library(SNPfiltR)
## This is SNPfiltR v.1.0.0
##
## Detailed usage information is available at: devonderaad.github.io/SNPfiltR/
##
## If you use SNPfiltR in your published work, please cite the following papers:
##
## DeRaad, D.A. (2022), SNPfiltR: an R package for interactive and reproducible SNP filtering. Molecular Ecology Resources, 00, 1-15. http://doi.org/10.1111/1755-0998.13618
##
## Knaus, Brian J., and Niklaus J. Grunwald. 2017. VCFR: a package to manipulate and visualize variant call format data in R. Molecular Ecology Resources, 17.1:44-53. http://doi.org/10.1111/1755-0998.12549
library(vcfR)
##
## ***** *** vcfR *** *****
## This is vcfR 1.12.0
## browseVignettes('vcfR') # Documentation
## citation('vcfR') # Citation
## ***** ***** ***** *****
#read in vcf as vcfR
v<- read.vcfR("~/Desktop/phil.dicaeum/n8.vcf")
## Scanning file to determine attributes.
## File attributes:
## meta lines: 14
## header_line: 15
## variant count: 250231
## column count: 73
##
Meta line 14 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 250231
## Character matrix gt cols: 73
## skip: 0
## nrows: 250231
## row_num: 0
##
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Processed variant: 250231
## All variants processed
#make popmap
pops<-read.csv("~/Desktop/phil.dicaeum/dicaeum.sampling.csv")
pops<-pops[pops$ID %in% colnames(v@gt),]
popmap<-pops[,c(1,4)]
#remove obviously contaminated sample from looking at the preliminary splitstree
v<-v[,colnames(v@gt)!= "D_hypoleucum_28663"]
#fix popmap
pops<-pops[pops$ID %in% colnames(v@gt),]
popmap<-pops[,c(1,4)]
#visualize distributions
hard_filter(v)
## no depth cutoff provided, exploratory visualization will be generated.
## no genotype quality cutoff provided, exploratory visualization will be generated.
## ***** Object of Class vcfR *****
## 63 samples
## 47564 CHROMs
## 250,231 variants
## Object size: 625.3 Mb
## 58.08 percent missing data
## ***** ***** *****
#hard filter to minimum depth of 5, and minimum genotype quality of 30
v<-hard_filter(v, depth = 5, gq = 35)
## 0.05% of genotypes fall below a read depth of 5 and were converted to NA
## 2.54% of genotypes fall below a genotype quality of 35 and were converted to NA
#execute allele balance filter
v<-filter_allele_balance(v)
## 16.48% of het genotypes (0.95% of all genotypes) fall outside of 0.25 - 0.75 allele balance ratio and were converted to NA
#visualize and pick appropriate max depth cutoff
max_depth(v)
## cutoff is not specified, exploratory visualization will be generated.
## dashed line indicates a mean depth across all SNPs of 72.8
#filter for max depth
max_depth(v, maxdepth = 150)
