Clusters the data with one of the following methods:
- immunr_hclust clusters the data using the hierarchical clustering from hcut;
- immunr_kmeans clusters the data using the K-means algorithm from kmeans;
- immunr_dbscan clusters the data using the DBSCAN algorithm from dbscan.
immunr_hclust(.data, .k = 2, .k.max = nrow(.data) - 1, .method = "complete", .dist = TRUE)
immunr_kmeans(.data, .k = 2, .k.max = as.integer(sqrt(nrow(.data))) + 1,
.method = c("silhouette", "gap_stat"))
immunr_dbscan(.data, .eps, .dist = TRUE)Arguments
- .data
Matrix or data frame with features, distance matrix or output from repOverlapAnalysis or geneUsageAnalysis functions.
- .k
The number of clusters to create, defined as
kto hcut or ascentersto kmeans.- .k.max
Limits the maximum number of clusters. It is passed as
k.maxto fviz_nbclust forimmunr_hclustandimmunr_kmeans.- .method
Passed to hcut or as fviz_nbclust.
In case of hcut the agglomeration method is going to be used (argument
hc_method).In case of fviz_nbclust it is the method to be used for estimating the optimal number of clusters (argument
method).- .dist
If TRUE then ".data" is expected to be a distance matrix. If FALSE then the euclidean distance is computed for the input objects.
- .eps
Local radius for expanding clusters, minimal distance between points to expand clusters. Passed as
epsto dbscan.
Value
immunr_hclust - list with two elements. The first element is an output from hcut.
The second element is an output from fviz_nbclust
immunr_kmeans - list with three elements. The first element is an output from kmeans.
The second element is an output from fviz_nbclust.
The third element is the input dataset .data.
immunr_dbscan - list with two elements. The first element is an output from dbscan.
The second element is the input dataset .data.
Examples
data(immdata)
gu <- geneUsage(immdata$data, .norm = TRUE)
immunr_hclust(t(as.matrix(gu[, -1])), .dist = FALSE)
#> $hcut
#>
#> Call:
#> stats::hclust(d = x, method = hc_method)
#>
#> Cluster method : complete
#> Distance : euclidean
#> Number of objects: 12
#>
#>
#> $nbclust
#>
#> attr(,"class")
#> [1] "immunr_hclust" "list"
gu[is.na(gu)] <- 0
immunr_kmeans(t(as.matrix(gu[, -1])))
#> $kmeans
#> K-means clustering with 2 clusters of sizes 8, 4
#>
#> Cluster means:
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> 1 0.003217927 0.005136958 0.03808497 0.002784769 0.02163279 0.001354764
#> 2 0.002404951 0.005592294 0.03748111 0.002902743 0.01969097 0.002277234
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> 1 0.07282388 0.003681283 0.007062313 0.007135019 0.01712132 0.0009044963
#> 2 0.09805826 0.003895011 0.005357461 0.008832893 0.01636634 0.0009559951
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> 1 0.01055184 0.004005534 0.02206314 0.09415083 0.007917676 0.0005239859
#> 2 0.01451586 0.004843118 0.02369379 0.09298495 0.006958857 0.0004885246
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> 1 0.01181950 0.012562645 0.03086468 0.03558444 0.08733554 0.02499238
#> 2 0.01448042 0.008492464 0.02489818 0.04641556 0.07148150 0.02866064
