`cytoftree`: extension of `cytometree` to analyze mass cytometry data

Introduction to `cytoftree`

cytoftree is an extension to cytometree function to analyze mass cytometry data. These data are specific due to a high number of zero and the high number of markers (up to 100 potentially). cytoftree is based on cytometree’s algorithm which is the construction of binary tree, whose nodes represents cell sub-populations, and slighly modified to take into account the specification of mass cytometry data.

Data transformation

According to the literature, mass cytometry data must be transform to get best partitions. We propose different transformations: asinh (as default), biexp, log10 or none (without transformation).

Binary tree construction

At each node, for each marker, the cells with zero values are temporarily set aside from the other cells.
The remaining observed cells (or “events”) and markers are modeled by both a normal distribution (so unimodal), and a mixture of 2 normal distributions (so bimodal).
If the AIC normalized differences \(D\) are significant, the cells are split into 2 groups according to the bimodal distribution. Cells with low values are annotated - (no marker) while cells with high values are annotated + (with marker). The cells with zero values are also annotated - (no marker).
The binary tree is constructed until the cells can no longer be split into 2 groups.

Post-hoc annotation

Given the unsupervised nature of the binary tree, some of the available markers may not be used to find the different cell populations present in a given sample. To recover a complete annotation, we defined, as a post processing procedure, an annotation method which allows the user to distinguish two (or three) expression levels per marker.

Influenza vaccine response dataset analysis with `cytoftree`

In this example, we will use an influenza vaccine response dataset (from ImmuneSpace), with 39 markers. To speed-up the computation, we sampled 10 000 cells from this dataset.

Data preparation

First, we can look the structure and the markers of the data.

library(cytometree)
data(IMdata)
dim(IMdata)

## [1] 10000    39

colnames(IMdata)

##  [1] "Time"             "Cell_length"      "(In113)Dd_CD57"   "(In115)Dd_Dead"  
##  [5] "(Ce140)Dd_Bead"   "(Nd142)Dd_CD19"   "(Nd143)Dd_CD4"    "(Nd144)Dd_CD8"   
##  [9] "(Nd146)Dd_IgD"    "(Sm147)Dd_CD85j"  "(Nd148)Dd_CD11c"  "(Sm149)Dd_CD16"  
## [13] "(Nd150)Dd_CD3"    "(Eu151)Dd_CD38"   "(Sm152)Dd_CD27"   "(Eu153)Dd_CD11b" 
## [17] "(Sm154)Dd_CD14"   "(Gd155)Dd_CCR6"   "(Gd156)Dd_CD94"   "(Gd157)Dd_CD86"  
## [21] "(Gd158)Dd_CXCR5"  "(Tb159)Dd_CXCR3"  "(Gd160)Dd_CCR7"   "(Dy162)Dd_CD45RA"
## [25] "(Dy164)Dd_CD20"   "(Ho165)Dd_CD127"  "(Er166)Dd_CD33"   "(Er167)Dd_CD28"  
## [29] "(Er168)Dd_CD24"   "(Tm169)Dd_ICOS"   "(Er170)Dd_CD161"  "(Yb171)Dd_TCRgd" 
## [33] "(Yb172)Dd_PD-1"   "(Yb173)Dd_CD123"  "(Yb174)Dd_CD56"   "(Lu175)Dd_HLADR" 
## [37] "(Yb176)Dd_CD25"   "(Ir191)Dd_DNA1"   "(Ir193)Dd_DNA2"

Then, we also check the proportion of zero for each marker, particularity of mass cytometry data.

zero_proportion <- apply(IMdata[, -c(1, 2)], MARGIN = 2, FUN = function(x) {
    round(prop.table(table(x == 0))["TRUE"] * 100, 2)
})
zero_proportion

##   (In113)Dd_CD57   (In115)Dd_Dead   (Ce140)Dd_Bead   (Nd142)Dd_CD19 
##            76.26            60.55            91.00            70.34 
##    (Nd143)Dd_CD4    (Nd144)Dd_CD8    (Nd146)Dd_IgD  (Sm147)Dd_CD85j 
##            38.92            45.04            70.61            55.14 
##  (Nd148)Dd_CD11c   (Sm149)Dd_CD16    (Nd150)Dd_CD3   (Eu151)Dd_CD38 
##            54.75            63.78            18.75            16.30 
##   (Sm152)Dd_CD27  (Eu153)Dd_CD11b   (Sm154)Dd_CD14   (Gd155)Dd_CCR6 
##            23.19            33.66            56.47            68.81 
##   (Gd156)Dd_CD94   (Gd157)Dd_CD86  (Gd158)Dd_CXCR5  (Tb159)Dd_CXCR3 
##            63.89            57.71            56.91            52.07 
##   (Gd160)Dd_CCR7 (Dy162)Dd_CD45RA   (Dy164)Dd_CD20  (Ho165)Dd_CD127 
##            29.54             8.67            49.11            34.57 
##   (Er166)Dd_CD33   (Er167)Dd_CD28   (Er168)Dd_CD24   (Tm169)Dd_ICOS 
##            39.43            31.08            64.00            74.00 
##  (Er170)Dd_CD161  (Yb171)Dd_TCRgd   (Yb172)Dd_PD-1  (Yb173)Dd_CD123 
##            76.15            90.25            81.13            75.48 
##   (Yb174)Dd_CD56  (Lu175)Dd_HLADR   (Yb176)Dd_CD25   (Ir191)Dd_DNA1 
##            66.72            45.16            56.40             1.42 
##   (Ir193)Dd_DNA2 
##             2.63

