SlicerMorph_Rexamples
Some functions to import SlicerMorph data into R
- read.markups.fcsv.R: utility function to read a FCSV formatted markups file into R as a matrix.
- read.markups.json.R: utility function to read a JSON formatted markups file into R as a matrix.
- geomorph_regression.Rmd: an example of import SlicerMorph GPA output to conduct allometric regression with SlicerMorph data. It also compares results SlicerMorph GPA results to the one derived from R/geomorph using the raw LM coordinates.
- slide_semiLMs.Rmd: an example of importing SlicerMorph GPA output to slide semiLMs in R using geomorph.
- bilat_symmetry.R: a simple R script that demonstrate how to symmetry analysis in R/geomorph using SlicerMorph output. It uses the log_parser.R function below to obtain necessary fields.
- log_parser.R: Since ?/?/? SlicerMorph's GPA module outputs a log file that contains summary of settings used in the GPA analysis, including input/output folders, filenames, formats, number of landmarks, excluded (if any) landmarks, centroid size scaling, and the output files. This Rscript parses this log file and returns a named list with:
- $input.path = Unix style path to input folder with landmark files.
- $output.path = Unix stype path to the output folder created by GPA
- $files = files included in the analysis
- $format = format of landmark files ("fcsv" or "mrk.json")
- $no.LM = number of landmarks original
- $skipped = If any landmark is omitted in GPA (TRUE/FALSE)
- $skippedLM = Indices of skipped LMs (created only if $skipped==TRUE)
- $scale = are data scaled by centroid sizes (TRUE/FALSE)
- $MeanShape = filename that contains mean shape coordinates calculated by GPA (csv format)
- $eigenvalues = filename that contains eigenvalues as calculated by PCA in the SlicerMorph GPA (csv format)
- $eigenvectors = filename that contains eigenvectors as calculated by PCA in the SlicerMorph GPA (csv format)
- $OutputData = filename that contains procrustes distances, centroid sizes and procrustes aligned coordinates as calculated by the SlicerMorph GPA (csv format)
- $pcScores = filename that contains individal PC scores of specimens as calculated by PCA in the SlicerMorph GPA (csv format)
- $ID = list of specimen identifiers
- $LM = 3D landmark array that contains the 3D raw coordinates as inputed to the SlicerMorph GPA module.
- $semi = If any landmarks are tagged as semi-landmarks (TRUE/FALSE)
- $semiLM = indices of LMs tagged as semi-landmarks (created only if $semi==TRUE)
Example for usage:
slicermorph.log = parser("C:/temp/RemoteIO/gorilla_patches/merged/2021-02-14_20_31_08/analysis.log")
slicermorph.log
$input.path
[1] "C:/temp/RemoteIO/gorilla_patches/merged"
$output.path
[1] "C:/temp/RemoteIO/gorilla_patches/merged/2021-04-05_20_51_17"
$files
[1] "USNM174715_LM_merged.mrk.json" "USNM174722_LM_merged.mrk.json" "USNM176209_LM_merged.mrk.json" "USNM176216_LM_merged.mrk.json"
$format
[1] ".mrk.json"
$no.LM
[1] 921
$skipped
[1] FALSE
$semi
[1] TRUE
$semiLMs
[1] " 42" "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60" "61" "62" "63" "64" "65" "66" "67"
[27] "68" "69" "70" "71" "72" "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84" "85" "86" "87" "88" "89" "90" "91" "92" "93"
[53] "94" "95" "96" "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119"