#> [,25] [,26] [,27] [,28] [,29] [,30]
#> 1 0.003829546 0.03453340 0.02073357 0.0300495338 0.0682959 0.002492186
#> 2 0.006282781 0.04145543 0.01610477 0.0003515766 0.1121346 0.003280249
#> [,31] [,32] [,33] [,34] [,35] [,36]
#> 1 0.01009462 0.009413752 0.001458858 0.01183709 0.02104105 0.009513854
#> 2 0.01453411 0.013339090 0.001442820 0.01395531 0.01135077 0.009440908
#> [,37] [,38] [,39] [,40] [,41] [,42]
#> 1 0.03355204 0.02163971 0.0003137076 3.862787e-05 0.06556093 0.01877986
#> 2 0.03261857 0.02226965 0.0001722818 3.357057e-05 0.03685651 0.02487504
#> [,43] [,44] [,45] [,46] [,47] [,48]
#> 1 0.0010012403 0.01051308 0.004479564 0.01563413 0.04882278 0.03305829
#> 2 0.0006173741 0.01162180 0.003929323 0.01415173 0.04569534 0.02175729
#>
#> Clustering vector:
#> A2-i129 A2-i131 A2-i133 A2-i132 A4-i191 A4-i192 MS1 MS2 MS3 MS4
#> 2 1 1 1 1 1 1 2 1 2
#> MS5 MS6
#> 1 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 0.022967774 0.009796951
#> (between_SS / total_SS = 29.4 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"
#>
#> $nbclust
#>
#> $data
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> A2-i129 0.0036742192 0.006429884 0.03521127 0.003214942 0.02801592 0.0012247397
#> A2-i131 0.0042728521 0.009156112 0.04303373 0.002136426 0.02624752 0.0016786205
#> A2-i133 0.0000000000 0.001251369 0.02111685 0.004066948 0.01955264 0.0007821054
#> A2-i132 0.0023361075 0.004234195 0.01576873 0.002482114 0.02350708 0.0035041612
#> A4-i191 0.0056354450 0.005441119 0.07306646 0.003303537 0.01846094 0.0003886514
#> A4-i192 0.0010303967 0.002747725 0.03692255 0.002747725 0.01940580 0.0012021295
#> MS1 0.0035152636 0.004625347 0.03607771 0.002590194 0.01739130 0.0007400555
#> MS2 0.0005598321 0.004898530 0.01987404 0.001399580 0.01469559 0.0018194542
#> MS3 0.0066553165 0.009750813 0.04705154 0.002476397 0.02693082 0.0020120724
#> MS4 0.0025513630 0.005237008 0.03518195 0.004297032 0.02336511 0.0017456694
#> MS5 0.0022980378 0.003888987 0.03164221 0.002474810 0.02156620 0.0005303164
#> MS6 0.0028343906 0.005803752 0.05965717 0.002699420 0.01268727 0.0043190714
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> A2-i129 0.09231476 0.005664421 0.006736069 0.0099510104 0.01347214 0.0013778322
#> A2-i131 0.07004425 0.008240501 0.008087899 0.0138867694 0.02151686 0.0013734168
#> A2-i133 0.04895980 0.001251369 0.007038949 0.0075082121 0.01595495 0.0010949476
#> A2-i132 0.06322091 0.005548255 0.011388524 0.0106584903 0.01226456 0.0010220470
#> A4-i191 0.06471045 0.003497862 0.005635445 0.0077730276 0.01496308 0.0011659541
#> A4-i192 0.09565516 0.002919457 0.007384510 0.0051519835 0.01270823 0.0008586639
#> MS1 0.07511563 0.001295097 0.007215541 0.0042553191 0.02035153 0.0009250694
#> MS2 0.08481456 0.002379286 0.003918824 0.0131560532 0.02239328 0.0006997901
#> MS3 0.06995821 0.003869370 0.004798019 0.0032502709 0.01888253 0.0006190992
#> MS4 0.08674634 0.004297032 0.004431315 0.0112797100 0.01826239 0.0016113871
#> MS5 0.09492664 0.002828354 0.004949620 0.0045960757 0.02032880 0.0001767721
#> MS6 0.12835740 0.003239304 0.006343636 0.0009447969 0.01133756 0.0001349710