## maxdepth cutoff is specified, filtered vcfR object will be returned
## 16.31% of SNPs were above a mean depth of 150 and were removed from the vcf
## ***** Object of Class vcfR *****
## 63 samples
## 41978 CHROMs
## 209,408 variants
## Object size: 450.1 Mb
## 64.05 percent missing data
## ***** ***** *****
#remove invariant sites generated by filtering
v<-min_mac(v, min.mac = 1)
## 17.36% of SNPs fell below a minor allele count of 1 and were removed from the VCF
#check vcfR to see how many SNPs we have left
v
## ***** Object of Class vcfR *****
## 63 samples
## 43982 CHROMs
## 206,800 variants
## Object size: 527.5 Mb
## 58.33 percent missing data
## ***** ***** *****
#investigate patterns of missingness per sample
missing_by_sample(v)
## No popmap provided
## indiv filt snps.retained
## 1 D_hypoleucum_18070 0.5 71578
## 2 D_hypoleucum_18191 0.5 73049
## 3 D_hypoleucum_14037 0.5 74014
## 4 D_hypoleucum_14079 0.5 60840
## 5 D_hypoleucum_14065 0.5 72256
## 6 D_hypoleucum_14120 0.5 46184
## 7 D_hypoleucum_17976 0.5 37744
## 8 D_hypoleucum_18159 0.5 72353
## 9 D_hypoleucum_14075 0.5 70540
## 10 D_hypoleucum_20218 0.5 67004
## 11 D_hypoleucum_19066 0.5 34890
## 12 D_hypoleucum_19177 0.5 72160
## 13 D_hypoleucum_19178 0.5 71258
## 14 D_hypoleucum_14146 0.5 36352
## 15 D_hypoleucum_20213 0.5 52551
## 16 D_hypoleucum_FMNH454949 0.5 55342
## 17 D_hypoleucum_25637 0.5 74455
## 18 D_hypoleucum_1271 0.5 71906
## 19 D_hypoleucum_1273 0.5 74371
## 20 D_hypoleucum_1275 0.5 59957
## 21 D_hypoleucum_25921 0.5 69190
## 22 D_hypoleucum_3274 0.5 68561
## 23 D_hypoleucum_26984 0.5 73388
## 24 D_hypoleucum_3208 0.5 74503
## 25 D_hypoleucum_3158 0.5 74560
## 26 D_hypoleucum_2253 0.5 63952
## 27 D_hypoleucum_3095 0.5 68611
## 28 D_hypoleucum_462070 0.5 73605
## 29 D_hypoleucum_25675 0.5 44555
## 30 D_hypoleucum_454950 0.5 73786
## 31 D_hypoleucum_25880 0.5 68333
## 32 D_hypoleucum_3314 0.5 72117
## 33 D_hypoleucum_357608 0.5 45510
## 34 D_hypoleucum_357615 0.5 75155
## 35 D_hypoleucum_357612 0.5 39822
## 36 D_hypoleucum_19638 0.5 66516
## 37 D_hypoleucum_25868 0.5 74066
## 38 D_hypoleucum_17969 0.5 68845
## 39 D_hypoleucum_25672 0.5 46130
## 40 D_hypoleucum_26975 0.5 64410
## 41 D_hypoleucum_2229 0.5 52940
## 42 D_hypoleucum_2067 0.5 75507
## 43 D_hypoleucum_28329 0.5 72370
## 44 D_hypoleucum_28361 0.5 61609
## 45 D_hypoleucum_28376 0.5 66034
## 46 D_hypoleucum_2208 0.5 75500
## 47 D_hypoleucum_28294 0.5 61791
## 48 D_hypoleucum_1956 0.5 76320
## 49 D_hypoleucum_25670 0.5 58783
## 50 D_hypoleucum_20921 0.5 75733
## 51 D_hypoleucum_20193 0.5 41715
## 52 D_hypoleucum_28416 0.5 70945
## 53 D_hypoleucum_18193 0.5 75608
## 54 D_hypoleucum_27450 0.5 74751
## 55 D_hypoleucum_19046 0.5 67654
## 56 D_hypoleucum_19136 0.5 69792
## 57 D_hypoleucum_28676 0.5 73799
## 58 D_nigrilore_KU28413 0.5 59445
## 59 D_nigrilore_KU28414 0.5 63208
## 60 D_hypoleucum_29945 0.5 74446
## 61 D_hypoleucum_31636 0.5 74750
## 62 D_hypoleucum_29951 0.5 72999
## 