[79] "120" "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132" "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144" "145"
[105] "146" "147" "148" "149" "150" "151" "152" "153" "154" "155" "156" "157" "158" "159" "160" "161" "162" "163" "164" "165" "166" "167" "168" "169" "170" "171"
[131] "172" "173" "174" "175" "176" "177" "178" "179" "180" "181" "182" "183" "184" "185" "186" "187" "188" "189" "190" "191" "192" "193" "194" "195" "196" "197"
[157] "198" "199" "200" "201" "202" "203" "204" "205" "206" "207" "208" "209" "210" "211" "212" "213" "214" "215" "216" "217" "218" "219" "220" "221" "222" "223"
[183] "224" "225" "226" "227" "228" "229" "230" "231" "232" "233" "234" "235" "236" "237" "238" "239" "240" "241" "242" "243" "244" "245" "246" "247" "248" "249"
[209] "250" "251" "252" "253" "254" "255" "256" "257" "258" "259" "260" "261" "262" "263" "264" "265" "266" "267" "268" "269" "270" "271" "272" "273" "274" "275"
[235] "276" "277" "278" "279" "280" "281" "282" "283" "284" "285" "286" "287" "288" "289" "290" "291" "292" "293" "294" "295" "296" "297" "298" "299" "300" "301"
[261] "302" "303" "304" "305" "306" "307" "308" "309" "310" "311" "312" "313" "314" "315" "316" "317" "318" "319" "320" "321" "322" "323" "324" "325" "326" "327"
[287] "328" "329" "330" "331" "332" "333" "334" "335" "336" "337" "338" "339" "340" "341" "342" "343" "344" "345" "346" "347" "348" "349" "350" "351" "352" "353"
[313] "354" "355" "356" "357" "358" "359" "360" "361" "362" "363" "364" "365" "366" "367" "368" "369" "370" "371" "372" "373" "374" "375" "376" "377" "378" "379"
[339] "380" "381" "382" "383" "384" "385" "386" "387" "388" "389" "390" "391" "392" "393" "394" "395" "396" "397" "398" "399" "400" "401" "402" "403" "404" "405"
[365] "406" "407" "408" "409" "410" "411" "412" "413" "414" "415" "416" "417" "418" "419" "420" "421" "422" "423" "424" "425" "426" "427" "428" "429" "430" "431"
[391] "432" "433" "434" "435" "436" "437" "438" "439" "440" "441" "442" "443" "444" "445" "446" "447" "448" "449" "450" "451" "452" "453" "454" "455" "456" "457"
[417] "458" "459" "460" "461" "462" "463" "464" "465" "466" "467" "468" "469" "470" "471" "472" "473" "474" "475" "476" "477" "478" "479" "480" "481" "482" "483"
[443] "484" "485" "486" "487" "488" "489" "490" "491" "492" "493" "494" "495" "496" "497" "498" "499" "500" "501" "502" "503" "504" "505" "506" "507" "508" "509"
[469] "510" "511" "512" "513" "514" "515" "516" "517" "518" "519" "520" "521" "522" "523" "524" "525" "526" "527" "528" "529" "530" "531" "532" "533" "534" "535"
[495] "536" "537" "538" "539" "540" "541" "542" "543" "544" "545" "546" "547" "548" "549" "550" "551" "552" "553" "554" "555" "556" "557" "558" "559" "560" "561"
[521] "562" "563" "564" "565" "566" "567" "568" "569" "570" "571" "572" "573" "574" "575" "576" "577" "578" "579" "580" "581" "582" "583" "584" "585" "586" "587"
[547] "588" "589" "590" "591" "592" "593" "594" "595" "596" "597" "598" "599" "600" "601" "602" "603" "604" "605" "606" "607" "608" "609" "610" "611" "612" "613"
[573] "614" "615" "616" "617" "618" "619" "620" "621" "622" "623" "624" "625" "626" "627" "628" "629" "630" "631" "632" "633" "634" "635" "636" "637" "638" "639"