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> A2-i129 0.016993264 0.004898959 0.03735456 0.07853644 0.007960808 0.0004592774
#> A2-i131 0.017091409 0.009308714 0.02990996 0.08698306 0.014191973 0.0013734168
#> A2-i133 0.018301267 0.003597685 0.02455811 0.09400907 0.009228844 0.0014077898
#> A2-i132 0.008322383 0.006570302 0.02409111 0.07825960 0.009344430 0.0007300336
#> A4-i191 0.006412748 0.002137583 0.02234745 0.09405363 0.009716284 0.0001943257
#> A4-i192 0.008071441 0.001373862 0.02610338 0.10630259 0.007384510 0.0000000000
#> MS1 0.007030527 0.003330250 0.01110083 0.11285846 0.004810361 0.0000000000
#> MS2 0.018894332 0.005738279 0.02379286 0.09769069 0.009377187 0.0004198740
#> MS3 0.008048290 0.004488469 0.01965640 0.08899551 0.004952794 0.0003095496
#> MS4 0.018396670 0.007385524 0.02242514 0.10433732 0.005908419 0.0009399758
#> MS5 0.011136645 0.001237405 0.01873785 0.09174474 0.003712215 0.0001767721
#> MS6 0.003779187 0.001349710 0.01120259 0.09137535 0.004589013 0.0001349710
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> A2-i129 0.016533987 0.008879363 0.02372933 0.05541947 0.06736069 0.03628291
#> A2-i131 0.012513353 0.013123760 0.02945216 0.01907523 0.05829391 0.03403022
#> A2-i133 0.012200845 0.012044424 0.02690443 0.07805412 0.09682465 0.02205537
#> A2-i132 0.012848591 0.011680537 0.03285151 0.05095634 0.07738356 0.02978537
#> A4-i191 0.009327633 0.018460941 0.03109211 0.02973183 0.11815002 0.02701127
#> A4-i192 0.008243174 0.020436201 0.03262923 0.02472952 0.09582689 0.02679031
#> MS1 0.014246068 0.007955597 0.03848289 0.04680851 0.09491212 0.01831637
#> MS2 0.008537439 0.008397481 0.02029391 0.07109867 0.09587124 0.02897131
#> MS3 0.013155858 0.009905587 0.03234793 0.01516793 0.08481659 0.01702523
#> MS4 0.016113871 0.008459782 0.03383913 0.04740164 0.05156439 0.02846784
#> MS5 0.012020506 0.006894113 0.02315715 0.02015202 0.07247658 0.02492487
#> MS6 0.016736402 0.008233230 0.02173033 0.01174248 0.07112971 0.02092050
#> [,25] [,26] [,27] [,28] [,29] [,30]
#> A2-i129 0.0067360686 0.03505818 0.01607471 0.0004592774 0.11007348 0.0033680343
#> A2-i131 0.0132763620 0.01022432 0.01327636 0.0279261407 0.06928125 0.0035098428
#> A2-i133 0.0034412639 0.01908337 0.01423432 0.0437979040 0.05615517 0.0028155795
#> A2-i132 0.0002920134 0.03562564 0.02263104 0.0487662432 0.04555410 0.0017520806
#> A4-i191 0.0025262340 0.06276720 0.03186941 0.0000000000 0.06101827 0.0007773028
#> A4-i192 0.0030911901 0.03245750 0.01992100 0.0494590417 0.05117637 0.0008586639
#> MS1 0.0016651249 0.04051804 0.02331175 0.0205365402 0.09435708 0.0027752081
#> MS2 0.0061581526 0.03247026 0.01385584 0.0001399580 0.13505948 0.0016794962
#> MS3 0.0038693701 0.03281226 0.01764433 0.0256926172 0.08079245 0.0035598205
#> MS4 0.0088626292 0.04592453 0.01248825 0.0005371290 0.10447160 0.0048341614
#> MS5 0.0024748100 0.04277886 0.02298038 0.0242177833 0.08803253 0.0038889871
#> MS6 0.0033742745 0.05236874 0.02200027 0.0002699420 0.09893373 0.0032393035
#> [,31] [,32] [,33] [,34] [,35]
#> A2-i129 0.015156154 0.006736069 0.0004592774 0.011635028 0.010104103
