slingshot dev gene expression
2022-06-27
library(Seurat)## Attaching SeuratObjectlibrary(slingshot)## Loading required package: princurvelibrary(dplyr)##
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## Assays## Loading required package: ggplot2library(ggplot2)
library(dplyr)
library(SeuratWrappers)pbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/pbmc_human/pbmc_tutorial_final.rds")
current.cluster.ids <- c(0:8)
new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T", "FCGR3A+ Mono", "NK", "DC", "Platelet")
pbmc@meta.data$celltype <- plyr::mapvalues(x = pbmc@meta.data[,"seurat_clusters"], from = current.cluster.ids, to = new.cluster.ids)DimPlot(pbmc)
epi <- pbmc
sub1 <- subset(epi, cells = rownames(epi@meta.data[epi@meta.data$celltype %in% c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T"), ]))sub1 <- FindVariableFeatures(sub1, nfeatures = 3000)
sub1 <- ScaleData(sub1)## Centering and scaling data matrixsub1 <- RunPCA(sub1)## PC_ 1
## Positive: MALAT1, RPS27A, LTB, CD3D, RPS6, RPL13A, RPS3, RPL3, RPS3A, IL32
## CD3E, RPL13, IL7R, RPL10A, AES, TPT1, CD7, CXCR4, RPS5, B2M
## BTG1, CD2, RPL19, CD69, ACAP1, RARRES3, STK17A, GIMAP5, AQP3, TRAF3IP3
## Negative: TYROBP, CST3, S100A9, LYZ, FCN1, S100A8, LGALS2, FCER1G, FTL, LST1
## FTH1, TYMP, LGALS1, AIF1, HLA-DRA, CFD, HLA-DRB1, GSTP1, CTSS, GRN
## CD68, GPX1, CD14, HLA-DRB5, PSAP, SAT1, SPI1, MS4A6A, SERPINA1, NPC2
## PC_ 2
## Positive: S100A8, S100A9, S100A12, FTL, JUND, TYROBP, G0S2, AIF1, IL8, LGALS2
## LYZ, FOLR3, C5AR1, MALAT1, LST1, FCER1G, MS4A6A, RETN, CST3, FCN1
## FTH1, SMCO4, RPL13, TMSB4X, ADM, CD8B, C19orf59, MARC1, IL1B, LINC00937
## Negative: B2M, ACTG1, ARHGDIB, MYL12B, UBB, HNRNPA2B1, ENO1, VIM, IL32, RAC2
## CLIC1, ANXA1, PSMB9, ACTB, FXYD5, HMGN1, ACTR3, LTB, RAN, PDIA3
## ANXA5, CD2, ARPC5, ARL6IP5, GDI2, RARRES3, HNRNPF, DOK2, CD47, SRSF3
## PC_ 3
## Positive: RPL19, RPL13, RPS5, RPL10A, RPS6, NACA, RPL3, C6orf48, RPL7A, S100B
## CD8B, RGS10, TPT1, RPS3, RP11-291B21.2, RPL13A, RPS3A, EIF3H, RSL1D1, HLA-DQA2
## HLA-DQA1, C1QBP, HSP90AB1, NELL2, TMEM176B, HLA-DMB, REG4, CD7, LILRB2, ALKBH7
## Negative: TMSB4X, S100A4, B2M, ALOX5AP, S100A10, IL32, TNFRSF4, TIGIT, CST7, ANXA1
## GZMA, SH3BGRL3, KLF6, GZMK, OPTN, S100A11, C12orf75, CLIC1, CCL5, PPP2R5C
## S100A6, ACTB, PMAIP1, CCR10, LGALS1, PTTG1, GAPDH, CYBA, CCR6, GALM
## PC_ 4
## Positive: JUNB, ANXA1, LTB, USP10, IL7R, JUN, ALOX5AP, VIM, TNFRSF4, S100A11
## MAL, FXYD5, CRIP2, TNFAIP8, CD37, AQP3, DNAJB9, TPT1, KLF6, BIRC3
## RP11-403A21.2, IFI44L, CCR6, CD69, SLC2A3, S100A4, TRADD, IL23A, PIM3, NFKBIA
## Negative: CCL5, CD8B, GZMK, GZMA, NKG7, CST7, STMN1, MT1E, KIAA0101, MKI67
## FXYD2, CTSW, PTTG1, MYBL2, RP11-291B21.2, FABP5, LYAR, RP5-1028K7.2, TK1, ZWINT
## TYMS, APOBEC3H, KIFC1, RRM2, CENPM, MAD2L1, CPNE2, SDPR, IFNG, BROX
## PC_ 5
## Positive: S100A12, VIM, FOLR3, GAPDH, IL8, S100A8, RETN, NFE2, TSPO, TALDO1
## ID1, S100A9, LAPTM4B, GPX1, RPL19, RPL10A, ALDH1A1, ACTG1, C19orf59, TNNT1
## TMEM91, ANG, CD14, PLBD1, MARC1, CEBPD, IER3, HMGB2, SAT2, SMARCD3
## Negative: APOBEC3B, APOBEC3A, HLA-DQA2, HLA-DQA1, IFIT1, IFITM3, ABI3, HLA-DPA1, IFIT3, HLA-DPB1