63 D_hypoleucum_31644 0.5 71622
## 64 D_hypoleucum_18070 0.6 63616
## 65 D_hypoleucum_18191 0.6 64388
## 66 D_hypoleucum_14037 0.6 64024
## 67 D_hypoleucum_14079 0.6 54176
## 68 D_hypoleucum_14065 0.6 62307
## 69 D_hypoleucum_14120 0.6 41210
## 70 D_hypoleucum_17976 0.6 33983
## 71 D_hypoleucum_18159 0.6 62882
## 72 D_hypoleucum_14075 0.6 60317
## 73 D_hypoleucum_20218 0.6 56835
## 74 D_hypoleucum_19066 0.6 31233
## 75 D_hypoleucum_19177 0.6 63991
## 76 D_hypoleucum_19178 0.6 62982
## 77 D_hypoleucum_14146 0.6 32573
## 78 D_hypoleucum_20213 0.6 47108
## 79 D_hypoleucum_FMNH454949 0.6 49611
## 80 D_hypoleucum_25637 0.6 64130
## 81 D_hypoleucum_1271 0.6 61842
## 82 D_hypoleucum_1273 0.6 65590
## 83 D_hypoleucum_1275 0.6 49600
## 84 D_hypoleucum_25921 0.6 58984
## 85 D_hypoleucum_3274 0.6 57740
## 86 D_hypoleucum_26984 0.6 63989
## 87 D_hypoleucum_3208 0.6 64870
## 88 D_hypoleucum_3158 0.6 65443
## 89 D_hypoleucum_2253 0.6 57370
## 90 D_hypoleucum_3095 0.6 61178
## 91 D_hypoleucum_462070 0.6 63547
## 92 D_hypoleucum_25675 0.6 39939
## 93 D_hypoleucum_454950 0.6 64997
## 94 D_hypoleucum_25880 0.6 57854
## 95 D_hypoleucum_3314 0.6 63991
## 96 D_hypoleucum_357608 0.6 40601
## 97 D_hypoleucum_357615 0.6 65342
## 98 D_hypoleucum_357612 0.6 36032
## 99 D_hypoleucum_19638 0.6 59324
## 100 D_hypoleucum_25868 0.6 65354
## 101 D_hypoleucum_17969 0.6 58527
## 102 D_hypoleucum_25672 0.6 41468
## 103 D_hypoleucum_26975 0.6 57578
## 104 D_hypoleucum_2229 0.6 47430
## 105 D_hypoleucum_2067 0.6 66059
## 106 D_hypoleucum_28329 0.6 63738
## 107 D_hypoleucum_28361 0.6 51416
## 108 D_hypoleucum_28376 0.6 56036
## 109 D_hypoleucum_2208 0.6 65821
## 110 D_hypoleucum_28294 0.6 51968
## 111 D_hypoleucum_1956 0.6 66784
## 112 D_hypoleucum_25670 0.6 52767
## 113 D_hypoleucum_20921 0.6 66196
## 114 D_hypoleucum_20193 0.6 37341
## 115 D_hypoleucum_28416 0.6 61005
## 116 D_hypoleucum_18193 0.6 65329
## 117 D_hypoleucum_27450 0.6 65793
## 118 D_hypoleucum_19046 0.6 57096
## 119 D_hypoleucum_19136 0.6 59218
## 120 D_hypoleucum_28676 0.6 63096
## 121 D_nigrilore_KU28413 0.6 52730
## 122 D_nigrilore_KU28414 0.6 54986
## 123 D_hypoleucum_29945 0.6 65331
## 124 D_hypoleucum_31636 0.6 65145
## 125 D_hypoleucum_29951 0.6 62654
## 126 D_hypoleucum_31644 0.6 63346
## 127 D_hypoleucum_18070 0.7 46250
## 128 D_hypoleucum_18191 0.7 46917
## 129 D_hypoleucum_14037 0.7 45940
## 130 D_hypoleucum_14079 0.7 40450
## 131 D_hypoleucum_14065 0.7 44765
## 132 D_hypoleucum_14120 0.7 30839
## 133 D_hypoleucum_17976 0.7 25388
## 134 D_hypoleucum_18159 0.7 45366
## 135 D_hypoleucum_14075 0.7 43208
## 136 D_hypoleucum_20218 0.7 40280
## 137 D_hypoleucum_19066 0.7 23463
## 138 D_hypoleucum_19177 0.7 46811
## 139 D_hypoleucum_19178 0.7 46325
## 140 D_hypoleucum_14146 0.7 24959
## 141 D_hypoleucum_20213 0.7 