Collect a set of principal variables, reducing the number of not important variables to analyse. Dimensionality reduction makes data analysis algorithms work faster and sometimes more accurate, since it also reduces noise in the data. Currently available methods are:

- immunr_pca performs PCA (Principal Component Analysis) using prcomp;

- immunr_mds performs MDS (Multi-Dimensional Scaling) using isoMDS;

- immunr_tsne performs tSNE (t-Distributed Stochastic Neighbour Embedding) using Rtsne.

immunr_pca(.data, .scale = default_scale_fun, .raw = TRUE, .orig = FALSE, .dist = FALSE)

immunr_mds(.data, .scale = default_scale_fun, .raw = TRUE, .orig = FALSE, .dist = TRUE)

immunr_tsne(.data, .perp = 1, .dist = TRUE, ...)

Arguments

.data

A matrix or a data frame with features, distance matrix or output from repOverlapAnalysis or geneUsageAnalysis functions.

.scale

A function to apply to your data before passing it to any of dimensionality reduction algorithms. There is no scaling by default.

.raw

If TRUE then return non-processed output from dimensionality reduction algorithms. Pass FALSE if you want to visualise results.

.orig

If TRUE then return the original result from algorithms. Pass FALSE if you want to visualise results.

.dist

If TRUE then assume ".data" is a distance matrix.

.perp

The perplexity parameter for Rtsne. Sepcifies the number of neighbours each data point must have in the resulting plot.

...

Other parameters passed to Rtsne.

Value

immunr_pca - an output from prcomp.

immunr_mds - an output from isoMDS.

immunr_tsne - an output from Rtsne.

See also

vis.immunr_pca for visualisations.