CytofTree function

According to the available markers, a gating strategy may be considered. In this example, we have a gating strategy to conserve only viable cells by splitting on the following markers : DNA1, DNA2, Cell_length, Bead and Dead. This way, we can be as close as possible to manual gating. To do this, we have to force the markers with the force_first_marker option (semi-supervised gating).

Then, to improve the performance of automating gating, we decided to transform data with asinh transformation (default transformation). Then, we have to choose which markers should be transformed using num_col argument. The columns Time et Cell_length are not mass cytometry measure and shouldn’t be transformed.

num_col <- c(3:ncol(IMdata))

tree <- CytofTree(M = IMdata, minleaf = 1, t = 0.1, verbose = FALSE, force_first_markers = c("(Ir191)Dd_DNA1",
    "(Ir193)Dd_DNA2", "Cell_length", "(Ce140)Dd_Bead", "(In115)Dd_Dead"), transformation = "asinh",
    num_col = num_col)

## Unable to force split on (In115)Dd_Dead for some node at level5
## Unable to force split on (In115)Dd_Dead for some node at level5

## It works !

max(tree$labels)

## [1] 824

High dimensional issues

Due to the high number of markers, cytoftree provides high number of sub-populations. minleaf value for the minimum of cells by sub-population and t threshold for the depth of the binary tree can be modified to get more or less sub-populations. The plot_graph function provides a look on the binary tree, but should be unreadable due to the high number of sub-populations.

Annotation function

The annotation function allows to recover the incomplete annotation on sub-populations. combinations option provides the complete annotation on each sub-population.

annot <- Annotation(tree, plot = FALSE, K2markers = colnames(IMdata))
annot$combinations[1:5, ]

##     Time Cell_length (In113)Dd_CD57 (In115)Dd_Dead (Ce140)Dd_Bead
## 787    1           0              0              0              0
## 593    1           0              0              0              0
## 622    1           0              0              0              0
## 818    1           0              0              0              0
## 636    1           0              0              0              0
##     (Nd142)Dd_CD19 (Nd143)Dd_CD4 (Nd144)Dd_CD8 (Nd146)Dd_IgD (Sm147)Dd_CD85j
## 787              0             1             0             0               0
## 593              0             0             1             0               0
## 622              1             0             0             1               1
## 818              0             1             0             0               0
## 636              0             0             0             0               0
##     (Nd148)Dd_CD11c (Sm149)Dd_CD16 (Nd150)Dd_CD3 (Eu151)Dd_CD38 (Sm152)Dd_CD27
## 787               0              0             1              0              1
## 593               0              0             1              0              1
## 622               0              0             0              0              0
## 818               0              0             1              0              1
## 636               0              0             0              0              0
##     (Eu153)Dd_CD11b (Sm154)Dd_CD14 (Gd155)Dd_CCR6 (Gd156)Dd_CD94 (Gd157)Dd_CD86
## 787               0              0              0              0              0
## 593               0              0              0              0              0
## 622               0              0              1              0              0
## 818               0              0              0              0              0
## 636               0              0              0              0              0
##     (Gd158)Dd_CXCR5 (Tb159)Dd_CXCR3 (Gd160)Dd_CCR7 (Dy162)Dd_CD45RA
## 787               0               0              1                1
## 593               0               0              1                1
## 622               1               0              0                1
## 818               0               0              1                0
## 636               0               0              0                0
##     (Dy164)Dd_CD20 (Ho165)Dd_CD127 (Er166)Dd_CD33 (Er167)Dd_CD28 (Er168)Dd_CD24
## 787              0               0              0              1              0
## 593              0               0              0              1              0
## 622              1               0              0              0              0
## 818              0               0              0              1              0
## 636              0               0              0              0              0
##     (Tm169)Dd_ICOS (Er170)Dd_CD161 (Yb171)Dd_TCRgd (Yb172)Dd_PD-1
## 787              0               0               0              0
## 593              0               0               0              0
## 622              0               0               0              0
## 818              0               0               0              0
## 636              0               0               0              0
##     (Yb173)Dd_CD123 (Yb174)Dd_CD56 (Lu175)Dd_HLADR (Yb176)Dd_CD25
## 787               0              0               0              0
## 593               0              0               0              0
## 622               0              0               1              0
## 818               0              0               0              0
## 636               0              0               0              0
##     (Ir191)Dd_DNA1 (Ir193)Dd_DNA2 leaves count   prop
## 787              1              1    787   357 0.0357
## 593              1              1    593   182 0.0182
## 622              1              1    622   119 0.0119
## 818              1              1    818    96 0.0096
## 636              0              0    636    93 0.0093