35396
## 142 D_hypoleucum_FMNH454949 0.7 37435
## 143 D_hypoleucum_25637 0.7 45900
## 144 D_hypoleucum_1271 0.7 44130
## 145 D_hypoleucum_1273 0.7 47527
## 146 D_hypoleucum_1275 0.7 34457
## 147 D_hypoleucum_25921 0.7 42260
## 148 D_hypoleucum_3274 0.7 40688
## 149 D_hypoleucum_26984 0.7 45975
## 150 D_hypoleucum_3208 0.7 46547
## 151 D_hypoleucum_3158 0.7 47346
## 152 D_hypoleucum_2253 0.7 42719
## 153 D_hypoleucum_3095 0.7 44832
## 154 D_hypoleucum_462070 0.7 45405
## 155 D_hypoleucum_25675 0.7 30064
## 156 D_hypoleucum_454950 0.7 47221
## 157 D_hypoleucum_25880 0.7 40835
## 158 D_hypoleucum_3314 0.7 46664
## 159 D_hypoleucum_357608 0.7 30345
## 160 D_hypoleucum_357615 0.7 46858
## 161 D_hypoleucum_357612 0.7 27495
## 162 D_hypoleucum_19638 0.7 43688
## 163 D_hypoleucum_25868 0.7 47240
## 164 D_hypoleucum_17969 0.7 41687
## 165 D_hypoleucum_25672 0.7 31019
## 166 D_hypoleucum_26975 0.7 42627
## 167 D_hypoleucum_2229 0.7 35465
## 168 D_hypoleucum_2067 0.7 47550
## 169 D_hypoleucum_28329 0.7 46794
## 170 D_hypoleucum_28361 0.7 36628
## 171 D_hypoleucum_28376 0.7 40345
## 172 D_hypoleucum_2208 0.7 47613
## 173 D_hypoleucum_28294 0.7 36843
## 174 D_hypoleucum_1956 0.7 47959
## 175 D_hypoleucum_25670 0.7 39754
## 176 D_hypoleucum_20921 0.7 47391
## 177 D_hypoleucum_20193 0.7 27971
## 178 D_hypoleucum_28416 0.7 44200
## 179 D_hypoleucum_18193 0.7 46632
## 180 D_hypoleucum_27450 0.7 47454
## 181 D_hypoleucum_19046 0.7 40743
## 182 D_hypoleucum_19136 0.7 42205
## 183 D_hypoleucum_28676 0.7 45089
## 184 D_nigrilore_KU28413 0.7 39038
## 185 D_nigrilore_KU28414 0.7 39764
## 186 D_hypoleucum_29945 0.7 47370
## 187 D_hypoleucum_31636 0.7 46995
## 188 D_hypoleucum_29951 0.7 44864
## 189 D_hypoleucum_31644 0.7 46351
## 190 D_hypoleucum_18070 0.8 23008
## 191 D_hypoleucum_18191 0.8 23340
## 192 D_hypoleucum_14037 0.8 22784
## 193 D_hypoleucum_14079 0.8 21227
## 194 D_hypoleucum_14065 0.8 22274
## 195 D_hypoleucum_14120 0.8 16317
## 196 D_hypoleucum_17976 0.8 13692
## 197 D_hypoleucum_18159 0.8 22717
## 198 D_hypoleucum_14075 0.8 21843
## 199 D_hypoleucum_20218 0.8 20799
## 200 D_hypoleucum_19066 0.8 12903
## 201 D_hypoleucum_19177 0.8 23346
## 202 D_hypoleucum_19178 0.8 23085
## 203 D_hypoleucum_14146 0.8 13558
## 204 D_hypoleucum_20213 0.8 18965
## 205 D_hypoleucum_FMNH454949 0.8 19615
## 206 D_hypoleucum_25637 0.8 22793
## 207 D_hypoleucum_1271 0.8 22216
## 208 D_hypoleucum_1273 0.8 23477
## 209 D_hypoleucum_1275 0.8 17998
## 210 D_hypoleucum_25921 0.8 21498
## 211 D_hypoleucum_3274 0.8 20545
## 212 D_hypoleucum_26984 0.8 22952
## 213 D_hypoleucum_3208 0.8 23091
## 214 D_hypoleucum_3158 0.8 23255
## 215 D_hypoleucum_2253 0.8 22029
## 216 D_hypoleucum_3095 0.8 22559
## 217 D_hypoleucum_462070 0.8 22609
## 218 D_hypoleucum_25675 0.8 16111
## 219 D_hypoleucum_454950 