![[Deprecated]](figures/lifecycle-deprecated.svg)
Collects 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_pcaperforms PCA (Principal Component Analysis) using prcomp;immunr_mdsperforms MDS (Multi-Dimensional Scaling) using isoMODS from MASS package.immunr_tsneperforms tSNE (t-Distributed Stochastic Neighbour Embedding) using Rtsne Rtsne package.
Usage
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 returns the non-processed output from dimensionality reduction algorithms. Pass FALSE if you want to visualise results.
- .orig
If TRUE then returns the original result from algorithms. Pass FALSE if you want to visualise results.
- .dist
If TRUE then assumes that ".data" is a distance matrix.
- .perp
The perplexity parameter for Rtsne. Specifies the number of neighbors 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 7.817807e-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 -0.044957586
#> [2,] -0.023088305 -0.029580095
#> [3,] 0.100415596 0.252074126
#> [4,] 0.226634993 -0.074449896
#> [5,] 0.081591915 -0.201485422
#> [6,] -0.080522841 -0.088199698
#> [7,] -0.316709028 0.160588139
#> [8,] -0.031806383 0.020976664
#> [9,] -0.170024384 -0.155696284
#> [10,] 0.073022890 0.115364393
#> [11,] -0.016011425 -0.063392127
#> [12,] -0.016123920 -0.255760605
#> [13,] 0.076040036 0.081694525
#> [14,] -0.012663650 0.060478661
#> [15,] 0.015148152 0.271241839
#> [16,] -0.375250816 -0.162457491
#> [17,] 0.043150670 0.041668294
#> [18,] 0.253158533 0.144576962
#> [19,] -0.029920269 -0.007076857
#> [20,] -0.141556297 -0.346115578
#> [21,] -0.259382191 0.250464460
#> [22,] 0.131521069 -0.012392413
#> [23,] 0.173716601 0.021814120
#> [24,] -0.045116629 0.211248803
#> [25,] -0.163339397 -0.166474809
#> [26,] 0.251133360 -0.221967215
#> [27,] 0.276457318 -0.023759381
#> [28,] -0.169064055 0.071159249
#> [29,] -0.107170172 -0.081174575
#> [30,] 0.056282836 0.007047645
#> [31,] 0.103852038 -0.152912654
#> [32,] 0.032289926 0.118715326
#> [33,] 0.242945214 -0.106951600
#> [34,] 0.162942071 0.015452196
#> [35,] 0.074067928 0.032887168
#> [36,] 0.127100271 0.090111112
#> [37,] -0.036203394 0.199654636
#> [38,] -0.083897059 0.076848361
#> [39,] 0.033449164 -0.126126370
#> [40,] 0.098998319 0.055750080
#> [41,] 0.050432042 -0.040561897
#> [42,] 0.061054984 0.051553921
#> [43,] 0.058029936 -0.030622738
#> [44,] -0.108455685 -0.171886787
#> [45,] -0.063132341 -0.158321234
#> [46,] 0.007849834 0.142884532
#> [47,] 0.154027098 -0.234394073
#> [48,] 0.069562806 0.195109064
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,] -264.57604 -30.7022684
#> [2,] 187.30981 -0.2520207
#> [3,] -44.67438 75.3502817
#> [4,] -44.75005 70.1878435
#> [5,] 186.69466 -23.3222672
#> [6,] 200.22852 5.0796523
#> [7,] 189.97250 -16.0704501
#> [8,] -276.22390 -25.0082524
#> [9,] 200.46378 -7.1313852
#> [10,] -267.18738 -25.8607284
#> [11,] 195.67942 -4.4293029
#> [12,] -262.93696 -17.8411023
#> attr(,"class")
#> [1] "immunr_tsne" "matrix" "array"