Due to the high number of sub-populations, it’s recommended to use RetrievePops function which provide informations for particular sub-populations.

`RetrievePops` : providing informations for particular sub-populations

RetrievePops provides several informations on specific sub-populations, in particular the proportions and the sub-populations merged.

phenotypes <- list()
phenotypes[["CD4+"]] <- rbind(c("(Ir191)Dd_DNA1", 1), c("(Ir193)Dd_DNA2", 1), c("Cell_length",
    0), c("(Ce140)Dd_Bead", 0), c("(In115)Dd_Dead", 0), c("(Sm154)Dd_CD14", 0), c("(Er166)Dd_CD33",
    0), c("(Nd150)Dd_CD3", 1), c("(Nd143)Dd_CD4", 1))

phenotypes[["CD8+"]] <- rbind(c("(Ir191)Dd_DNA1", 1), c("(Ir193)Dd_DNA2", 1), c("Cell_length",
    0), c("(Ce140)Dd_Bead", 0), c("(In115)Dd_Dead", 0), c("(Sm154)Dd_CD14", 0), c("(Er166)Dd_CD33",
    0), c("(Nd150)Dd_CD3", 1), c("(Nd144)Dd_CD8", 1))

pheno_result <- RetrievePops(annot, phenotypes = phenotypes)