Examples

data(immdata)
gu <- geneUsage(immdata$data)
gu[is.na(gu)] <- 0
gu <- t(as.matrix(gu[, -1]))
immunr_pca(gu)
#> Standard deviations (1, .., p=12):
#>  [1] 3.631709e+00 3.196007e+00 2.328286e+00 2.069586e+00 1.827896e+00
#>  [6] 1.740118e+00 1.568858e+00 1.493067e+00 1.305664e+00 1.128147e+00
#> [11] 9.245962e-01 8.799322e-16
#> 
#> Rotation (n x k) = (48 x 12):
#>                PC1         PC2          PC3          PC4           PC5
#>  [1,] -0.034197200  0.11890907  0.102184229  0.337051326 -0.0004301153
#>  [2,] -0.155248223  0.11008628  0.021300422  0.291138092 -0.0173125004
#>  [3,]  0.004603059  0.20559263 -0.003966471  0.274392673  0.0172462689
#>  [4,] -0.106659451  0.02605975  0.011764270 -0.184827051  0.1226537330
#>  [5,] -0.194298480 -0.06192070  0.123874384  0.073143661 -0.0055155674
#>  [6,] -0.113234991  0.19657020  0.160696491 -0.118967005 -0.0575552712
#>  [7,] -0.082192204  0.23622040 -0.080015472 -0.052899601 -0.0610220259
#>  [8,] -0.208282703 -0.00934285  0.162923919  0.202218062 -0.1483661863
#>  [9,] -0.078954728 -0.03391247  0.359203719 -0.127120646 -0.1395922858
#> [10,] -0.191930050 -0.16219529 -0.084691508 -0.024054324 -0.1849452986
#> [11,] -0.152505875 -0.04980929 -0.247684603  0.046522764  0.1468102443
#> [12,] -0.165927455 -0.14910574 -0.007460287 -0.009361819 -0.1037193678
#> [13,] -0.197238571 -0.14092741 -0.196981539 -0.102933513 -0.0282308465
#> [14,] -0.240584196 -0.09132669  0.010327042  0.023104605 -0.0502359710
#> [15,] -0.165219817 -0.15404537  0.005739252  0.014257446 -0.2389551124
#> [16,] -0.114634773  0.07792101 -0.197642527 -0.187447307  0.1749548997
#> [17,] -0.155689426 -0.17284156  0.057060626  0.079007789 -0.1737856510
#> [18,] -0.208218229 -0.14451935  0.057385627 -0.062227359  0.1011160269
#> [19,] -0.181580347  0.18955418  0.059724679 -0.112818571 -0.0060219817
#> [20,]  0.041862783 -0.14158279  0.144624202  0.104936410 -0.0315577460
#> [21,] -0.097965157 -0.01772842  0.149451614 -0.059293710  0.1649176565
#> [22,] -0.073386264 -0.13418787 -0.130515262 -0.341396032 -0.1559850687
#> [23,]  0.139899167 -0.04242638 -0.074978403 -0.153782907 -0.0820752523
#> [24,] -0.205692335 -0.06335076 -0.016733441 -0.012167606 -0.2939825011
#> [25,] -0.226468715 -0.04650661 -0.136630831  0.184443991  0.0124607890
#> [26,]  0.038602294  0.23913527 -0.075899112 -0.089254172 -0.0983536527
#> [27,]  0.146885100  0.17122790  0.169784936  0.006672227 -0.1419677821
#> [28,]  0.034325881 -0.16369356  0.293971329 -0.103848193  0.1878421051
#> [29,] -0.127892072  0.13388180 -0.294341738 -0.080524763 -0.1158748998
#> [30,] -0.201607244  0.11725295 -0.057250012 -0.019838145  0.2003669007
#> [31,] -0.184930241  0.16227623 -0.065425153  0.037318195 -0.0182216760
#> [32,] -0.145911482  0.19175409 -0.089582485 -0.179452972  0.1676860582
#> [33,] -0.133991554  0.06711262  0.028801844 -0.095990401  0.1727953547
#> [34,] -0.167035806  0.15627000 -0.139771958  0.022285556 -0.0711360357
#> [35,]  0.001412424 -0.15662225  0.097523974  0.014091955  0.3272087656
#> [36,]  0.007755805  0.21914810  0.135040644  0.056013215  0.1094407346
#> [37,]  0.005746089  0.28174909 -0.004299562  0.055997067  0.1628696250
#> [38,] -0.223061162  0.05040923  0.099001337  0.101459231 -0.0168676342
#> [39,] -0.073271189 -0.07548116 -0.053677826  0.335197220 -0.0131411323
#> [40,] -0.177140198 -0.12201183 -0.020534882 -0.013653015  0.2344136065