[599] "640" "641" "642" "643" "644" "645" "646" "647" "648" "649" "650" "651" "652" "653" "654" "655" "656" "657" "658" "659" "660" "661" "662" "663" "664" "665"
[625] "666" "667" "668" "669" "670" "671" "672" "673" "674" "675" "676" "677" "678" "679" "680" "681" "682" "683" "684" "685" "686" "687" "688" "689" "690" "691"
[651] "692" "693" "694" "695" "696" "697" "698" "699" "700" "701" "702" "703" "704" "705" "706" "707" "708" "709" "710" "711" "712" "713" "714" "715" "716" "717"
[677] "718" "719" "720" "721" "722" "723" "724" "725" "726" "727" "728" "729" "730" "731" "732" "733" "734" "735" "736" "737" "738" "739" "740" "741" "742" "743"
[703] "744" "745" "746" "747" "748" "749" "750" "751" "752" "753" "754" "755" "756" "757" "758" "759" "760" "761" "762" "763" "764" "765" "766" "767" "768" "769"
[729] "770" "771" "772" "773" "774" "775" "776" "777" "778" "779" "780" "781" "782" "783" "784" "785" "786" "787" "788" "789" "790" "791" "792" "793" "794" "795"
[755] "796" "797" "798" "799" "800" "801" "802" "803" "804" "805" "806" "807" "808" "809" "810" "811" "812" "813" "814" "815" "816" "817" "818" "819" "820" "821"
[781] "822" "823" "824" "825" "826" "827" "828" "829" "830" "831" "832" "833" "834" "835" "836" "837" "838" "839" "840" "841" "842" "843" "844" "845" "846" "847"
[807] "848" "849" "850" "851" "852" "853" "854" "855" "856" "857" "858" "859" "860" "861" "862" "863" "864" "865" "866" "867" "868" "869" "870" "871" "872" "873"
[833] "874" "875" "876" "877" "878" "879" "880" "881" "882" "883" "884" "885" "886" "887" "888" "889" "890" "891" "892" "893" "894" "895" "896" "897" "898" "899"
[859] "900" "901" "902" "903" "904" "905" "906" "907" "908" "909" "910" "911" "912" "913" "914" "915" "916" "917" "918" "919" "920" "921"
$scale
[1] TRUE
$MeanShape
[1] "MeanShape.csv"
$eigenvalues
[1] "eigenvalues.csv"
$eigenvectors
[1] "eigenvectors.csv"
$OutputData
[1] "OutputData.csv"
$pcScores
[1] "pcScores.csv"
$ID
[1] "USNM174715_LM_merged" "USNM174722_LM_merged" "USNM176209_LM_merged" "USNM176216_LM_merged"
$LM
, , USNM174715_LM_merged
x y z
1 -109.17300 -330.2660 -145.48000
2 -114.15900 -396.1410 -128.71400
3 -110.47600 -302.8060 -87.92080
4 -108.71900 -276.1560 -106.79900
5 -107.35100 -270.1980 -118.94400
6 -105.20600 -247.3930 -160.39200
7 -104.62400 -217.0150 -161.95400
8 -115.18100 -248.3020 -163.41700
9 -96.27470 -250.1250 -162.11100
10 -123.91400 -228.3400 -163.64500
11 -84.58430 -231.1230 -164.44100
12 -124.58600 -246.6700 -150.83100
13 -86.96560 -249.5490 -151.22200
14 -134.27900 -240.9120 -153.56600
15 -77.84840 -245.8070 -155.51900
16 -146.68700 -241.9100 -147.58200
17 -63.74880 -247.7270 -149.09100
18 -109.10900 -317.1660 -49.05330
19 -113.02000 -370.4450 -81.87840
20 -114.57000 -393.8680 -110.42000
21 -115.84400 -423.4980 -113.17500
22 -147.43100 -308.1560 -44.41880
23 -74.25550 -307.9240 -44.05720
24 -167.68400 -305.5140 -62.67320
25 -52.67670 -309.8970 -64.37480
26 -120.20000 -319.3720 -68.03920
27 -98.45590 -319.9960 -69.01920
28 -133.13900 -335.7460 -81.58340
29 -88.64880 -338.1650 -81.65340
30 -189.86500 -319.5460 -97.29880
31 -31.01130 -325.2640 -97.55350
32 -175.70000 -349.7790 -119.93000
33 -48.89370 -354.9990 -119.70800
34 -145.42700 -359.6420 -95.92180
35 -80.53060 -362.3540 -94.77980
36 -133.86200 -388.8390 -99.33610
37 -94.84410 -390.3740 -99.00900