# CD4+
pheno_result$phenotypesinfo[[1]]

## $phenotype
## [1] "(Ir191)Dd_DNA1-1" "(Ir193)Dd_DNA2-1" "Cell_length-0"    "(Ce140)Dd_Bead-0"
## [5] "(In115)Dd_Dead-0" "(Sm154)Dd_CD14-0" "(Er166)Dd_CD33-0" "(Nd150)Dd_CD3-1" 
## [9] "(Nd143)Dd_CD4-1" 
## 
## $proportion
## [1] 0.1953
## 
## $cells
##    [1]    2    9   25   30   34   35   40   43   47   48   52   53   59   69
##   [15]   84   85   87   88   90   98  115  118  123  125  130  134  136  144
##   [29]  161  170  183  187  190  193  196  206  207  208  213  223  228  236
##   [43]  241  242  253  262  269  270  280  283  286  294  304  307  313  315
##   [57]  318  319  320  325  348  353  356  377  379  385  386  389  397  401
##   [71]  406  413  414  422  424  425  427  436  439  466  476  483  490  496
##   [85]  503  504  525  538  547  548  549  551  556  559  568  569  578  580
##   [99]  582  597  601  603  612  614  615  617  619  628  631  637  638  640
##  [113]  641  649  658  660  661  662  666  671  682  695  700  702  703  705
##  [127]  709  711  712  714  722  724  726  730  739  741  749  752  761  764
##  [141]  793  798  799  800  801  803  812  824  835  840  849  851  855  860
##  [155]  862  866  878  883  886  889  895  908  910  921  931  932  935  936
##  [169]  937  949  954  955  956  959  961  979  980  988  999 1001 1014 1026
##  [183] 1039 1045 1046 1052 1060 1066 1075 1081 1086 1097 1099 1103 1108 1112
##  [197] 1126 1128 1132 1133 1136 1145 1146 1157 1168 1171 1174 1185 1190 1193
##  [211] 1196 1197 1207 1208 1212 1215 1216 1219 1221 1224 1231 1235 1236 1238
##  [225] 1239 1248 1254 1262 1267 1274 1275 1276 1277 1285 1288 1289 1290 1303
##  [239] 1310 1311 1317 1320 1322 1331 1333 1343 1349 1351 1354 1355 1371 1372
##  [253] 1376 1380 1391 1405 1406 1407 1408 1409 1411 1412 1416 1426 1429 1437
##  [267] 1440 1444 1445 1449 1450 1456 1457 1459 1468 1475 1476 1479 1484 1487
##  [281] 1491 1496 1497 1498 1500 1503 1510 1514 1515 1529 1530 1531 1536 1537
##  [295] 1557 1559 1563 1567 1568 1573 1574 1575 1580 1582 1585 1591 1596 1601
##  [309] 1602 1632 1633 1637 1640 1641 1663 1667 1670 1679 1681 1682 1685 1693
##  [323] 1709 1710 1731 1734 1737 1738 1753 1764 1766 1768 1773 1775 1778 1779
##  [337] 1780 1781 1784 1787 1789 1791 1795 1800 1801 1803 1806 1811 1825 1834
##  [351] 1835 1839 1842 1846 1847 1848 1857 1864 1873 1880 1905 1906 1908 1919
##  [365] 1920 1923 1933 1934 1940 1954 1956 1965 1972 1991 1992 1998 2004 2008
##  [379] 2013 2016 2020 2023 2024 2030 2032 2033 2034 2043 2044 2050 2062 2074
##  [393] 2080 2084 2096 2097 2103 2109 2112 2124 2132 2133 2135 2142 2154 2155
##  [407] 2163 2170 2171 2175 2189 2192 2196 2203 2207 2209 2212 2217 2218 2235
##  [421] 2245 2261 2269 2271 2285 2287 2291 2297 2298 2301 2302 2309 2311 2319
##  [435] 2320 2322 2327 2330 2345 2346 2349 2361 2365 2393 2400 2402 2413 2416
##  [449] 2418 2423 2428 2429 2433 2434 2440 2445 2449 2456 2462 2465 2469 2477
##  [463] 2490 2492 2495 2510 2511 2513 2518 2521 2524 2529 2530 2543 2549 2551
##  [477] 2553 2554 2559 2562 2563 2568 2571 2572 2583 2585 2593 2611 2615 2626
##  [491] 2629 2633 2641 2644 2646 2652 2656 2661 2668 2670 2675 2679 2681 2682
##  [505] 2683 2690 2698 2701 2703 2704 2706 2707 2709 2710 2712 2716 2717 2729
##  [519] 2733 2737 2738 2748 2759 2773 2784 2786 2793 2797 2812 2829 2834 2837
##  [533] 2839 2841 2848 2856 2857 2863 2868 2874 2879 2883 2890 2909 2912 2915
##  [547] 2924 2926 2927 2932 2936 2938 2942 2950 2959 2963 2969 2970 2980 2982
##  [561] 2984 2987 2993 2994 2997 2999 3000 3005 3011 3012 3013 3016 3019 3022
##  [575] 3024 3029 3031 3041 3042 3044 3047 3048 3051 3056 3061 3068 3072 3073
##  [589] 3074 3075 3076 3079 3082 3090 3093 3106 3115 3117 3129 3139 3140 3142
##  [603] 3144 3150 3152 3162 3165 3168 3169 3173 3176 3177 3181 3185 3189 3191
##  [617] 3193 3216 3217 3223 3231 3241 3242 3246 3251 3252 3253 3260 3262 3263
##  [631] 3269 3272 3274 3286 3288 3299 3302 3313 3315 3319 3320 3323 3334 3336
##  [645] 3337 3350 3351 3358 