=TRUE, stringsAsFactors=FALSE, quote=\"\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>index</th><th scope=col>ox_code</th><th scope=col>src_code</th><th scope=col>sra_sample_accession</th><th scope=col>population</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>0</td><td>AB0085-C</td><td>BF2-4</td><td>ERS223996</td><td>BFS</td></tr>\n",
"\t<tr><th scope=row>2</th><td>1</td><td>AB0087-C</td><td>BF3-3</td><td>ERS224013</td><td>BFM</td></tr>\n",
"\t<tr><th scope=row>3</th><td>2</td><td>AB0088-C</td><td>BF3-5</td><td>ERS223991</td><td>BFM</td></tr>\n",
"\t<tr><th scope=row>4</th><td>3</td><td>AB0089-C</td><td>BF3-8</td><td>ERS224031</td><td>BFM</td></tr>\n",
"\t<tr><th scope=row>5</th><td>4</td><td>AB0090-C</td><td>BF3-10</td><td>ERS223936</td><td>BFM</td></tr>\n",
"\t<tr><th scope=row>6</th><td>5</td><td>AB0091-C</td><td>BF3-12</td><td>ERS224065</td><td>BFM</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & index & ox_code & src_code & sra_sample_accession & population\\\\\n",
"\\hline\n",
"\t1 & 0 & AB0085-C & BF2-4 & ERS223996 & BFS\\\\\n",
"\t2 & 1 & AB0087-C & BF3-3 & ERS224013 & BFM\\\\\n",
"\t3 & 2 & AB0088-C & BF3-5 & ERS223991 & BFM\\\\\n",
"\t4 & 3 & AB0089-C & BF3-8 & ERS224031 & BFM\\\\\n",
"\t5 & 4 & AB0090-C & BF3-10 & ERS223936 & BFM\\\\\n",
"\t6 & 5 & AB0091-C & BF3-12 & ERS224065 & BFM\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" index ox_code src_code sra_sample_accession population\n",
"1 0 AB0085-C BF2-4 ERS223996 BFS\n",
"2 1 AB0087-C BF3-3 ERS224013 BFM\n",
"3 2 AB0088-C BF3-5 ERS223991 BFM\n",
"4 3 AB0089-C BF3-8 ERS224031 BFM\n",
"5 4 AB0090-C BF3-10 ERS223936 BFM\n",
"6 5 AB0091-C BF3-12 ERS224065 BFM"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"head(metadata[,1:5])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# calculate windowed fst using weir and hill\n",
"# This function could be generalized by passing a function that operated on dA and dB\n",
"calculate_windowed_fst <- function(data, chrom, popA_ix, popB_ix, window_size=100000, \n",
" start=NULL, stop=NULL){\n",
" \n",
" positions <- data[gsub(\"chrom\", chrom, \"/chrom/variants/POS\")]\n",
" \n",
" if (is.null(start)){\n",
" start = 0\n",
" }\n",
" \n",
" if (is.null(stop)){\n",
" stop <- positions[attr(positions, \"dim\")]\n",
" }\n",
" \n",
" windows <- seq(start, stop, by=window_size)\n",
" ix = findInterval(windows, positions[], all.inside=TRUE)\n",
" fst = rep(NA, length(windows) - 1)\n",
" time_load = rep(NA, length(windows) - 1)\n",
" time_stat = rep(NA, length(windows) - 1)\n",
"\n",
" for (i in 1:(length(ix)-1)){\n",
" begin = ix[i]\n",
" end = ix[i + 1]\n",
" \n",
" num_alleles = data[gsub(\"chrom\", chrom, \"/chrom/variants/num_alleles\")][begin:end]\n",
" ok <- num_alleles <= 2\n",
" \n",
" # don't calculate on a single SNP\n",
" if (sum(ok) < 2){\n",
" next()\n",
" }\n",
" \n",
" # only biallelic\n",
" load_time <- system.time({\n",
" dA <- data[gsub(\"chrom\", chrom, \"/chrom/calldata/genotype\")][begin:end, popA_ix, 1:2]\n",
" dA <- dA[ok,,]\n",
" \n",
" dB <- data[gsub(\"chrom\", chrom, \"/chrom/calldata/genotype\")][begin:end, popB_ix, 1:2]\n",
" dB <- dB[ok,,]\n",
" })\n",
" time_load[i] = load_time[\"elapsed\"][[1]]\n",
" \n",
" stat_time <- system.time({\n",
" \n",
" allele_counts = matrix(NA, nrow=2, ncol=sum(ok))\n",
" allele_counts[1,] = apply(dA, MARGIN=1, FUN=function(x) { sum(x > 0)})\n",
" allele_counts[2,] = apply(dB, MARGIN=1, FUN=function(x) { sum(x > 0)})\n",
" \n",
" alleles_typed = matrix(NA, nrow=2, ncol=sum(ok))\n",
" alleles_typed[1,] = apply(dA, MARGIN=1, FUN=function(x) { sum(x >= 0)})\n",
" alleles_typed[2,] = apply(dB, MARGIN=1, FUN=function(x) { sum(x >= 0)})\n",