#> [41,]  0.060517256 -0.22462044  0.036407759 -0.064705029  0.2777855319
#> [42,] -0.046744945  0.22497912  0.150993106 -0.242426201 -0.0494681285
#> [43,] -0.085774244 -0.06156615 -0.011068747  0.227618069  0.2881524371
#> [44,] -0.132250623  0.21184401  0.113960219 -0.014649068  0.0299373411
#> [45,] -0.111848882  0.14219526  0.283997094 -0.052528580 -0.0196511677
#> [46,] -0.204599932 -0.01770538  0.037122868 -0.034849500  0.1921021566
#> [47,] -0.197098008 -0.06732129  0.116430020 -0.163910463  0.0432799854
#> [48,] -0.089243222 -0.04855577  0.356331250 -0.087638436 -0.0787965670
#>                PC6          PC7          PC8          PC9        PC10
#>  [1,]  0.142386718 -0.151877197 -0.051279946  0.277181770 -0.09224779
#>  [2,] -0.050631264 -0.130124854  0.009219081  0.287315825 -0.02803594
#>  [3,]  0.086325320  0.283575115  0.048178960  0.061738323 -0.05335362
#>  [4,]  0.389598515  0.235198510  0.013002330  0.010982048  0.06528658
#>  [5,]  0.185967618 -0.203174854  0.010609296  0.188823645  0.25639570
#>  [6,] -0.218883504  0.128127850 -0.053380945  0.122514367  0.03503720
#>  [7,] -0.084562652  0.136453287  0.043693514 -0.092112099  0.29378370
#>  [8,] -0.007366248 -0.006609190 -0.076418904 -0.015250171  0.12470211
#>  [9,] -0.084329667  0.042760997 -0.036409479 -0.066474085 -0.09309295
#> [10,] -0.069865576  0.012381579 -0.175000013  0.019946758 -0.02963299
#> [11,] -0.178923866 -0.203762032 -0.079013141  0.067812826 -0.16090448
#> [12,]  0.294171911  0.100728812 -0.150188313  0.114718591 -0.10187435
#> [13,]  0.028600084 -0.011933867  0.078817280  0.014726114  0.05360435
#> [14,] -0.024767574 -0.062164763 -0.175971843  0.139855274 -0.14353321
#> [15,]  0.071317213  0.015379520  0.131631052  0.106793612  0.32143421
#> [16,] -0.030386374  0.196042896 -0.154111097  0.126474451 -0.04112697
#> [17,] -0.185663594  0.185377054  0.031631898  0.033940649 -0.16609262
#> [18,]  0.020983856  0.111586597  0.084484212 -0.039164284 -0.17936940
#> [19,]  0.187315236 -0.015703738  0.082856657 -0.005095490 -0.03041171
#> [20,] -0.035017333  0.441690612 -0.072936668  0.167250470  0.19356319
#> [21,]  0.206072629  0.016169702 -0.364358502  0.271985574 -0.16438248
#> [22,]  0.010871152 -0.016459377  0.042638878  0.117250879 -0.15067276
#> [23,] -0.250972243  0.170494651  0.083475059  0.370451931 -0.15768065
#> [24,] -0.021286780  0.074501088 -0.053933549 -0.090407150  0.23927387
#> [25,]  0.029704703  0.097278451  0.042214455 -0.025655357 -0.01115750
#> [26,]  0.100219887  0.136287771 -0.259942480  0.006301798  0.11829694
#> [27,] -0.076310438  0.080300555 -0.184854167 -0.095281482 -0.03617765
#> [28,] -0.113021696 -0.040027724  0.023615337  0.012435093  0.08701607
#> [29,] -0.064635792 -0.086670605  0.032900688  0.090565446  0.03480849
#> [30,]  0.185625975 -0.130341774  0.059905140 -0.078532860  0.04447787
#> [31,] -0.166956950 -0.140499619  0.193371508  0.076975707  0.10489929
#> [32,] -0.029645000  0.039776434 -0.164287288 -0.011584965 -0.01741600
#> [33,] -0.184274622 -0.206171353 -0.336405609 -0.072288308  0.20670453
#> [34,] -0.190180391  0.150738874 -0.143230215 -0.061805280  0.11638330
#> [35,] -0.151910454  0.002663285  0.205645184  0.267333459  0.19536855
#> [36,] -0.082351092  0.260502660  0.138163988  0.253605649 -0.04659994
#> [37,] -0.044178724 -0.003435601 -0.078014263  0.171187829  0.11200939
#> [38,]  0.039224233 -0.046575478  0.296564925 -0.023272662 -0.07210496