3361 3363 3364 3368 3390 3393 3394 3396 3401 3424
##  [659] 3430 3441 3448 3452 3455 3461 3462 3474 3479 3484 3486 3487 3494 3497
##  [673] 3510 3517 3522 3523 3524 3530 3533 3536 3537 3540 3550 3559 3560 3568
##  [687] 3580 3584 3587 3599 3603 3604 3611 3612 3614 3616 3618 3620 3624 3627
##  [701] 3632 3638 3641 3645 3647 3649 3655 3671 3674 3676 3677 3682 3692 3698
##  [715] 3699 3718 3720 3723 3737 3742 3744 3746 3759 3762 3764 3769 3773 3796
##  [729] 3799 3804 3806 3807 3810 3813 3815 3818 3821 3825 3837 3839 3858 3864
##  [743] 3868 3869 3871 3879 3904 3905 3908 3913 3921 3922 3923 3934 3937 3949
##  [757] 3950 3951 3954 3955 3959 3962 3967 3980 3990 3997 4002 4004 4011 4020
##  [771] 4024 4025 4026 4029 4030 4032 4035 4039 4044 4045 4046 4047 4048 4049
##  [785] 4057 4058 4059 4060 4061 4068 4069 4072 4073 4092 4093 4095 4096 4100
##  [799] 4105 4115 4122 4133 4138 4142 4144 4161 4171 4178 4180 4181 4182 4186
##  [813] 4193 4195 4199 4206 4219 4225 4233 4236 4237 4249 4264 4275 4280 4281
##  [827] 4295 4298 4300 4303 4304 4309 4312 4313 4317 4318 4334 4338 4339 4341
##  [841] 4345 4347 4348 4349 4356 4360 4362 4367 4378 4388 4389 4395 4396 4402
##  [855] 4416 4420 4428 4430 4433 4438 4441 4445 4452 4456 4457 4458 4462 4464
##  [869] 4467 4472 4473 4474 4476 4483 4498 4501 4503 4506 4509 4510 4514 4516
##  [883] 4520 4524 4535 4537 4542 4543 4544 4546 4551 4557 4559 4566 4569 4572
##  [897] 4575 4582 4583 4587 4592 4599 4605 4606 4609 4614 4618 4632 4635 4636
##  [911] 4638 4644 4654 4658 4671 4673 4678 4683 4686 4687 4713 4715 4717 4718
##  [925] 4719 4724 4725 4728 4730 4734 4738 4744 4753 4759 4765 4768 4769 4777
##  [939] 4783 4786 4801 4804 4809 4812 4813 4823 4824 4843 4851 4852 4862 4867
##  [953] 4873 4874 4876 4883 4898 4899 4900 4919 4929 4936 4945 4956 4964 4966
##  [967] 4969 4970 4973 4976 4980 4981 4983 4984 4990 5003 5005 5012 5016 5020
##  [981] 5024 5025 5027 5028 5048 5058 5060 5063 5068 5070 5080 5082 5084 5087
##  [995] 5088 5094 5096 5097 5104 5121 5124 5126 5131 5136 5142 5146 5149 5161
## [1009] 5164 5171 5174 5176 5184 5191 5196 5201 5203 5205 5209 5211 5227 5228
## [1023] 5230 5231 5232 5233 5238 5240 5242 5243 5255 5256 5260 5262 5264 5265
## [1037] 5271 5285 5292 5296 5300 5304 5314 5315 5316 5322 5326 5334 5337 5345
## [1051] 5346 5348 5353 5354 5361 5362 5368 5369 5373 5379 5381 5382 5387 5393
## [1065] 5398 5406 5411 5412 5416 5421 5425 5426 5428 5429 5437 5442 5444 5446
## [1079] 5453 5457 5461 5463 5464 5468 5471 5475 5487 5494 5500 5502 5503 5511
## [1093] 5516 5518 5523 5530 5531 5533 5539 5540 5545 5548 5549 5553 5556 5558
## [1107] 5560 5566 5572 5573 5584 5586 5590 5595 5600 5605 5607 5609 5614 5617
## [1121] 5618 5619 5620 5625 5634 5636 5651 5652 5655 5657 5660 5677 5685 5690
## [1135] 5694 5699 5700 5708 5709 5714 5716 5718 5726 5734 5735 5736 5738 5742
## [1149] 5744 5753 5754 5756 5758 5767 5776 5787 5788 5791 5792 5797 5801 5811
## [1163] 5815 5825 5830 5835 5836 5839 5845 5852 5853 5862 5863 5864 5874 5876
## [1177] 5878 5881 5888 5902 5907 5909 5912 5917 5925 5926 5929 5933 5935 5939
## [1191] 5945 5948 5952 5954 5961 5964 5989 6002 6009 6015 6016 6017 6019 6021
## [1205] 6028 6040 6041 6045 6065 6074 6085 6086 6090 6093 6098 6099 6101 6113
## [1219] 6116 6130 6132 6135 6137 6142 6158 6159 6165 6168 6170 6173 6174 6175
## [1233] 6181 6182 6185 6187 6189 6192 6193 6194 6208 6210 6222 6224 6242 6243
## [1247] 6248 6253 6265 6269 6275 6276 6287 6292 6293 6295 6297 6299 6305 6307
## [1261] 6309 6311 6314 6323 6332 6336 6345 6354 6367 6368 6372 6377 6378 6387
## [1275] 6388 6389 6394 6400 6402 6405 6410 6419 6424 6428 6429 6453 6463 6472
## [1289] 6487 6490 6506 6525 6528 6531 6534 6537 6538 6540 6541 6547 6551 6570
## [1303] 6574 6575 6578 6579 6599 6605 6608 6619 6633 6636 6640 6641 6650 6659
## [1317] 6663 6675 6680 6681 6699 6705 6708 6717 6718 6720 6742 6751 6753 6761
## [1331] 6781 6784 6790 6791 6796 6799 6802 6804 6809 6831 6834 6845 6847 6850
## [1345] 6856 6857 6863 6866 6875 