" \n",
" fst[i] = calculate.pairwise.Fst(allele.counts=allele_counts,\n",
" sample.sizes=alleles_typed)\n",
"\n",
" })\n",
" time_stat[i] = stat_time[\"elapsed\"][[1]]\n",
" }\n",
" \n",
" return (data.frame(\"Fst\"=fst,\n",
" \"start\"=windows[1:length(windows) -1],\n",
" \"stop\"=windows[2:length(windows)],\n",
" \"time_load\"=time_load,\n",
" \"time_stat\"=time_stat))\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# find the indices of the Burkina Faso S and M samples\n",
"bfs_ix = which(metadata$population == \"BFS\")\n",
"bfm_ix = which(metadata$population == \"BFM\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# slow ish: ~11 minutes for the who chromosome arm\n",
"fst <- calculate_windowed_fst(fh, \"3L\", bfs_ix, bfm_ix, start=NULL, stop=NULL)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fst$position <- with(fst, (start + stop)/2)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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TUoWSzlbEhyE5xpBsi5mQLi3ibEV8GJJjDMm2mAmpZpKzFfFhSI4xJNtiJqRrkO5sTbwYkmMMybaYCekmbHK2Jl4MyTGGZFvMhHQHVjpbEy+G5BhDsi1mQnoAvzlbEy+G5BhDsi1mQnoU852tiRdDcowh2RYzIT2N752tiRdDcowh2RYzIb2Aic7WxIshOcaQbIuZkF4TG0WfITnGkGyLmZDewXBna+LFkBxjSLbFTEhdMNDZmngxJMcYkm0xE1JPfOxsTbwYkmMMybaYCWkAUp2tiRdDcowh2RYzIQ3Du87WxIshOcaQbIuZkMbgdWdr4sWQHGNItsVMSBPworM18WJIjjEk22ImpBl4xtmaeDEkxxiSbTke0pkDoR3ST5rfF6NFmGdG6rDQfDyO6UdkZ3jwkOz8DpzOEJ7h4YPC89NPyM7wwD/C8zuhO/y1yfGQMv4MbY9+3Pz+Ox4K88xIHRKaj8c/+gHZGe4Rnt+fp84Iz/DAXtn57dOPys7wz8PC8zuq73M2gxwPKdJNuxW409lrqxc37Rzjpp1tMRPSRjRwtiZeDMkxhmRbzISUjnrO1sSLITnGkGyLmZC0PLWcrYkXQ3KMIdkWOyElX+psTbwYkmMMybbYCalkBWdr4sWQHGNItsVOSCklnK2JF0NyjCHZFjshVS3kbE28GJJjDMm22AmpToKzNfFiSI4xJNtiJ6T62OFsVTwYkmMMybbYCakh1jtbFQ+G5BhDsi12QmqMZc5WxYMhOcaQbIudkJpgkf/jb1fcHOUqMSTHGJJtsRNSC8y1bixNU18bYWqUq8SQHGNItsVOSM9huvl9db426tu16BnlKjEkxxiSbbET0v8wzvz+vXU+RTU8F+UqMSTHGJJtNkP6+7j7xpH90iG9idHm969xnfpWATdEuUoMyTGGZJvNkDDCfePtEtIhfYAh5vfPUEN9K4ILo1wlhuQYQ7LNTkijR49Gq9Gmz2vmlw6pO/qZ33ugovF1VwKwKrpVYkiOMSTb7IQEf/dLh9QHPczvHVDE+LrZ+CPGRbdKDMkxhmSbnZCmTp2Kl6da5pzwPLpucLcRu31Pyhg5PrqQPkNH83trJOzStGWIQ/foVokhOcaQbLMTkuGO2Zkf2ez67o9hqQe99393RRnSKHQwvzcDNmjafFTBk9GtEkNyjCHZZjMky+mtR723R43I0E/0/MFz90DXrlGG9A2ub6c+ir0DWKJp09A8rn50q8SQHGNIttkNaV7LzfqfNZHY/ox1/7hrqfF1Sj/35IyRE4ZFGdJU413RHON7PahDHMbitbIlo1slhuQYQ7LNZkjT47Bcfwp31sIX1gN7XenG18WpGdbd33se84R0eKlp7cHQDuv/WTf+bHMlZhvfqwIzDx4cia43Iy3MzwZ3LKqfyt5x/V/ZGR4+Kju/g6czhGf47z+y8/tHPyE7w4PHhed3Qne4yjZDuqHggjMnku/Sj5W93npgq2uv8XWFy9r3cKDret0T0maX6dPs/oSs3ofaQiwNTNH1zzHsf1gc+c8SxaRsQrqgma4vwBhdf9z9gewW19+6CumYumPusYv2FengQRfGGl+TgMEHD6ZiVE8Miur/Br4iOcZXJNtshlS4pa67sEvXmxe0HvjL2rTrbG7aLeux//jxod8eP+3/I2G2Lb3vkTStMwZp2ibjrVKqusj5V9/gf1FtrfI9kmN8j2SbzZCuqvDvqYtq6/rRctWsB466lhlfp1k7G2ZZW3Ou9dGF1BO9NO13JKv94K0wbQnujmqVGJJjDMk2myENxUU10Uefejk6ux8Z+WWGfqq3tft7/3bDwFHbj/r/SJgF8AtpALpo2ixURxtNexQ/7Sp4WVSrxJAcY0i22QzpzPsXJDY/qb+Opkfcj2zqOHPj2O4HdH3hqJPmA9Hu/ta04XhHfZzUGI+pE8+Xa7XypEWzSgzJMYZkm82QdD3jP1VPWob3gbVDuo1UhwhNdh13GNJXeFXTBqMN7tG0G7FFewi/RLNKDMkxhmSbjZBeHJrdk0MKswB+IU1S23Q90A03aVqdRE173X2Gkk0MyTGGZJuNkNBEfe3fMsdCmoWW6uDvUXnqaFrlor4TK2xiSI4xJNtsh9TE7knoYRbAL6QFeETT2mBysUqaVrKipvVzn1hhE0NyjCHZFkshLcG9anfd/ItKfDIofzVNGwJXNKvEkBxjSLbFUkhrcZum3YmVtRLzFcS1mjYab0azSgzJMYZkWyyFtE0Ne1IfO25QZ+A20rRxeCmaVWJIjjEk22IpJC3hCk27IkFrrEJqomkz8Ew0q8SQHGNItsVUSAWralq1/MbbpEsrqB1489A8mlUSDKmf+iCLITnGkAIeKtfMUA7NLDkQUokUc7/383itjTpg9TfcH80qyYU0RL0uMiTnGFLAQ4FyIKSKJTWtfClt4IW/zE7sr2mrcHs0qyQW0rqSau8HQ3KOIflbEigHQqqSrGklU7x3t0Q32KpYSM8BatiIbENa8sTKqObLkJw7l0OKUpgF8A9JHRdU2HfI9674q6JZJbGQrkFiLS1ESKnoG9V8GZJzDCkz/5DU1S/z1vBNK3B5NKskFlLNpKKVNSuktG93ZZ3+Hj6Iar4MyTmGlJl/SA2xflec36tQiYuiWSWxkC4pWq60ZoX0EUZknf5mlKfwMiTnGFJm/iE1xh/brGtRWCpENSCXWEhly6g3bWZIb6mTdzNrh8ejmi9Dco4hZeYfUlP8sh4NfdPM32PbxEK6oNIVavRkFVI7dfKuadty7/TnozwXniE5x5Ay8w/pCfywAnf5pqmdD/aJhZSv+g3YYoXUSp28a2padKNnektENxYsQ3KOIWXmH1JrTPkND/im1cf2KFZJKqSdcXXvxAorpMfxivvRqzHQ84TmqBLVjBmScwwpM/+QXsHXP6OZb1pDrItilaRC2oSbmmChFdKDeN79aFV1IrzlIUQ3qDJDcu7/YUi7Q9ujH/fefhuj5uBJ37R78UeYnw7mUBQ/E8xq3PEkfti9+x/9wO478YT70QootGP3zGeqT9i9uzHyRDXjvw4ILaHHqTPCM9y/V3Z+f+v/ys5w92Hh+f2r/+1sBjke0pkDoR3ST3pvd8fnP+AF37TmWBLmp4M5HMXPZLb4rX0HVqDJ/zDjwIFj+pEDN6OZe0ox4KsDFwGvHjhwO5AWzcwPHhJYQn+nM4RnePig8Pz0E7IzPPCP8PxO6A5/bXI8JDubdj3Ra7z/OUgtMSuKF1mJTbsn8L06+PwNfGlt2l3t3UGXJy9aaPnj1RkeNwCLo5k5N+2c+3+4aRdmAfxDGoAuY/Cab9oLmBTFKkmEdB+GazPwrAuDrZCq4WZrwjbUwS1bUQqPalpdYHo0M2dIzjGkzPxDGoZ3huNt37TXMDaKVZII6RZ0UyfofqQ+h1UhXYRrrAmrcGee2ktxpRpeohbUC5Z9DMk5hpSZf0hf4TXPlWRN7+LzKFZJIqSr0U4bjTcGorMVUkm4DwFchKalKszBvbhV06oAn0Qzc4bkHEPKzD+kSWgTMARXV/SPYpUkQqqKh7UheN+8rq0KKRmVrAmz8dTlBb7DC+qz2BSgUzQzZ0jOMaTM/EOahZYfoo9vWh98GMUqSYRUFjdq/dB9nDosVYWUiNLWhAl46Xr0Q8fE2ppWBt6PaW1hSM4xpMz8Q/oZj3RR10jyCNjOi5hESIVxmdYDfc3hV4yQdgBFrAmj0eEevIK+hS/VtGJQI0vYx5CcY0iZ+Ye0FPcGvC36Qm1Z2SYQ0q54JGsuDP5Zjf1qhLQOyGNNGYTUlmiMUaXLalrBImqXg30MyTmGlJl/SGtx2xv+A+d/Z7zlt08gpI0ANrbHF3+gsRnSUuO+dYmZD9H3VVTFFDU2eWLV6M6FZ0jOMaTM/EPahuvb4TvftGloFcUqhQkpbWPo6cofRjg/G4uyAbeYIf1s3N9gTnFhaGfkx881k7Q01MtXPYrlY0gCGFJm/iHtSrjieUzxTZsb1ZlzYUJqUj7IWeOZzDfC+bYVpqWjrhnS98b9ZeaU1/DVAOPO6npI24SbypSNYvkYkgCGlJl/SFrhy57yPypoER6KYpXChFQbW8POYiry4pMWmKupsfyNkCYa7VgXPXseU8cC8ekNsX4NGlXLH8XyMSQBDCmzgJAqlGyO+b67K3BnFKsUJqQqWBN2FmNRHW8/hEXmkJVGSF8aIf1gTnkM82YBRdVZ8Utxb31Ec3FOhuQcQ8osIKRq+dVvr9dGNIhilcKElIIlYWcxBPfjmbuwXEspYYY0FPGYbE65D0t+By7SHsaCRWh6G9ZGsYAMyTmGlFlASPVxm/u9iGlnXN0oVilMSCXxc9hZfIz2uOtmbNCq5TND6ofi+Mqc0hAbtgJXak/h+x/x+IP4NYoFZEjOMaTMAkK6A3UC/o/3H+QuYmFCSo7g3IyOGJRwRT1ju60u0lVIPXAZhplT6sWla/nRUGuLCTPxjHobZR9Dco4hZRYQ0sMop4Yb8bqgUhSrFCakxAjOzXgNY8uWqZWkjgLfoEL6ANe5D/urVkDTyqOp9gZGT8ILbTExigVkSM4xpMwCQnoWebHTb2LZMlGsUuiQ0uDeSAvleUy9IrFSEXOkPRXSm7gXH5njR6SU1LSaaKW5MOQbvNw+quuuMyTnGFJmASG9CgSMwHVJkShWKXRIG4HhYWfxGOY1RlJpTXsEP6mQXsRTaJ3vfWNK8Ys1rQHaqwPxvkCHzvg0igVkSM4xpMwCQnIBhfwnRjWwXeiQVsA3nla27sUfzwFGM09jhgrpabyBq8wR9/LW0rQH0U37BN2G4oPe6BnFAjIk53I3pEUzw37M4TykdYO7jfCMoXJm4edd+y8+E3VIvYDi/hMbYJN1Y3OF98KtiVfokH4FPg47i5ux8T2gmhqV+BsVUjN0RyFcrmk71HlIzxqvQ8Pw7gB0je666wzJudwNqRFWh5uB45A2u777Y1jqQevOj67JK6d1nB91SEOBgINu7sFS68Z8z5gJEQgd0jx4Rx/O3lXxuwYCV6nhWD5UId2LIQAK7NLWoJHapzdN+wqv9sLHX/mPMBExhuRc7oZ0IzaHm4HjkEaNyNBP9PzBvH26+yTj64zUgJekMAsQENK35vaUz+Oe3ctTcGm4NfEKHdJMeEcfzl6VZG081KHdk/CcCuk2TIB5uN2veFDT0udr2mS07ob+U6M6qpYhOZe7IalPQcJwGtJx11Lj65R+5p0DrjXG17WugEG+wixAQEjGb3lV/4kveHYvj0aBcGviFTqkicCrmjYk9N62MuW0RUAjTVuHBiqk6/CTCmm8NgdPWM+YgxYuDJmHxyJeLB+G5FzuhlQzKewMnIa015VufF2cmqHunPr7P+PrzM7/RRuS8dtb239iB3xh3eiPCI6Qcwsd0ligjaYVrxjySclVte1x5qWgS5ZRIdVJWKlC6m28Qr3gWdImxsItjerMPobkXO6GdGnhsDNwGtJW117j6wrXCe8jyzvNNL+nDzaN+y+0U/oZ350/gev9J36CUdaNfsCSzl2s27u3hJ7j6ZBTjU22Nv/9lzfvyRDPORFf/7//LsRTxs1b4vad1k9XK3wQKIK3/psKl/WUdNz/Lmbuwx2hlyWaJbQvQxee4alTwvPz/0cWIf1XeEYPtcopZcLOwGlIW1x/myEdc98//I1r/Gnz1rYepuFnwtAzfLdPxuFW/2lfor91o7OxXVWglHW7QcnQM8wIOXUM0PLMSWBviOccxB1nzlyBF42bbbEoQ8+4qMxp4wUKD5/5Gn2spxxWJ/P+dCru+tDLEs0S2qfrwjPMEF9C6RmKzy/kX2GpSmFn4DSkv6xNu84Z1t013fuuy/SMMC+JAZt2WiHc5j9xNNpbN54HnkSidUZehTC7UEJv2vWCsTm2HiEPkluC+zTtdrxo3OyOXsamXfGLtALokqeG9pFnkKOdcVc9ixlaoaohZpMdbto5l7ubdsnhr9/jNKSjrmXG12nWzgZ9rWvSqczPCLMAgSGVUYMk+ExW72eUZkA5YL15u1CYQ65Dh9QVRqt/hD5O6Ed1Zu4TeF1Tw0a8YIRU4HKtBEZXStbe947Nkr+aOmK1dPmQf1hwDMm53A0psXaIiRanIekjv8zQT/V27/7+aHJGlieEWYDAkKoGnhPr3St2l3qvb52qtCPziNuTH7wn4ErJoUN6z3gbpi0A+oZ4jtlvB6jPgFfgtsP6vrirjNfBnxpi1cv41v2cEilNsTC6Y5gYknO5GlIa6oWdgeOQNnWcuXFs9wO6vnDUyW2uaQuVk1GHVBfN/Scu81zVqz7i4Q5oOTIdKXoPEHDaUuiQ3lBnE30P/yHGsxiFtzTtE2t0ymLlD+s7caNWJX7bU5jxNL53P6diyXuxNLpjmBiSc7ka0qYITjB1HJK+dki3keoQocmu40