0.8 23330
## 220 D_hypoleucum_25880 0.8 20945
## 221 D_hypoleucum_3314 0.8 23270
## 222 D_hypoleucum_357608 0.8 16081
## 223 D_hypoleucum_357615 0.8 23068
## 224 D_hypoleucum_357612 0.8 15103
## 225 D_hypoleucum_19638 0.8 22099
## 226 D_hypoleucum_25868 0.8 23429
## 227 D_hypoleucum_17969 0.8 21357
## 228 D_hypoleucum_25672 0.8 16416
## 229 D_hypoleucum_26975 0.8 21598
## 230 D_hypoleucum_2229 0.8 18202
## 231 D_hypoleucum_2067 0.8 23497
## 232 D_hypoleucum_28329 0.8 23158
## 233 D_hypoleucum_28361 0.8 19196
## 234 D_hypoleucum_28376 0.8 20922
## 235 D_hypoleucum_2208 0.8 23394
## 236 D_hypoleucum_28294 0.8 19190
## 237 D_hypoleucum_1956 0.8 23595
## 238 D_hypoleucum_25670 0.8 20870
## 239 D_hypoleucum_20921 0.8 23380
## 240 D_hypoleucum_20193 0.8 15025
## 241 D_hypoleucum_28416 0.8 22394
## 242 D_hypoleucum_18193 0.8 23043
## 243 D_hypoleucum_27450 0.8 23464
## 244 D_hypoleucum_19046 0.8 20824
## 245 D_hypoleucum_19136 0.8 21414
## 246 D_hypoleucum_28676 0.8 22688
## 247 D_nigrilore_KU28413 0.8 20070
## 248 D_nigrilore_KU28414 0.8 20117
## 249 D_hypoleucum_29945 0.8 23414
## 250 D_hypoleucum_31636 0.8 23227
## 251 D_hypoleucum_29951 0.8 22688
## 252 D_hypoleucum_31644 0.8 23044
## 253 D_hypoleucum_18070 0.9 4692
## 254 D_hypoleucum_18191 0.9 4760
## 255 D_hypoleucum_14037 0.9 4657
## 256 D_hypoleucum_14079 0.9 4482
## 257 D_hypoleucum_14065 0.9 4603
## 258 D_hypoleucum_14120 0.9 3788
## 259 D_hypoleucum_17976 0.9 3325
## 260 D_hypoleucum_18159 0.9 4668
## 261 D_hypoleucum_14075 0.9 4623
## 262 D_hypoleucum_20218 0.9 4525
## 263 D_hypoleucum_19066 0.9 3289
## 264 D_hypoleucum_19177 0.9 4752
## 265 D_hypoleucum_19178 0.9 4691
## 266 D_hypoleucum_14146 0.9 3429
## 267 D_hypoleucum_20213 0.9 4346
## 268 D_hypoleucum_FMNH454949 0.9 4390
## 269 D_hypoleucum_25637 0.9 4710
## 270 D_hypoleucum_1271 0.9 4613
## 271 D_hypoleucum_1273 0.9 4790
## 272 D_hypoleucum_1275 0.9 4118
## 273 D_hypoleucum_25921 0.9 4611
## 274 D_hypoleucum_3274 0.9 4449
## 275 D_hypoleucum_26984 0.9 4662
## 276 D_hypoleucum_3208 0.9 4712
## 277 D_hypoleucum_3158 0.9 4707
## 278 D_hypoleucum_2253 0.9 4638
## 279 D_hypoleucum_3095 0.9 4679
## 280 D_hypoleucum_462070 0.9 4658
## 281 D_hypoleucum_25675 0.9 3893
## 282 D_hypoleucum_454950 0.9 4730
## 283 D_hypoleucum_25880 0.9 4549
## 284 D_hypoleucum_3314 0.9 4727
## 285 D_hypoleucum_357608 0.9 3790
## 286 D_hypoleucum_357615 0.9 4678
## 287 D_hypoleucum_357612 0.9 3629
## 288 D_hypoleucum_19638 0.9 4632
## 289 D_hypoleucum_25868 0.9 4730
## 290 D_hypoleucum_17969 0.9 4542
## 291 D_hypoleucum_25672 0.9 3920
## 292 D_hypoleucum_26975 0.9 4614
## 293 D_hypoleucum_2229 0.9 4134
## 294 D_hypoleucum_2067 0.9 4754
## 295 D_hypoleucum_28329 0.9 4721
## 296 D_hypoleucum_28361 0.9 4306
## 297 D_hypoleucum_28376 0.9 4585
## 298 D_hypoleucum_2208 0.9 