6880 6881 6883 6884 6885 6886 6890 6897 6899
## [1359] 6900 6903 6906 6911 6920 6922 6923 6932 6934 6946 6958 6959 6966 6968
## [1373] 6974 6976 6978 6981 7003 7005 7008 7011 7012 7020 7024 7026 7027 7030
## [1387] 7035 7041 7044 7053 7070 7073 7088 7091 7093 7112 7116 7120 7121 7125
## [1401] 7129 7132 7136 7137 7142 7149 7157 7160 7172 7179 7182 7190 7193 7198
## [1415] 7205 7210 7212 7216 7224 7225 7226 7231 7232 7252 7256 7257 7261 7266
## [1429] 7271 7276 7285 7286 7291 7293 7294 7299 7307 7308 7311 7324 7326 7328
## [1443] 7330 7336 7351 7352 7356 7357 7360 7362 7363 7368 7374 7384 7387 7394
## [1457] 7418 7426 7443 7451 7467 7470 7472 7476 7479 7485 7495 7497 7499 7510
## [1471] 7521 7526 7527 7528 7539 7540 7542 7544 7546 7547 7554 7575 7578 7587
## [1485] 7599 7604 7605 7606 7615 7616 7620 7629 7631 7632 7633 7640 7647 7649
## [1499] 7654 7655 7661 7664 7665 7667 7668 7669 7676 7678 7682 7683 7696 7702
## [1513] 7703 7709 7713 7727 7735 7737 7741 7744 7746 7747 7758 7761 7768 7783
## [1527] 7786 7789 7797 7798 7806 7811 7819 7831 7832 7833 7834 7842 7845 7851
## [1541] 7852 7853 7854 7857 7859 7863 7865 7871 7876 7880 7881 7887 7895 7902
## [1555] 7903 7907 7911 7915 7916 7917 7920 7930 7934 7975 7981 7992 7995 8005
## [1569] 8017 8021 8027 8032 8038 8039 8049 8058 8061 8071 8077 8098 8100 8106
## [1583] 8111 8118 8120 8121 8132 8133 8137 8138 8151 8152 8154 8156 8159 8162
## [1597] 8169 8172 8182 8183 8184 8186 8194 8202 8206 8207 8208 8213 8215 8236
## [1611] 8239 8242 8247 8254 8268 8272 8273 8276 8277 8284 8293 8295 8304 8305
## [1625] 8313 8319 8348 8349 8352 8353 8359 8361 8365 8369 8370 8376 8381 8403
## [1639] 8406 8415 8421 8425 8426 8434 8445 8458 8467 8468 8481 8486 8495 8497
## [1653] 8499 8514 8517 8518 8519 8530 8532 8534 8536 8541 8542 8545 8550 8554
## [1667] 8565 8567 8573 8580 8585 8593 8594 8604 8622 8624 8629 8632 8635 8638
## [1681] 8639 8641 8644 8650 8653 8656 8660 8661 8664 8671 8682 8688 8692 8697
## [1695] 8703 8705 8706 8728 8745 8747 8749 8754 8765 8784 8788 8790 8796 8799
## [1709] 8800 8802 8808 8809 8816 8817 8819 8823 8827 8834 8838 8843 8845 8855
## [1723] 8857 8874 8881 8885 8898 8903 8906 8909 8917 8921 8924 8938 8939 8940
## [1737] 8942 8947 8949 8950 8954 8958 8959 8960 8975 8982 8993 8994 8997 9011
## [1751] 9019 9024 9031 9043 9057 9058 9060 9063 9064 9067 9073 9074 9075 9076
## [1765] 9082 9083 9087 9088 9098 9108 9116 9120 9123 9127 9131 9132 9138 9139
## [1779] 9145 9163 9166 9169 9171 9172 9181 9195 9196 9198 9199 9206 9211 9215
## [1793] 9226 9227 9228 9233 9236 9237 9252 9256 9258 9265 9270 9273 9294 9305
## [1807] 9306 9314 9318 9321 9322 9323 9328 9331 9335 9337 9351 9355 9360 9370
## [1821] 9378 9380 9388 9389 9390 9394 9399 9405 9406 9416 9419 9420 9421 9424
## [1835] 9426 9450 9459 9462 9472 9473 9479 9480 9481 9485 9488 9490 9507 9508
## [1849] 9518 9541 9542 9550 9558 9560 9561 9566 9572 9575 9582 9583 9588 9591
## [1863] 9598 9605 9610 9612 9626 9632 9635 9636 9638 9640 9643 9650 9651 9656
## [1877] 9660 9661 9663 9670 9673 9675 9676 9679 9687 9692 9693 9694 9696 9706
## [1891] 9709 9713 9718 9732 9733 9734 9742 9747 9749 9756 9757 9759 9765 9766
## [1905] 9771 9776 9793 9802 9806 9810 9812 9813 9843 9853 9855 9858 9859 9868
## [1919] 9869 9872 9873 9878 9885 9889 9892 9893 9897 9899 9900 9901 9903 9904
## [1933] 9908 9913 9930 9935 9937 9938 9940 9945 9950 9959 9961 9970 9973 9977
## [1947] 9980 9981 9988 9989 9990 9992 9994
## 
## $Mergedlabels
##   [1] 787 818 786 817 739 773 385 697 471 714 814 824 801 469 806 802 618 640
##  [19] 643 789 808 774 822 562 670 220 821 326 804 820 472 556 642 712  66 528
##  [37] 557 615 715 770 171 470 713 811 819 215 617 619 646 741 247 355 382 501
##  [55] 668 696 816 176 216 351 558 559 666 669 671 698 699 717 718 772 807 327
##  [73] 353 586 647 665 672 767 812 815 174 181 183 352 356 387 500 560 561 620
##  [91] 644 645 648 716 771 805 113 170 172 175 182 219 221 246 284 386 466 529
## [109] 555 667 788
## 
## $Newlabel
## [1] 825