tdliNRh3QrnvKfuAU3WTeqFSgNd0BzM7+a1I1Prux//5D2TIhPd14ELtcmAM+a9zoGHSrVHDd5Sy9z3KCGWKOvwP1a57bq5Iom3hN4qxa6Hau1GwPO+ogQQ3IuV0NaiTvCzsB5SOGEWYDAkB5C64CpeepY38uXvsoIqZ+6OQ54P+A5KcUvLuZ//5BWPMTV0J9VJ4p/CfdL3cV5gz2no7qai9v7GKT/gOfUzc4Y1Mj7cdaV8Tdhk6ZOR7eNITmXqyH9Zn6kGFqMhfRUpiEhS7iPGCpU9X5cbA1gPASeD0Xd8lW7Kt7/CI5DaXEhLuL8GFBKzeNq896FCDbK3cv4xnt7NprpY9SmnvpUuGu9OM9hwDeiFtK0R7AgxLplgyE5l6sh/eR/YeNsxFhI7Tz7u90utg4GT4ur9wJa4GV1+yOocYR9NqDBbQGHPRxaBvyR7Z/3EPIW1voCKea9/EGvuec/JtjOouX0ftYB46PR/vKCnsfvQMUETXsGM0OtXHAMyblcDWlWBGO8x1hI7yHwdAn3m/nVuH32I+PwpLr9NtS1iXx+xsMPB7wuHPoemJPtn3c3SufRugH51J204Nfcux+/+e7chU3vYZS6NRWtynlP2W2GvAVU+N9l/fFwGJJzuRrS5BILU44AACAASURBVExvOIKJsZA+zHSOw03Wh68L8bA5xL663Qao4/+UcXixtXusLMuh0fDbNMvsFlyOtPcQZ34otT7zHkBLA/8NvlQMed7KbQEeTvZ+AGu0eIGmvYMRWX88HIbkXK6G9I21LRRSjIU0q9LkgKn3WKNzTVfv9rfienW7GRBwxOkgdHoLI/0eONQbIc4Ar4/rseFVlME8487v1h7An+sEHudQ23+n9jS89CB+V7dW41bf+GCzoS6Z1D2aa/YxJOdyNaRREVwVJcZCyuxx8/ddG2seZaDGD1ZnK5Ys6P+Ujvj0I/+rk2mH3gK6+u7ecF/AHOsk3oXlrXGd+aL1I/CBpo52+CDgOSkX+t1ZjbuuxzZ1Kz2uim+rMr2QepvVH93CrF8QDMm5XA1pcATX6YrxkF6wNto+U9dydY8pdHV8/YDRu9ti/NCAHeKHnoPZnWVLfNxC/zlWLfQQFj2BFuZLyVSYB9S9nOn8PHUVMb97l1Vx704vnF+d1+d2I6po2siQH+wG2vm7+wZDci5XQwq4Hms2YjykDtZ7mB7mR0g1zB0ElYsG7AvQmmLBOLzk98Ch+4CnvfeMTTBrj/rsqea3lBItMLcpXKi/1jw56VHjseaBR/ilxwccKVErqYh7eL0U/0v0vYJa6g2aMfPfXww/CLLh3QT3BmTkIUV4DhZDcixkSD1CHlBmifGQumKA+va2+SbIOnO+2MXPYIbfU27Chh8DTlQ9VB9+H6B9aryXMT9lSrE+tS1ZoRWm34VfG+LSNPV50u2aOp7iSuvJs81nrkYj/2V4AJ5DrWrD72OuL9VHUbPwhPZxgQhOXtfUJ1jW0RSRhzQI4yJ6HkNyLGRIHfFZ2BnEeEju9yDWiKb3YcFSbWfClW8G7GqrWkBbbo6U5XHokny40bq5+dEJryPFfPoKYJV6qPCl7TCuATam1Tde13qbI5xotdxDroy39hkuUPsIfV6F5wWrAfCu9+ENiQ2NPyCx9qY8edEizEqa7kLxHeaNiEN6RV0LOgIMybGQIQXuzAouxkNyX0W2BX40vrZE9cRP1+JWa0PPo1iKlhZ3rd8Dhwpf4r6S3s67ccsD6Gm+EoyANVRxUq32GF03bqemXtc6W4OtlEai+VLUxnqvlGlIk0+8G3T3w39jearawKyTOASPq+v6hXct3OOHRxxSiwiOOlYYkmMhQ3olxMcpHjEe0iTrd9raC/4KgMS30HSI/062HXHXaFryZf5zRP2yZbbO0yYXLgMUqJpnQ+IVmvroFL2Mb+m4xoUh6gJhb+BL4y0YChu9JboPhahmncQxOvDwiqnAG9atlln3qz+PWviy8CVhVtJUJd59uaeIQ7oDebM/1snPeRpSqHXP1ZDaRDBcfIyHtK5wkZWaNrZwgtr/bLx+NCkQj1YT0Nb3jCVquyvT/ur7auRtkbjkdRSqeQ9wmVYzj/FPcr21g24TbjJe0C4qrj4C6mdsMibGpWlqYJNpxrQVcdbB5p8E7tNe430dehkYk2kBhxptb6qeL/zVNI13ZxUvSjYHNlchjWsWwSXKrgQmh3/W+RrSq6VCHFmfqyE95T15JnsxHpKWiiba5MS85udE/ZFvzZtAh0Xw+2hoOp5RG1h+v8lz8FwDlMKo+7FI7U24y3ghmaLtTC5ivpFaiTv6oXvJCtaHA08ab6CWGz8QhyHm/GF+UtUZg/wX4WBe9yaZ8aYTUzMt3wp1+OsdmY4B/3VHkDXZlVinqbmFaoZ0l5luGBX835OFcH6GdGuoA4JzNaRH8VPYGcR6SOk14n5539wo07Sv8LC2uRR67Mx3ue8Jn6tdZg3dZ6G7n/b2g8ZrzDvV86Zrq+PxktbPSGYumhRU23+/4X5jy1Cdg/8d2mlNcBPmamNxMTppasT8hJJqBq9hrP8iHK7ieV3oi6x/oxcbs3kusK9FCW0zP0tTB9fekmqtiAqpcsD7vGzkK2buVAzr/AypXqj/a3I1pAcCPm8JLtZD0t7G50+6d3fvSDX+4+8XP16rltd32kRH9bZFnfXt1Rd9njVCapJfHRdXDX20BcYrWE90rZmUro6If3Q03ky4wrpk+u1ogW+NH3jAHBuifJFa5itb4O517XBjz9l8I5H19KNHjTeiHQNfwgYG/fVfhIemq917G3YaIW1PjODwrQ24uXSJsM/SzteQqhlvYrOVqyHdiZVhZxDzIQ3BuzcHXMVvs/ofYqH3rvlq0Mr/P6/2GGNsAOJCc6f1i5it7bqgvPYQ5qhNPe17PDUOz6mj9tQ+8+vwLj7T3oELD2ja1rh6t5rnYwRcx9b4N/vhFfc1myYh6xXRf+mcrg0LPL6hLYKNlTEdz+7IW03bWuxpI6Q5iGBoyV/QtG5c2LFytfM1pBTrM8TgcjWkm4OetRYo5kOag8cuyjzCyBt++/XvVC8SHfw/WTI66I7EijD/z9+q3pXchoUli+18VT1pEtpMx4NopM5xukarldQfXY0Mx8XXUwP1N21mbroFbCia15B135qP4EPXzg78IKkBSgZ50pd4Q7sqcfM8VDVCGghUD7naygS8cFtEA8yenyEV8z9iMrNcDenaCC5eH/MhbYm7JstgSIP93oNXz7tLnX3R2ze1VlL6YNS+D74Dsz8ytt3u0fqrN0Jf4dUfcaN6AdKKXKJVKj4Wt0y9H4svrKj2er/2IsYbE65IDNgJ5wtpBYJvaa0PvFZGcSQE+Yu3kh0/FnnSD2jtEFdA/SFTNoRY9SFwBWyzZuv8DCkP3sx+Yq6GVDuCIW5yPKSM/aEd1E+GfkKZJDyQ6aFf0Nx7u2hl48u36OB9YF+Bqvun4YW3gLmeh7YkAR/vn4fH9+8fg/e3x5XAE8ajlYrvL3XRykKIKw6tdtKW/R9iUBeMMGZQtELAn3ZUP+K+9RcqB1/GIpf63VljbFeuyfqc7hi6fwi69AN+ff3xxqiF1fv3r4h7ev+evdmt+Uf47HnMyW6qn9Ph/pLtOnRQdn6H9eO2f2Y38EL2U/9xsDTBHNcPZz+xaqHwM8j5kPaE9rd+IvQTrjO20TI9tDPxSs/NLbjZ+LoAzb0Tl+PuPX9+un4Y4rZ6H2sELN6zK2+VPXuGIHVPBaCV8WDd+N0Fqu/ZPCgBRfa0x21/tsHkgeixZ89C3Bfwpx3RD3luptwWfBlr5J3wp/fOl4jH91mf8zq+2zMHT74BfJYfeQu+hPF79kxCwz1X3JTdmr+G797AN9lN9XMq3F+yXQf3yc5vv37M9s+sAx7NfuphB0sTzDF9f/YTU0qEn0GOh+R00057HFmvL1nJO0zQHDUoqrbZOufP9K11KPhP6rw7j/4op6kTMDZo76CXdrt17OntWB6nDsF5AZdo6fXR8S4sHasuHNYv02X4fJt22rZstpWfhHUavKk96ng+ePLXErO0jbjhUeAm4yWrTi/1Ke+nqJ6eWDDrcz1rPjeyi9Sel5t2ixBqECy/TbsVN4yPYpEyCblpVyqC8XRjP6T3kHVchDu8o5uMtA7GK3aReW/nkG1ad+v9UtqFfqdGbCqlTrpvhW/WFyu4XB2goEYFao4paGh833pdG01blli7etLO2aqHlpkOJ/ALKTs7x5bN591B0RivB3ubfJ86zfbC8kZFcaiEZhPVwU8dceEyYEU2c70dqyI5E+Y8DWk2cG32U/1CGmGeCuNQyJCKRHAIWOyHNAxZPw57Cx+5b3VBf/WtVpK5h3oonjdysTpYty3zTw1Ch9bqMLrPYJ4n+CL6+51ucXV8/sraH2i8Ib1mnsCfjCAktd8w1XOzcqGvAwYVm9vR3HdhngRydXzF5HyI++muiSvVybYvIn5q1qMlPK5I3DnKGggsjPMypPGZLjoXyC+kj3FZ9s+LVMiQkmqEn0Hsh/QjkrJ8lvJ7/NXuW62t4wnvtsZ2aIM8Cxtme+HcxaiSp+xWtdVnDq/1Ad40twstrwK3aDviLi9RP7FO4I9FFNLyRM+/+s6k6vMDzsNobF35ooYai7Kp8dtRw/qHL3PBTnW3G7L9uKRCSd+FzUI6L0MaBf+N88z8QnoH8aH2fUYmVEg7cXW207xiP6StCZWzPngjfrZuNLYu19wKU9S3a4GG5YtlfbpbCcSpQX/S85nvPPqimfEC5jHBfJtTRI3V/0zgT0UUknanZ2Cv33H3Bs9Iy6aLzAP5tHLq1+JNYwGbWH9AU+M9k7GdZ7xn8jvWvOfXfj+Yr7o2L3As9GzkQEhzXg710UlEx6T7iSakgUD+7Kf6hdQWEZ7/GEqokLaoqwmHE/shae2DnOc7wHNyee085jaddZBOesHKVwPXZDunRu5zVGub49R9gfp+YzvsKKiG1KuMpHqZ399HFlJf83A9TZ19/oJW0H09nbmN12gb463NswLVzOVGiw+sUfL64AOtKlANfsN4Lo9P0eaNct9Za7xELsfdYf/krzbnQEj3BB3wz613nnn25hdNSB8a/6VlH6xfSI9GcnntcEKFtCbwol3BnQMhBbM1uYz5xmNmvLUd9rn5Ee1cNFnboXz27yqmvGT9yzxq7r6YjrL+++duVS8c9dB00+eZ/i+OLKQ5nvFfP0YPrZK65mjblG1PG7cnWyPDbjN3LE4HOmz9Yp963hLjTdIFQCJQ3zuTHsb7wRoJ7uuozTBeIrfhunB/8Hi8mQMhlcTA7CdfE2piMNGE9L7xV5P9wOp+Id2OCP63CSdUSEs910QJ5RwNSXvI/B9z19Xucxe/N09i7W3uRAh9WRdl0l1qo3oR4r37LAyDL/1D0+6Py3rmSWQhpSW5j/lph6+067BF21US31THQ2okSbWJbf1rrFVXAnCf2Hdxoc1x6mI1hcrN7+M+7+Jm4BXgB+M/im1q38gHmpY/+JFEo+v32OS+2QUPyIe0CCGGclsWn2k83LCiCekVVET2L3x+IV0dX6xMts+LVKiQfkHT8DM4V0MaYm7bDfSMprAa9Z7vtf1J851S+JAs24tlPeH1tyDnFEcWklYzj1XDfVhkZP6LOi61ZQLKaS2QcIGmtvjM92NF8K0npCfwKSoBBevGV0Ul86DbDXmKIh/UK2Oda9SOwGGaeUnAlllHVbsZ3k/OnkRt+ZD6IMS48V09F8WJWDQhtTK2vLM/MdUvpEoXNEJEwziFEiqkOZGMyXGuhrQpr9q3Xz/uF/f9AsZ/7cUKJ6qDsyMNSVve7spF4Z8VaUiPukfer5OYZjTQXfsAyGss1K9XJNZXR56+gaHmVCMxd0if4lbcB1RqCpRLSFHbkwPxihG38ZZqCRK3ac3U3r6qydpK5A/cK7V9044CKWWKuu/VR7J8SM38Nzgzq4/A4cvCiyakR/FwiDFH/EIqWvmtaMa7DRQqpGmZhj0M6lwNSWuEu+/52TeCcBXc/2xJa6yQiEOKUIQhpaK39m3zHVrRFCPRvCnpt6ACUA99Clz2rHqhvCHOHMTo9SppnpBWxSeiQyKuew2Y+Li5Q74+ZjVGCeO1oB/wvXZt3BbjTVtc2nD4b4EabiozBk/e5DlEvQSgiYdUMblE2ewmrk6obY6CYUM0ITXGqyFOfvSFlBZ/9RSEuLKcZVfrECc3aaFDGhfJYE7nbEi9oa7P7P3cf+Cb6cbvr/k2/SyFNNHY3rkOQ9abu75boHv+yu0QNxrV8EA39NV25Pd9augZ/KQm0LcUmvRDHe23PBXTtLHGq0APvJFwjfYwjCpLldLMoxtetMYM89pVCCkY2sI9jsBq4+/hp8wh9e4e9dq6Z9rw6vjsdpkNwZtlQ3zCE0w0Id2MXp4doUH4QlqJ29MKXhxubvM8I7RlI1RIwyMY+vvcDSlt6LQU5FkbZMpZCmljfL1tedFoprlB/UsC8PQ4VEkrgMQhX6Np6hi/g/E8IRmFjKmFtr9W/Vq9YbpnWS1M1rb221zhQq0c8NwW80DAh7Hg2vg6AWe4LzHSSVj7jvXplDYByRiWKaT1eQvujH59RyV/PwwdmmQ7TkFLTLwq2HkiIYQOadfQLKdLGq5KGItXs/0ZX0jz8KgR3bIwS9APoXdIhArp7QjGhzx3QzJMSgy6W/IshaRVyWdshCVaF0PXejW7b/72uh211x9eoK55Ybxd8p2N7gnpW2DOreaORk1bd63xpEbmzZswC9fG1Z9rvt1vhYn5q/ZHO22dN42xSEJtbbB7z1kP3Iu3M4XUA37n+O760ebVOZ9Cu9b47rWgF7xRLi2ww/0xeMRChzQ86HFQVQrPyPzRuB9fSOPRVnsr7P74ZxG3KdT07EParjUJsffQ61wOSVu8OdijZyuk9qgI4314YpZD53ZeUvku+P2f6Qlpe36sesz9wqJtf+Lal6yRAZ7C4+hYsegwc4PiDbyJFhvyVP0933Wele2IV/O9rc3EE+a95zAAD6uQtj9sfr7/Y7907Sr4Ltv0eXEk94hktDCvuqh7ZeLmfr6DBwMtj2tg5J3d8YHBhQ7puaCbXeXKLkKTbH/GF9JQ43+UKWH3q12NbK+tOF69HmYbUrc886vnieD195wOKbizFdKSeMT/lJT8ddCJXfy2UrwDRD5QYVen+CxbUJ0Qn7jgTjxnvoal4krjHVYD42UHtR63BlN6HN9vVp9IWWfl3oQ1SbU6te6XNhiXqBetq1GnI/L7tuprJ9yXHOHIx5adhZCYVEubmN2uqgF4W/vAk3+Esob0jd8oCDVRIMhvauEqG3BJtiPb+UJSZ0en5Qtz3Gp6flhHN2c13fzbyTakquiQ9/LgkwIwpLAiDcn4na6uTYtgh3rASKtZNzhGq9OlXkVJ8yPn/kD8b+pD3cK3AUlLtfRuk6+JN3+9ChtvsMf8/mOBMtpl6gDBLveaH0CtTiho3GlnnkuvzEVDbWnluAg28j1+sQ43XJbdSGDNME1d3C3yGWpBQprodzHJDQnBDoDflXCl8cbxwSyPu/lC6qB2kl/tPm51ejZv7OaiNF5O+7JVkMkdoI7tzi6kOTC2M7JdCj8MKayIQxoYyecNWtghixfj8h3q5JGiqjGjKmOmS+LQKn3+O3h2Q0OUK2pdsLBmnvRUFChtvBG+A9cPTapQsFh85QdT+6HDlH79duT3HIveSn3m/FNyvsmL3tuyKCXLGZJBDEZd9UH1rmKFgx5TvTCpVJrRQZtMD+/8sMs32W9AZgnpFZT1PnsMypsHeO0MeFnajAbajmvwYdD5LatX+iLPO7/n1YcLz1nnrG0rVCn4QvRBGzQ2Nu+CHKJwC+LXfv9bsJAmXDBAa4uEUAd5+DCksCIOKb1/didwBAo39vdY45389s7DzfdEU5GiXn9qxi/UtLTy+Uoj2XMt6sZwxRdNVplNaP+POnKz1T1AQm11gJE6ltf8rRz4WNHC6uSq4fEXJKPFLcj/c/ila4fPEtWRAu3wjrb8svaZJ9+uPtz51fuK57b8euNl7L7AnW9+e1T9Q1pz4x3Djfdh1uH6ysvoplZp41WV/Xe4L1cf+i4tVCTYWY/brkAp739aTfGL2t40j1v9KvjFtdXOz8kFCuCK4hdm2ZuZbvyFds1bI1hIVyDxg9IFH0VE1wlmSGFFHFKk7Fyxb1sb8+Oi+WPU1+5IemlxkvsUpReA/FOXf6LO1Tp1Rpsbh4mrhhqbgNbBvA9jrjZr0fREz8E8LiSVBsqiwmUNQ4wHP/fD3hu1hlhev9IeTVuV/8KttyNhYtq2bf+7y/Pi9Mn1qGv8ATviq4370e9dzoZLcOtnV6Ke/4ljI+Pv9aY0ZasnpO0rrjKS+zgxr+8Mlvpxa1IKb9zeAAHbiz+bOy074pbZWZfzKdy/t3SBVdadhlinrtZtHrfaGlk3CkbfsnBTcxTfVgP4umnWPQ7fI8V4S4kVmUP6rVsv1DAmPDIciOT4F+chrRvcbcTuoHcYUnBRX/oyLXWR+qzX+ry/Bwp+635cHSJ0b1X1n20D9+6r99EnNSGpZNyARe7f7eE/zkpKnNtUHcpXtPGcwNkuSbm4x+ad09fOMX5v6m8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"</g>\n",
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"</g>\n",
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"</g>\n",
"<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n",
" <use xlink:href=\"#glyph1-1\" x=\"246.042969\" y=\"333.345703\"/>\n",
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" <use xlink:href=\"#glyph1-3\" x=\"259.386719\" y=\"333.345703\"/>\n",
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"</g>\n",
"<g style=\"fill:rgb(0%,0%,0%);fill-opacity:1;\">\n",
" <use xlink:href=\"#glyph2-1\" x=\"19.236328\" y=\"248.957031\"/>\n",
" <use xlink:href=\"#glyph2-2\" x=\"19.236328\" y=\"241.625\"/>\n",
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"</g>\n",
"</g>\n",
"</svg>\n"
],
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"p <- ggplot(fst, aes(x=position, y=Fst))\n",
"p + geom_line() + theme(aspect.ratio=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"# Plot time taken for each window to see which part is slow\n",
"p <- ggplot(fst, aes(x=position, y=time_load, color=\"load time\")) + ylab(\"time (s)\")\n",
"p <- p + geom_line() + theme(aspect.ratio=0.3)\n",
"p + geom_line(data=fst, aes(x=position, y=time_stat, color=\"stat_time\"))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>Fst</dt>\n",
"\t\t<dd>31.9953513757763</dd>\n",
"\t<dt>start</dt>\n",
"\t\t<dd>8757100000</dd>\n",
"\t<dt>stop</dt>\n",
"\t\t<dd>8.799e+09</dd>\n",
"\t<dt>time_load</dt>\n",
"\t\t<dd>513.103000000001</dd>\n",
"\t<dt>time_stat</dt>\n",
"\t\t<dd>222.652</dd>\n",
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"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[Fst] 31.9953513757763\n",
"\\item[start] 8757100000\n",
"\\item[stop] 8.799e+09\n",
"\\item[time_load] 513.103000000001\n",
"\\item[time_stat] 222.652\n",
"\\item[position] 8778050000\n",
"\\end{description*}\n"
],
"text/markdown": [
"Fst\n",
": 31.9953513757763start\n",
": 8757100000stop\n",
": 8.799e+09time_load\n",
": 513.103000000001time_stat\n",
": 222.652position\n",
": 8778050000\n",
"\n"
],
"text/plain": [
" Fst start stop time_load time_stat position \n",
"3.199535e+01 8.757100e+09 8.799000e+09 5.131030e+02 2.226520e+02 8.778050e+09 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sum the output array to give the final time load and time stat\n",
"apply(fst, MARGIN=2, sum, na.rm=T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot hz in a KES sample vs CMS sample"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# choose the first sample of each pop\n",