## B2M, LY6E, OASL, ISG15, TMSB4X, HES4, MGLL, SPATS2L, GBP1, FCGR3A
## WARS, RNF213, MT2A, OAS1, TNFSF10, MALAT1, IFITM2, UBE2L6, TPPP3, IFI6sub1 <- RunUMAP(sub1, dims = 1:10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 11:25:32 UMAP embedding parameters a = 0.9922 b = 1.112## 11:25:32 Read 1663 rows and found 10 numeric columns## 11:25:32 Using Annoy for neighbor search, n_neighbors = 30## 11:25:32 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 11:25:32 Writing NN index file to temp file /tmp/RtmpN5YCtD/filea0650584ad9de
## 11:25:32 Searching Annoy index using 1 thread, search_k = 3000
## 11:25:33 Annoy recall = 100%
## 11:25:34 Commencing smooth kNN distance calibration using 1 thread
## 11:25:35 Initializing from normalized Laplacian + noise
## 11:25:35 Commencing optimization for 500 epochs, with 65128 positive edges
## 11:25:38 Optimization finishedConver seurat to sce
sub_sce1 <- as.SingleCellExperiment(sub1)Determine cell fate by your own
sub_slingshot1 <- slingshot(sub_sce1, clusterLabels = "celltype", reducedDim = 'UMAP', start.clus="Naive CD4 T", end.clus="Memory CD4 T")## Using full covariance matrixt1 <- sub_slingshot1$slingPseudotime_1
t1## [1] 6.286284464 10.062236240 NA 10.134710991 NA
## [6] 2.349435807 0.000000000 NA NA NA
## [11] NA 8.518430072 4.347636025 NA 3.821501751
## [16] 2.146858756 1.578136647 10.493762709 NA NA
## [21] 9.915373817 NA 4.832079340 NA 1.837395486
## [26] 7.466313466 5.429324267 1.188507918 1.720021053 9.597668926
## [31] 5.373385805 NA 2.438091031 NA 0.477805145
## [36] 7.412518622 NA 8.846064308 NA NA
## [41] 2.264842903 2.027697665 2.639144139 10.268591238 7.444462381
## [46] 2.235091217 9.176919047 8.459727499 10.346054023 0.597424319
## [51] 4.994513473 9.772304880 1.399303091 NA NA
## [56] NA 9.704230468 NA 3.274219172 NA
## [61] 2.746157606 NA NA NA 2.881555582
## [66] NA NA NA 6.304330053 NA
## [71] 2.568274843 NA 1.060642581 NA 10.419783536
## [76] NA 1.997054531 4.819373113 NA 0.000000000
## [81] 4.012087868 NA 6.282910137 4.158600175 1.435955637
## [86] 1.419539180 6.380969349 8.549610341 3.220641190 1.453069282
## [91] 4.629914669 NA 0.490296961 8.429061273 1.656188365
## [96] NA 6.400868725 1.632353387 NA NA
## [101] NA 7.706132203 1.081556025 0.802041793 7.283253093
## [106] 8.778442708 NA 10.493762709 1.841983008 8.691184248
## [111] 3.033065548 NA 7.788152514 NA 4.981592299
## [116] 10.493762709 4.117517872 NA 6.318942104 10.493762709
## [121] NA 1.665525270 NA 4.090644229 NA
## [126] NA 1.709026169 0.045150164 0.000000000 NA
## [131] 4.227699896 4.685798079 0.325061762 3.900922277 1.743269188
## [136] 6.823867779 6.143129263 9.169182396 2.398806968 NA
## [141] 8.616496581 10.493762709 3.668653984 8.545568979 NA
## [146] NA NA 7.978921448 NA 0.000000000
## [151] 1.638344038 10.493762709 NA 9.079640670 8.437930083
## [156] 7.907488933 5.439294300 NA NA NA
## [161] 3.723524114 5.741522590 2.155277709 0.000000000 9.376001225
## [166] NA 7.426657582 10.493762709 NA NA
## [171] NA 3.836443340 6.875035765 7.016355919 1.582665372
## [176] 2.145944393 NA 8.619925802 9.442650834 1.949104181
## [181] NA NA 9.251498326 