# CD8+
pheno_result$phenotypesinfo[[2]]

## $phenotype
## [1] "(Ir191)Dd_DNA1-1" "(Ir193)Dd_DNA2-1" "Cell_length-0"    "(Ce140)Dd_Bead-0"
## [5] "(In115)Dd_Dead-0" "(Sm154)Dd_CD14-0" "(Er166)Dd_CD33-0" "(Nd150)Dd_CD3-1" 
## [9] "(Nd144)Dd_CD8-1" 
## 
## $proportion
## [1] 0.0752
## 
## $cells
##   [1]    2    7    8   19   32   60   74   83   96  113  117  122  133  137  143
##  [16]  158  166  175  224  245  247  253  255  258  274  278  285  314  323  335
##  [31]  347  372  375  395  405  411  419  420  425  432  437  445  452  453  456
##  [46]  465  486  503  510  513  516  530  541  553  558  597  608  609  621  635
##  [61]  657  677  681  691  727  737  738  745  777  795  842  855  856  857  861
##  [76]  877  909  914  922  924  996 1008 1009 1012 1029 1031 1032 1042 1065 1078
##  [91] 1079 1096 1100 1110 1127 1130 1141 1163 1228 1230 1237 1242 1287 1297 1302
## [106] 1358 1359 1374 1428 1472 1505 1522 1525 1527 1540 1541 1551 1556 1579 1595
## [121] 1605 1607 1634 1646 1671 1672 1677 1690 1705 1711 1745 1750 1755 1818 1829
## [136] 1842 1853 1857 1862 1877 1891 1896 1909 1916 1918 1946 1947 1957 2041 2058
## [151] 2068 2073 2095 2101 2110 2131 2144 2147 2152 2167 2172 2173 2176 2213 2243
## [166] 2249 2251 2253 2304 2329 2341 2346 2366 2374 2389 2392 2395 2399 2403 2410
## [181] 2417 2426 2437 2444 2481 2482 2487 2514 2516 2523 2525 2527 2528 2557 2563
## [196] 2569 2603 2604 2613 2624 2627 2636 2638 2647 2680 2689 2718 2724 2727 2748
## [211] 2756 2777 2789 2823 2833 2849 2853 2859 2860 2862 2872 2881 2908 2917 2928
## [226] 2934 2939 2944 2951 2954 2965 2976 2992 2996 3001 3020 3024 3038 3094 3120
## [241] 3121 3124 3131 3145 3160 3165 3172 3201 3214 3220 3267 3275 3293 3345 3355
## [256] 3389 3404 3405 3447 3450 3453 3458 3468 3469 3486 3496 3498 3502 3534 3571
## [271] 3578 3579 3591 3597 3601 3604 3622 3628 3653 3654 3672 3686 3714 3724 3736
## [286] 3747 3761 3769 3778 3787 3800 3801 3803 3817 3820 3825 3829 3831 3847 3889
## [301] 3897 3900 3919 3925 3941 3952 3953 3969 3981 3995 4010 4036 4046 4064 4081
## [316] 4090 4098 4101 4105 4106 4116 4154 4184 4224 4234 4256 4262 4263 4273 4291
## [331] 4299 4308 4380 4444 4448 4449 4459 4475 4480 4481 4513 4517 4527 4541 4550
## [346] 4570 4590 4593 4616 4638 4648 4651 4717 4720 4723 4735 4737 4758 4767 4772
## [361] 4794 4818 4819 4840 4850 4861 4866 4882 4886 4893 4926 4931 4934 4943 4953
## [376] 4967 5002 5004 5009 5035 5045 5069 5070 5107 5136 5137 5141 5147 5159 5160
## [391] 5167 5172 5175 5193 5204 5217 5228 5235 5237 5257 5258 5268 5289 5327 5370
## [406] 5386 5389 5391 5400 5405 5407 5409 5420 5451 5472 5473 5494 5546 5561 5568
## [421] 5570 5593 5599 5600 5630 5633 5656 5663 5686 5707 5741 5772 5786 5793 5821
## [436] 5832 5835 5847 5887 5889 5894 5901 5909 5910 5916 5946 5947 5997 6000 6008
## [451] 6012 6020 6042 6048 6052 6057 6065 6070 6084 6091 6092 6123 6124 6137 6145
## [466] 6164 6172 6179 6188 6196 6201 6210 6211 6212 6227 6228 6238 6245 6279 6303
## [481] 6310 6312 6341 6364 6376 6383 6401 6403 6427 6430 6449 6475 6481 6491 6514
## [496] 6515 6520 6527 6565 6582 6598 6606 