#> [39,] -0.268712403  0.064528409 -0.134447485 -0.181425038 -0.19277506
#> [40,]  0.130695710  0.203005428  0.056911238 -0.120552120 -0.20572195
#> [41,] -0.112572782 -0.064714173 -0.074458390  0.089035015  0.24542963
#> [42,] -0.041649429 -0.002199646  0.156874250 -0.045043644 -0.05540021
#> [43,]  0.032334017  0.201862147 -0.045786575 -0.360586880  0.15750794
#> [44,]  0.029628552  0.021753289  0.241767380 -0.161688387 -0.19389642
#> [45,] -0.046156529 -0.232529249 -0.029618047 -0.053678888 -0.14919733
#> [46,] -0.289427852  0.065624366 -0.049916834 -0.103190649 -0.12751054
#> [47,] -0.145121248  0.070248141  0.243792189 -0.008071605  0.11796392
#> [48,] -0.005231829 -0.054677128 -0.207963042 -0.107409381  0.03158828
#>               PC11         PC12
#>  [1,]  0.190820049  0.277294021
#>  [2,] -0.023088305 -0.239169814
#>  [3,]  0.100415596  0.106339869
#>  [4,]  0.226634993 -0.056044241
#>  [5,]  0.081591915 -0.292161246
#>  [6,] -0.080522841 -0.025114442
#>  [7,] -0.316709028  0.178387505
#>  [8,] -0.031806383 -0.028953127
#>  [9,] -0.170024384 -0.150274353
#> [10,]  0.073022890  0.147601569
#> [11,] -0.016011425 -0.078903804
#> [12,] -0.016123920 -0.216754430
#> [13,]  0.076040036 -0.004967956
#> [14,] -0.012663650  0.067595895
#> [15,]  0.015148152  0.312869530
#> [16,] -0.375250816 -0.135856832
#> [17,]  0.043150670  0.035774473
#> [18,]  0.253158533  0.111162673
#> [19,] -0.029920269 -0.003045053
#> [20,] -0.141556297 -0.272137894
#> [21,] -0.259382191  0.177990662
#> [22,]  0.131521069  0.007159045
#> [23,]  0.173716601  0.021933296
#> [24,] -0.045116629  0.215160037
#> [25,] -0.163339397 -0.046313912
#> [26,]  0.251133360 -0.229879456
#> [27,]  0.276457318 -0.016973264
#> [28,] -0.169064055  0.096137513
#> [29,] -0.107170172 -0.057620986
#> [30,]  0.056282836  0.006846131
#> [31,]  0.103852038 -0.096236502
#> [32,]  0.032289926  0.150533373
#> [33,]  0.242945214 -0.080348454
#> [34,]  0.162942071 -0.026125267
#> [35,]  0.074067928  0.087590202
#> [36,]  0.127100271  0.045845116
#> [37,] -0.036203394  0.169995266
#> [38,] -0.083897059  0.044538941
#> [39,]  0.033449164 -0.158597997
#> [40,]  0.098998319  0.045699938
#> [41,]  0.050432042 -0.117896711
#> [42,]  0.061054984  0.054924135
#> [43,]  0.058029936  0.024614798
#> [44,] -0.108455685 -0.180138546
#> [45,] -0.063132341 -0.114009479
#> [46,]  0.007849834  0.197019115
#> [47,]  0.154027098 -0.234045862
#> [48,]  0.069562806  0.129115627
immunr_mds(dist(gu))
#> Standard deviations (1, .., p=4):
#> [1] 0 0 0 0
#> 
#> Rotation (n x k) = (12 x 2):
#>               [,1]       [,2]
#> A2-i129  233.88293 -107.06672
#> A2-i131 -145.53408   50.75250
#> A2-i133 -343.68122 -292.27878
#> A2-i132 -323.82099  -80.54790
#> A4-i191 -203.42611  211.63501
#> A4-i192 -278.19595  113.60611
#> MS1      -70.02373   16.25948
#> MS2      383.69473 -402.72176
#> MS3      -73.92883  134.97146
#> MS4      344.35982 -113.71333
#> MS5      -67.39896  114.26628
#> MS6      544.07239  354.83764
immunr_tsne(dist(gu))
#>              DimI     DimII
#>  [1,]   15.314316 148.72060
#>  [2,]  -97.179830 -89.20496
#>  [3,]  279.691995 -58.27676
#>  [4,]  279.856906 -54.24547
#>  [5,] -115.188842 -87.42693
#>  [6,]  -91.844943 -79.58337
#>  [7,] -109.233750 -85.57459
#>  [8,]   18.484367 158.42552
#>  [9,] -101.340078 -78.24946
#> [10,]   14.010775 152.82703
#> [11,]  -99.697514 -82.23041
#> [12,]    7.126598 154.81880
#> attr(,"class")
#> [1] "immunr_tsne" "matrix"      "array"