4723
## 299 D_hypoleucum_28294 0.9 4408
## 300 D_hypoleucum_1956 0.9 4770
## 301 D_hypoleucum_25670 0.9 4495
## 302 D_hypoleucum_20921 0.9 4789
## 303 D_hypoleucum_20193 0.9 3691
## 304 D_hypoleucum_28416 0.9 4635
## 305 D_hypoleucum_18193 0.9 4701
## 306 D_hypoleucum_27450 0.9 4755
## 307 D_hypoleucum_19046 0.9 4474
## 308 D_hypoleucum_19136 0.9 4624
## 309 D_hypoleucum_28676 0.9 4680
## 310 D_nigrilore_KU28413 0.9 4303
## 311 D_nigrilore_KU28414 0.9 4345
## 312 D_hypoleucum_29945 0.9 4742
## 313 D_hypoleucum_31636 0.9 4734
## 314 D_hypoleucum_29951 0.9 4680
## 315 D_hypoleucum_31644 0.9 4750
## 316 D_hypoleucum_18070 1.0 2
## 317 D_hypoleucum_18191 1.0 2
## 318 D_hypoleucum_14037 1.0 2
## 319 D_hypoleucum_14079 1.0 2
## 320 D_hypoleucum_14065 1.0 2
## 321 D_hypoleucum_14120 1.0 2
## 322 D_hypoleucum_17976 1.0 2
## 323 D_hypoleucum_18159 1.0 2
## 324 D_hypoleucum_14075 1.0 2
## 325 D_hypoleucum_20218 1.0 2
## 326 D_hypoleucum_19066 1.0 2
## 327 D_hypoleucum_19177 1.0 2
## 328 D_hypoleucum_19178 1.0 2
## 329 D_hypoleucum_14146 1.0 2
## 330 D_hypoleucum_20213 1.0 2
## 331 D_hypoleucum_FMNH454949 1.0 2
## 332 D_hypoleucum_25637 1.0 2
## 333 D_hypoleucum_1271 1.0 2
## 334 D_hypoleucum_1273 1.0 2
## 335 D_hypoleucum_1275 1.0 2
## 336 D_hypoleucum_25921 1.0 2
## 337 D_hypoleucum_3274 1.0 2
## 338 D_hypoleucum_26984 1.0 2
## 339 D_hypoleucum_3208 1.0 2
## 340 D_hypoleucum_3158 1.0 2
## 341 D_hypoleucum_2253 1.0 2
## 342 D_hypoleucum_3095 1.0 2
## 343 D_hypoleucum_462070 1.0 2
## 344 D_hypoleucum_25675 1.0 2
## 345 D_hypoleucum_454950 1.0 2
## 346 D_hypoleucum_25880 1.0 2
## 347 D_hypoleucum_3314 1.0 2
## 348 D_hypoleucum_357608 1.0 2
## 349 D_hypoleucum_357615 1.0 2
## 350 D_hypoleucum_357612 1.0 2
## 351 D_hypoleucum_19638 1.0 2
## 352 D_hypoleucum_25868 1.0 2
## 353 D_hypoleucum_17969 1.0 2
## 354 D_hypoleucum_25672 1.0 2
## 355 D_hypoleucum_26975 1.0 2
## 356 D_hypoleucum_2229 1.0 2
## 357 D_hypoleucum_2067 1.0 2
## 358 D_hypoleucum_28329 1.0 2
## 359 D_hypoleucum_28361 1.0 2
## 360 D_hypoleucum_28376 1.0 2
## 361 D_hypoleucum_2208 1.0 2
## 362 D_hypoleucum_28294 1.0 2
## 363 D_hypoleucum_1956 1.0 2
## 364 D_hypoleucum_25670 1.0 2
## 365 D_hypoleucum_20921 1.0 2
## 366 D_hypoleucum_20193 1.0 2
## 367 D_hypoleucum_28416 1.0 2
## 368 D_hypoleucum_18193 1.0 2
## 369 D_hypoleucum_27450 1.0 2
## 370 D_hypoleucum_19046 1.0 2
## 371 D_hypoleucum_19136 1.0 2
## 372 D_hypoleucum_28676 1.0 2
## 373 D_nigrilore_KU28413 1.0 2
## 374 D_nigrilore_KU28414 1.0 2
## 375 D_hypoleucum_29945 1.0 2
## 376 D_hypoleucum_31636 1.0 2
## 377 D_hypoleucum_29951 1.0 2
## 378 D_hypoleucum_31644 1.0 2
#investigate patterns of missingness per SNP
missing_by_snp(v)