6611 6630 6631 6638 6658 6671 6675 6686
## [511] 6691 6705 6706 6714 6723 6731 6755 6762 6765 6779 6807 6828 6829 6849 6854
## [526] 6882 6894 6895 6906 6912 6914 6927 6928 6929 6943 6961 6973 7012 7016 7076
## [541] 7085 7107 7122 7151 7168 7220 7240 7253 7283 7312 7390 7414 7417 7425 7448
## [556] 7475 7482 7486 7487 7525 7532 7534 7537 7538 7577 7583 7584 7586 7627 7643
## [571] 7652 7673 7678 7700 7704 7710 7714 7715 7751 7787 7791 7820 7823 7824 7826
## [586] 7837 7870 7875 7879 7883 7901 7902 7938 7959 7970 7989 8002 8004 8016 8035
## [601] 8050 8064 8066 8069 8070 8086 8095 8105 8109 8112 8130 8139 8201 8204 8224
## [616] 8229 8232 8248 8254 8279 8306 8308 8316 8329 8333 8338 8339 8364 8372 8384
## [631] 8385 8388 8401 8405 8417 8419 8423 8428 8437 8445 8476 8477 8511 8517 8519
## [646] 8522 8543 8559 8584 8587 8592 8625 8648 8653 8671 8694 8711 8715 8719 8741
## [661] 8742 8760 8773 8777 8807 8818 8864 8873 8895 8904 8916 8922 8928 8929 8943
## [676] 8946 8948 8959 8965 8974 8995 9009 9018 9027 9028 9033 9051 9080 9081 9118
## [691] 9130 9137 9141 9146 9159 9177 9178 9210 9216 9257 9263 9314 9316 9321 9338
## [706] 9342 9361 9363 9365 9367 9369 9419 9431 9435 9437 9445 9489 9503 9526 9531
## [721] 9540 9565 9571 9574 9581 9595 9599 9622 9625 9641 9644 9648 9677 9683 9687
## [736] 9702 9740 9776 9787 9791 9828 9832 9834 9894 9904 9920 9942 9958 9966 9972
## [751] 9978 9993
## 
## $Mergedlabels
##  [1] 593 673 700 621 474 535 591 186 220 588 289 507 563 429 532 587 590 675 180
## [20] 215 253 329 389 506 534 360 391 565 566  68 114 179 187 216 291 362 388 473
## [39] 564 719 742 743 330 359 361 390 530 178 181 183 290 392 533 182 185 475 531
## [58] 592
## 
## $Newlabel
## [1] 826

Proportions comparison between manual and automatic gating

We can compare proportions providing by automatic gating (cytoftree) and manual gating for the selected sub-populations.

	CD4+	CD8+
Manual Gating	0.182	0.065
Automating Gating	0.195	0.075

cytoftree provides good results, close to the proportions getting by manual gating.

Anthony Devaux, Boris Hejblum

2022-11-08

Introduction to `cytoftree`

Data transformation

Binary tree construction

Post-hoc annotation

Influenza vaccine response dataset analysis with `cytoftree`

Data preparation

CytofTree function

High dimensional issues

Annotation function

`RetrievePops` : providing informations for particular sub-populations

Proportions comparison between manual and automatic gating

`cytoftree`: extension of `cytometree` to analyze mass cytometry data

Anthony Devaux, Boris Hejblum

2022-11-08

Introduction to cytoftree

Data transformation

Binary tree construction

Post-hoc annotation

Influenza vaccine response dataset analysis with cytoftree

Data preparation

CytofTree function

High dimensional issues

Annotation function

RetrievePops : providing informations for particular sub-populations

Proportions comparison between manual and automatic gating

Introduction to `cytoftree`

Influenza vaccine response dataset analysis with `cytoftree`

`RetrievePops` : providing informations for particular sub-populations