"sample_kes = match(\"KES\", metadata$population)\n",
"sample_cms = match(\"CMS\", metadata$population)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] 300 344\n"
]
}
],
"source": [
"# what numbers of samples did we pick?\n",
"print(c(sample_kes, sample_cms))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Function to apply a function f, to values based on windows of positions\n",
"# and a single sample index\n",
"# NOTE: The final incomplete window is not included\n",
"windowed_analysis_3D <- function(positions, values, sample_ix, f, \n",
" win_size=100000, ...){\n",
"\n",
" #windows <- seq(0, 500000, by=100000)\n",
" windows <- seq(0, positions[attr(positions, \"dim\")], by=100000)\n",
" ix = findInterval(windows, positions[], all.inside=TRUE)\n",
" x = rep(NA, length(windows) - 1)\n",
" \n",
" for (i in 1:(length(ix)-1)){\n",
" start = ix[i]\n",
" stop = ix[i + 1]\n",
"\n",
" v = values[start:stop, sample_ix, 1:2]\n",
" #print(\"ok\")\n",
" x[i] = f(v, ...)\n",
" }\n",
" return (data.frame(\"values\"=x,\n",
" \"start\"=windows[1:length(windows) -1],\n",
" \"stop\"=windows[2:length(windows)]))\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# counts the number of hets in an individual\n",
"calc_hz <- function(x){\n",
" \n",
" stopifnot(length(dim(x)) == 3)\n",
" stopifnot(dim(x)[2] == 1)\n",
" stopifnot(dim(x)[3] == 2)\n",
"\n",
" # just return count of hz. Assumes if one allele missing, other is.\n",
" return( sum(x[,,1] != x[,,2]) )\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# kenya heterozygosity\n",
"hz_kes = windowed_analysis_3D(fh[\"/3L/variants/POS\"], \n",
" fh[\"/3L/calldata/genotype\"], \n",
" sample_ix=sample_kes, \n",
" calc_hz)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hz_kes$position = with(hz_kes, (start + stop)/2)\n",
"hz_kes$population = \"KES\""
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# cameroon\n",
"hz_cms = windowed_analysis_3D(fh[\"/3L/variants/POS\"], \n",
" fh[\"/3L/calldata/genotype\"], \n",
" sample_ix=sample_cms, \n",
" calc_hz)\n",
"\n",
"hz_cms$position = with(hz_cms, (start + stop)/2)\n",
"hz_cms$population = \"CMS\""
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# join the outputs to make plotting easier\n",
"fst_df = rbind(hz_kes, hz_cms)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th></th><th scope=col>values</th><th scope=col>start</th><th scope=col>stop</th><th scope=col>position</th><th scope=col>population</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><th scope=row>1</th><td>9</td><td>0</td><td>1e+05</td><td>50000</td><td>KES</td></tr>\n",
"\t<tr><th scope=row>2</th><td>16</td><td>1e+05</td><td>2e+05</td><td>150000</td><td>KES</td></tr>\n",
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"\t<tr><th scope=row>4</th><td>21</td><td>3e+05</td><td>4e+05</td><td>350000</td><td>KES</td></tr>\n",
"\t<tr><th scope=row>5</th><td>3</td><td>4e+05</td><td>5e+05</td><td>450000</td><td>KES</td></tr>\n",
"\t<tr><th scope=row>6</th><td>7</td><td>5e+05</td><td>6e+05</td><td>550000</td><td>KES</td></tr>\n",
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],
"text/latex": [
"\\begin{tabular}{r|lllll}\n",
" & values & start & stop & position & population\\\\\n",
"\\hline\n",
"\t1 & 9 & 0 & 1e+05 & 50000 & KES\\\\\n",
"\t2 & 16 & 1e+05 & 2e+05 & 150000 & KES\\\\\n",
"\t3 & 1 & 2e+05 & 3e+05 & 250000 & KES\\\\\n",
"\t4 & 21 & 3e+05 & 4e+05 & 350000 & KES\\\\\n",
"\t5 & 3 & 4e+05 & 5e+05 & 450000 & KES\\\\\n",
"\t6 & 7 & 5e+05 & 6e+05 & 550000 & KES\\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
" values start stop position population\n",