## cutoff is not specified, exploratory visualizations will be generated
## Picking joint bandwidth of 0.0671
## filt missingness snps.retained
## 1 0.30 0.35380374 118045
## 2 0.50 0.27764467 90571
## 3 0.60 0.24277876 75418
## 4 0.65 0.22409186 66391
## 5 0.70 0.19504400 51513
## 6 0.75 0.16960576 38350
## 7 0.80 0.13985079 24435
## 8 0.85 0.10807756 12851
## 9 0.90 0.07399186 4827
## 10 0.95 0.03932816 963
## 11 1.00 0.00000000 2
#implement per sample completeness filter
v<-missing_by_sample(v, cutoff = .785)
## 3 samples are above a 0.785 missing data cutoff, and were removed from VCF
#remove invariant sites generated by dropping individuals
v<-min_mac(v, min.mac = 1)
## 0.64% of SNPs fell below a minor allele count of 1 and were removed from the VCF
#fix popmap
pops<-pops[pops$ID %in% colnames(v@gt),]
popmap<-popmap[popmap$ID %in% colnames(v@gt),]
colnames(popmap)<-c('id','pop')
missing_by_snp(v)
## cutoff is not specified, exploratory visualizations will be generated
## Picking joint bandwidth of 0.0636
## filt missingness snps.retained
## 1 0.30 0.34418169 119207
## 2 0.50 0.26937724 93086
## 3 0.60 0.23288863 77879
## 4 0.65 0.21423534 69242
## 5 0.70 0.19260721 58548
## 6 0.75 0.16827150 46121
## 7 0.80 0.14037959 32140
## 8 0.85 0.10845811 18499
## 9 0.90 0.07491050 8101
## 10 0.95 0.03711678 1881
## 11 1.00 0.00000000 62
#reorder popmap to match vcf, this is a bug, you shouldn't have to do this
popmaps<-popmap[order(match(popmap$id,colnames(v@gt)[-1])),]
miss<-assess_missing_data_tsne(v, popmap=popmaps, threshold=c(.85,.9,.95), clustering = FALSE)
## cutoff is specified, filtered vcfR object will be returned
## 91% of SNPs fell below a completeness cutoff of 0.85 and were removed from the VCF
## Loading required namespace: adegenet
## cutoff is specified, filtered vcfR object will be returned
## 97.32% of SNPs fell below a completeness cutoff of 0.9 and were removed from the VCF
## cutoff is specified, filtered vcfR object will be returned
## 99.08% of SNPs fell below a completeness cutoff of 0.95 and were removed from the VCF
#missing data doesn't seem to be driving clustering regardless of threhsold
#implement missing data per SNP filter
v<-missing_by_snp(v, cutoff=.85)
## cutoff is specified, filtered vcfR object will be returned
## 91% of SNPs fell below a completeness cutoff of 0.85 and were removed from the VCF
#investigate clustering patterns with a minor allele cutoff
v.mac<-min_mac(v, min.mac = 2)
## 40.57% of SNPs fell below a minor allele count of 2 and were removed from the VCF
v.mac
## ***** Object of Class vcfR *****
## 60 samples
## 2389 CHROMs
## 10,994 variants
## Object size: 54.9 Mb
## 11.01 percent missing data
## ***** ***** *****
assess_missing_data_tsne(v.mac, popmap=popmaps, clustering = FALSE)