#> A2-i131 0.015107584 0.008393102 0.0015260186 0.013886769 0.021059057
#> A2-i133 0.008603160 0.009385265 0.0007821054 0.009072423 0.028625059
#> A2-i132 0.009490437 0.010512484 0.0024821142 0.011388524 0.016206746
#> A4-i191 0.005635445 0.005829771 0.0005829771 0.014768752 0.015157404
#> A4-i192 0.003949854 0.005838915 0.0006869311 0.010303967 0.026446849
#> MS1 0.007400555 0.011655874 0.0009250694 0.009990749 0.012025902
#> MS2 0.014695591 0.011756473 0.0016794962 0.014835549 0.014975507
#> MS3 0.016251354 0.011143786 0.0018572976 0.011143786 0.028478564
#> MS4 0.011548274 0.018262388 0.0022827984 0.014233920 0.011145428
#> MS5 0.014318543 0.012550822 0.0028283543 0.014141771 0.020328796
#> MS6 0.016736402 0.016601431 0.0013497098 0.015116750 0.009178027
#> [,36] [,37] [,38] [,39] [,40] [,41]
#> A2-i129 0.004592774 0.02602572 0.02908757 0.0000000000 0.0000000000 0.03230251
#> A2-i131 0.007477491 0.02517931 0.03326721 0.0009156112 0.0001526019 0.05295285
#> A2-i133 0.009228844 0.02096043 0.02299390 0.0000000000 0.0001564211 0.07398717
#> A2-i132 0.008176376 0.02715725 0.01971091 0.0001460067 0.0000000000 0.05781866
#> A4-i191 0.014768752 0.03789351 0.01612903 0.0005829771 0.0000000000 0.06024096
#> A4-i192 0.009101838 0.03331616 0.01579942 0.0001717328 0.0000000000 0.08397733
#> MS1 0.005735430 0.03885291 0.02072155 0.0001850139 0.0000000000 0.05513414
#> MS2 0.003778866 0.02687194 0.01609517 0.0004198740 0.0000000000 0.05164451
#> MS3 0.018263427 0.04581334 0.02399009 0.0001547748 0.0000000000 0.06454109
#> MS4 0.006176984 0.03249631 0.02041090 0.0001342823 0.0001342823 0.04431315
#> MS5 0.003358671 0.03924342 0.02050557 0.0003535443 0.0000000000 0.07583525
#> MS6 0.023215009 0.04508031 0.02348495 0.0001349710 0.0000000000 0.01916588
#> [,42] [,43] [,44] [,45] [,46] [,47]
#> A2-i129 0.025413350 0.0003061849 0.012706675 0.004592774 0.008726271 0.05006124
#> A2-i131 0.008545704 0.0018312223 0.013428964 0.005188463 0.023195483 0.05753090
#> A2-i133 0.024558110 0.0009385265 0.010949476 0.003128422 0.017362740 0.06444549
#> A2-i132 0.031829464 0.0002920134 0.009928457 0.007154329 0.016936779 0.05416849
#> A4-i191 0.009716284 0.0011659541 0.007773028 0.002331908 0.010687913 0.03789351
#> A4-i192 0.009273570 0.0012021295 0.006525846 0.002232526 0.012536493 0.04585265
#> MS1 0.024606846 0.0003700278 0.013506013 0.005920444 0.013320999 0.03533765
#> MS2 0.013435969 0.0002799160 0.006298111 0.002099370 0.016515045 0.04534640
#> MS3 0.019965950 0.0006190992 0.010679461 0.005107568 0.014239282 0.04550379
#> MS4 0.016785283 0.0012085404 0.010071170 0.003491339 0.016113871 0.04337317
#> MS5 0.021742973 0.0015909493 0.011313417 0.004772848 0.016793353 0.04984974
#> MS6 0.043865569 0.0006748549 0.017411257 0.005533810 0.015251721 0.04400054
#> [,48]
#> A2-i129 0.02862829
#> A2-i131 0.03784526
#> A2-i133 0.02753011
#> A2-i132 0.06322091
#> A4-i191 0.02720560
#> A4-i192 0.02919457
#> MS1 0.02719704
#> MS2 0.01021693
#> MS3 0.02275190
#> MS4 0.02645361
#> MS5 0.02952095
#> MS6 0.02173033
#>
#> attr(,"class")
#> [1] "immunr_kmeans" "list"