6.738221861 8.373884529
## [186] 8.406797521 NA 7.386971057 NA NA
## [191] NA 2.995309082 9.516055765 10.493762709 2.774828114
## [196] NA NA 2.016648589 NA NA
## [201] 10.434416747 0.549042408 0.000000000 1.984701367 8.900710490
## [206] 2.204813011 1.611889297 NA 10.224899195 NA
## [211] NA NA NA 1.013654132 NA
## [216] 2.849189797 2.188141604 10.487994592 9.747750325 2.037381365
## [221] NA 6.754642048 NA 3.560995098 1.463129769
## [226] NA 6.279399167 NA 9.711503559 2.922652713
## [231] 9.468722752 0.000000000 3.649812781 1.806274125 NA
## [236] NA 8.513238324 10.493762709 NA 1.764173532
## [241] 2.838895053 NA 8.164353767 NA NA
## [246] 0.765922537 6.671815012 7.798824220 NA NA
## [251] 10.493762709 NA NA 0.000000000 4.531078066
## [256] 0.719064645 9.519252862 1.428347987 8.150123546 NA
## [261] 9.400145282 NA 0.000000000 9.318993845 0.000000000
## [266] 2.295851256 7.504392293 1.743511008 1.816561758 8.283333514
## [271] 2.977432870 5.257482227 9.907136627 0.301411688 2.361946038
## [276] 0.617025866 7.801425568 9.530639430 NA 0.710104378
## [281] 1.957923223 0.046476467 NA 9.757241169 6.077960490
## [286] 5.501687309 4.176352895 9.980734676 6.273152590 7.902965405
## [291] 2.320111719 2.053228973 8.652451774 7.679446050 2.060847614
## [296] 0.000000000 4.221938432 2.623347613 0.199233111 6.356593044
## [301] 0.000000000 NA 7.306274535 NA NA
## [306] 5.513551768 1.796860911 NA NA 3.751711236
## [311] 7.645827227 7.857823914 9.864244303 1.014920351 0.000000000
## [316] 8.445803083 6.880700767 3.074367582 9.648633026 5.120335672
## [321] 8.511855115 3.268713621 8.248060361 3.435659320 10.493762709
## [326] NA 2.188392142 8.158523090 NA NA
## [331] 7.731343257 NA 9.820913259 8.242623340 NA
## [336] 0.881550726 1.936700326 3.480836339 1.878768270 1.684318535
## [341] NA 6.526448654 0.508786821 9.016101674 NA
## [346] 8.909362279 6.517787994 0.772798638 0.000000000 NA
## [351] 8.092862628 NA NA 8.769913890 NA
## [356] 5.959629842 7.939455054 1.748913061 NA NA
## [361] NA NA NA 7.734023787 2.360876059
## [366] 2.443882597 0.000000000 NA NA 8.796582784
## [371] 3.263820423 2.166811241 NA 1.469861942 NA
## [376] 2.161432430 0.112271564 4.661170081 3.449941722 1.139782258
## [381] 8.861682842 NA NA 1.652594861 4.854752369
## [386] 2.098453368 NA 8.553335672 0.407574373 8.697387010
## [391] 10.131571217 NA NA 1.730639179 8.261083196
## [396] NA 7.647601288 5.439294300 2.279024469 6.765057636
## [401] NA NA NA NA 7.558544255
## [406] 4.202035052 NA NA NA 8.535447555
## [411] 8.675594651 7.926292963 7.966624712 NA NA
## [416] 6.216231223 2.967486236 7.805915981 6.385419070 1.486832514
## [421] 0.893663699 8.505790982 2.029582134 1.998567687 1.964088241
## [426] NA 7.287504678 2.235994731 NA 7.398820906
## [431] 0.000000000 NA NA NA 6.135595021
## [436] 2.460720380 8.561204884 9.207020890 9.836304426 7.322270730
## [441] 0.000000000 0.000000000 7.666581744 NA 5.612957523
## [446] 9.244129772 NA NA NA 9.500067309
## [451] NA 9.393476726 6.561830657 9.149055693 NA
## [456] 3.698609568 8.736281523 1.878109217 7.863350391 NA
## [461] NA NA 10.493762709 6.154537902 2.013819930
## [466] 8.530833974 