## tsne.ax1 tsne.ax2 popmap.pop
## 1 23.285083 44.283749 Luzon
## 2 52.012789 -29.714065 Zamboanga
## 3 3.045111 -1.630845 Mindanao
## 4 1.823689 -3.004490 Mindanao
## 5 5.069517 -1.928278 Mindanao
## 6 -21.486003 -20.117547 Mindanao
## 7 51.187124 -27.261521 Zamboanga
## 8 3.688963 -3.300658 Mindanao
## 9 36.229384 36.102958 Luzon
## 10 51.127101 -31.785423 Zamboanga
## 11 50.403871 -29.546373 Zamboanga
## 12 26.245166 47.584687 Luzon
## 13 24.933394 48.755476 Luzon
## 14 38.368464 37.057353 Luzon
## 15 -32.112363 -7.306372 Mindanao
## 16 -40.417793 -11.877839 Mindanao
## 17 -30.359613 -8.896437 Mindanao
## 18 34.622797 37.416804 Luzon
## 19 -41.437414 -14.264191 Mindanao
## 20 34.119261 35.824224 Luzon
## 21 -31.498155 -11.099356 Mindanao
## 22 -22.038848 -26.867581 Mindanao
## 23 -21.399663 -31.884239 Mindanao
## 24 -32.210442 -9.779238 Mindanao
## 25 28.078437 42.480649 Luzon
## 26 20.932720 53.772649 Luzon
## 27 35.526005 39.006265 Luzon
## 28 27.468343 44.819879 Luzon
## 29 -36.804177 -8.645378 Mindanao
## 30 -17.671575 -19.275233 Mindanao
## 31 -21.809102 -29.518483 Mindanao
## 32 -18.646999 -20.203929 Mindanao
## 33 24.864162 44.419736 Luzon
## 34 24.022050 39.589388 Luzon
## 35 21.760675 41.772599 Luzon
## 36 22.679416 53.651250 Luzon
## 37 24.143084 46.289444 Luzon
## 38 -22.493396 -32.838849 Mindanao
## 39 -22.188346 -35.655099 Mindanao
## 40 -35.332834 -16.593551 Mindanao
## 41 -24.290429 -19.816074 Mindanao
## 42 -24.114153 -17.526218 Mindanao
## 43 -20.520434 -35.460526 Mindanao
## 44 -36.534331 -15.110493 Mindanao
## 45 -20.740588 -37.575934 Mindanao
## 46 22.013085 45.310476 Luzon
## 47 -42.849258 -11.619304 Mindanao
## 48 21.772676 55.228000 Luzon
## 49 -39.616292 -10.707290 Mindanao
## 50 53.613577 -31.041270 Zamboanga
## 51 -25.534335 -16.229277 Mindanao
## 52 -26.884330 -15.495748 Mindanao
## 53 -26.351991 -20.047901 Mindanao
## 54 -34.322229 -14.904292 Mindanao
## 55 -14.544637 -8.102419 nigrilore
## 56 -14.891110 -6.991050 nigrilore
## 57 50.735209 -33.781664 Zamboanga
## 58 -30.879867 -18.007916 Mindanao
## 59 49.243578 -31.930041 Zamboanga
## 60 -33.034026 -16.023193 Mindanao
#seeing potentially spurious groupings with singletons removed, we will ignore for now
#plot depth per snp and per sample
dp <- extract.gt(v, element = "DP", as.numeric=TRUE)
heatmap.bp(dp, rlabels = FALSE)
#plot genotype quality per snp and per sample
gq <- extract.gt(v, element = "GQ", as.numeric=TRUE)
heatmap.bp(gq, rlabels = FALSE)
#write out vcf with all SNPs
#vcfR::write.vcf(v, "~/Desktop/phil.dicaeum/filtered.85.vcf.gz")
#linkage filter vcf to thin SNPs to one per 500bp
v.thin<-distance_thin(v, min.distance = 500)
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## 2590 out of 18499 input SNPs were not located within 500 base-pairs of another SNP and were retained despite filtering
#write out thinned vcf
#vcfR::write.vcf(v.thin, "~/Desktop/phil.dicaeum/filtered.85.unlinked.vcf.gz")