38 -133.22300 -422.5990 -115.62300
39 -98.29100 -424.5970 -114.33800
40 -149.15400 -398.0440 -137.46500
41 -78.33230 -401.6130 -135.32300
42 -132.22856 -342.4400 -82.87096
43 -130.02647 -346.1437 -83.18362
44 -127.67728 -350.5166 -82.26191
45 -125.36446 -354.7242 -81.64531
46 -123.09546 -358.7322 -81.39661
47 -120.81516 -362.7917 -81.05300
48 -118.59048 -366.5982 -81.17604
49 -133.55605 -345.2674 -84.14644
50 -131.30141 -349.2101 -84.01811
51 -128.94194 -353.6297 -83.01037
52 -126.66726 -357.6636 -82.71386
53 -124.44346 -361.4661 -82.84435
54 -122.19733 -365.3702 -82.78739
55 -134.85880 -348.2071 -85.21453
56 -132.58142 -352.2533 -84.89539
57 -130.27034 -356.4529 -84.29346
58 -128.02649 -360.3466 -84.25568
59 -125.80061 -364.1585 -84.36878
60 -136.12250 -351.3246 -85.95471
61 -133.86867 -355.2636 -85.83342
62 -131.63747 -359.0998 -85.90179
63 -129.36984 -363.1017 -85.66451
64 -137.41130 -354.3279 -86.90578
65 -135.22139 -357.9762 -87.32064
66 -133.00676 -361.7370 -87.52803
67 -138.75777 -357.0688 -88.34052
68 -136.59294 -360.6029 -88.96607
69 -140.12634 -359.7092 -89.96101
70 -129.48975 -337.8159 -80.07718
71 -126.79589 -341.0214 -80.76524
72 -124.85319 -345.2919 -80.37954
73 -123.00690 -349.6992 -79.85603
74 -120.97350 -353.8411 -79.59998
75 -119.08329 -358.1861 -79.13925
76 -117.01736 -362.2819 -78.92971
77 -127.79458 -335.6315 -78.94035
78 -125.15065 -338.9077 -79.55704
79 -123.19472 -343.1595 -79.19027
80 -121.19190 -347.3448 -78.89050
81 -119.34605 -351.7527 -78.36636
82 -117.41369 -356.0379 -77.96589
83 -126.19997 -333.5896 -77.65979
84 -123.49902 -336.7850 -78.35799
85 -121.53329 -341.0229 -78.00521
86 -119.56850 -345.2621 -77.65111
87 -117.66492 -349.5881 -77.20948
88 -124.72687 -331.7200 -76.20554
89 -121.98729 -334.8606 -76.95895
90 -119.98711 -339.0497 -76.65543
91 -117.98911 -343.2418 -76.34879
92 -123.24669 -329.8404 -74.76138
93 -120.58500 -333.0915 -75.40346
94 -118.60883 -337.3145 -75.06562
95 -121.82040 -328.0372 -73.24020
96 -119.16814 -331.3016 -73.86880
97 -120.45251 -326.3168 -71.63556
98 -102.49448 -325.8408 -70.22696
99 -104.09470 -330.1659 -72.12097
100 -105.72586 -336.6898 -73.21606
101 -107.34427 -342.3077 -74.64033
102 -108.97501 -348.8018 -75.74623
103 -110.59983 -354.8754 -77.00491
104 -112.22638 -361.0716 -78.21906
105 -104.92935 -327.1106 -69.63148
106 -106.55410 -333.1789 -70.89210
107 -108.16519 -338.2768 -72.50530
108 -109.78030 -343.6608 -74.01456
109 -111.39760 -349.1996 -75.46761
110 -113.01520 -354.7601 -76.91273
111 -107.38916 -330.1529 -68.39196
112 -108.99459 -334.8488 -70.15124
113 -110.59497 -339.1859 -72.04089
114 -112.19718 -343.6532 -73.88326
115 -113.80901 -348.8040 -75.47726
116 -109.83187 -331.9804 -67.59383
117 -111.42061 -335.4908 -69.78389
118 -113.00556 -338.7317 -72.07187
119 -114.59680 -342.4195 -74.19746
120 -112.24746 -331.8811 -67.49583
121 -113.81391 -333.8079 -70.26130
122 -115.39008 -336.4242 -72.77622
123 -114.62249 -328.9003 -68.44486
124 -116.19797 -331.4683 -70.97734
125 -116.99333 -325.6221 -69.50191
126 -99.54158 -326.7212 -72.60339
127 -101.62721 -331.5724 -74.66275
128 -103.13805 -337.3511 -75.94667
129 -104.40447 -343.5243 -76.90086
130 -105.61355 -349.7900 -77.77767
[ reached getOption("max.print") -- omitted 588 row(s) and 3 matrix slice(s) ]