"1 9 0e+00 1e+05 50000 KES\n",
"2 16 1e+05 2e+05 150000 KES\n",
"3 1 2e+05 3e+05 250000 KES\n",
"4 21 3e+05 4e+05 350000 KES\n",
"5 3 4e+05 5e+05 450000 KES\n",
"6 7 5e+05 6e+05 550000 KES"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"head(fst_df)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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],
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/svg+xml": {
"isolated": true
}
},
"output_type": "display_data"
}
],
"source": [
"p <- ggplot(fst_df, aes(x=position, y=values, color=population)) + ylab(\"n Heterozygotes\")\n",
"p <- p + geom_line() + theme(aspect.ratio=0.3)\n",
"p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Select a gene and list the NS variants\n",
"\n",
"This cannot be done in R, as the h5 library does not yet support compound data types"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# define start/end of a gene from vectorbase\n",
"tep1 <- c(11202091, 11206882)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# find the start and stop indices\n",
"ix <- findInterval(tep1, fh[\"/3L/variants/POS\"][])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ERROR",
"evalue": "Error in eval(expr, envir, enclos): Reading of compound datatypes is not yet supported.\n",
"output_type": "error",
"traceback": [
"Error in eval(expr, envir, enclos): Reading of compound datatypes is not yet supported.\n"
]
}
],
"source": [
"fh[\"/3L/variants/ANN\"][ix[1]: ix[2]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Select a gene and find variants at > 5%"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read in genotypes\n",
"tep1_g = fh[\"/3L/calldata/genotype\"][ix[1]:ix[2],,]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# function that given a genotype array creates an n_variants x 4 matrix of allele counts\n",
"# or of allele frequencies if freq=TRUE is passed\n",
"count_alleles <- function(geno, freq=FALSE) {\n",
" \n",
" n_var = dim(geno)[1]\n",
" n_samples = dim(geno)[2]\n",
" \n",
" ac = matrix(0, nrow=n_var, ncol=4)\n",
" for (i in 0:3){\n",
" ac[, i + 1] = apply(geno, MARGIN=1, function(x) sum(x == i))\n",
" }\n",
" \n",
" if (freq) {\n",
" return (ac / apply(ac, MARGIN=1, sum))\n",
" } else {\n",
" return(ac)\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# use above function\n",
"allele_count = count_alleles(tep1_g, TRUE)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# minor allele frequencies are calculated\n",
"maf <- 1 - apply(allele_count, MARGIN=1, max)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>11204271</li>\n",
"\t<li>11204298</li>\n",
"\t<li>11204330</li>\n",
"\t<li>11204373</li>\n",
"\t<li>11204436</li>\n",
"\t<li>11204442</li>\n",
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"\\begin{enumerate*}\n",
"\\item 11204271\n",
"\\item 11204298\n",
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"\\item 11204373\n",
"\\item 11204436\n",
"\\item 11204442\n",
"\\item 11204659\n",
"\\item 11205025\n",
"\\item 11205026\n",
"\\item 11205037\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 11204271\n",
"2. 11204298\n",
"3. 11204330\n",
"4. 11204373\n",
"5. 11204436\n",
"6. 11204442\n",
"7. 11204659\n",
"8. 11205025\n",
"9. 11205026\n",
"10. 11205037\n",
"\n",
"\n"
],
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" [9] 11205026 11205037"
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},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# display positions within the gene that are also >5% MAF\n",
"fh[\"/3L/variants/POS\"][ix[1]:ix[2]][maf > 0.05]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
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