8.759200501 5.728989159 1.905296886 NA
## [471] 7.650561334 5.984781489 8.909362279 9.312860164 0.000000000
## [476] 7.958853485 10.092226128 4.065063136 NA 8.377856450
## [481] 5.501687309 NA 2.937816524 5.569947992 NA
## [486] 1.926810898 8.830662102 NA NA 2.227289243
## [491] 6.990474827 NA NA NA NA
## [496] 2.251734509 1.898637142 10.332070364 NA NA
## [501] NA 0.000000000 3.436326471 7.052126696 7.731999158
## [506] 0.000000000 3.905250624 NA 2.197159834 6.523806330
## [511] 0.335789082 3.029770257 9.702465575 7.147348656 NA
## [516] 7.457258712 NA 0.866633246 2.997241231 3.310306580
## [521] NA 9.302209149 1.462779315 9.625674006 0.388082480
## [526] 7.190695353 0.814624699 NA 0.001610670 1.697656366
## [531] 7.930681505 4.388405915 0.442966845 NA 6.900794868
## [536] 8.631376598 9.822509962 NA 2.224270688 2.010517366
## [541] 8.498368453 0.000000000 1.618848960 2.355466963 0.000000000
## [546] 0.000000000 0.000000000 NA 7.189170210 NA
## [551] 6.831818086 2.208472039 NA NA 7.639421663
## [556] NA 8.789417340 2.505288260 10.493762709 8.236352069
## [561] NA 8.244385108 NA NA 0.051448924
## [566] 8.552793269 7.049797896 2.285082371 8.530289862 NA
## [571] 6.742479667 10.085482036 8.436564235 3.504148962 0.000000000
## [576] NA 1.335306885 2.394819821 NA 2.581041245
## [581] 8.491660752 3.287125560 2.221035529 NA 5.288838736
## [586] 8.460971126 8.684723563 0.000000000 NA NA
## [591] NA 1.963132219 9.187416036 7.391053500 5.905850276
## [596] NA 5.470107119 5.608178448 9.267518004 NA
## [601] NA 1.791427613 5.834832717 NA 0.000000000
## [606] 4.956979682 NA NA 7.296933371 1.959243565
## [611] 6.021570133 2.516204212 1.101656376 1.871981329 NA
## [616] NA NA NA 6.457979003 8.236858468
## [621] 6.330401878 0.453263920 8.073910497 2.099823234 0.000000000
## [626] NA 3.149286971 1.462779315 1.408769044 NA
## [631] NA 5.028673029 NA 8.145817286 1.281091956
## [636] NA 5.803014405 NA NA 8.847128588
## [641] 3.091097000 NA 0.000000000 NA 0.017995730
## [646] 8.945121251 7.869874716 5.727900006 NA NA
## [651] 9.418312949 0.000000000 NA NA NA
## [656] NA 6.396530380 NA NA 9.855957841
## [661] 0.088606592 1.283622769 7.173834216 NA 5.484243053
## [666] 9.779336999 NA 3.833603355 6.489927614 2.704024552
## [671] NA NA NA NA 1.843610953
## [676] 6.506240608 NA 0.839817154 3.447799723 NA
## [681] NA 7.718485041 1.033052429 2.940666281 1.974267629
## [686] 2.621240706 7.959371303 7.443504185 NA 2.997803277
## [691] 2.372660912 NA 7.919336397 1.471230362 NA
## [696] NA NA 7.940094049 7.530528613 NA
## [701] 0.000000000 6.903408215 2.285494904 9.157035580 8.024392494
## [706] 6.860806939 NA 0.000000000 NA 8.474913333
## [711] NA 2.768701226 NA NA 6.236969575
## [716] 6.193265447 6.432283185 8.253008505 NA 4.162357894
## [721] NA NA NA NA 9.947297524
## [726] NA 10.167486540 NA 6.530763963 7.069076054
## [731] 8.775983424 NA 0.000000000 0.325172301 3.111402759
## [736] 5.493942347 8.694163207 5.174517692 NA 3.939349011
## [741] 6.571328519 9.489660409 NA 0.000000000 1.735548584
## [746] 6.569458818 8.395296711 NA 8.533456921 6.154950885
## [751] 6.567503794 NA NA 9.047332317 10.240862686
## [756] 5.512780380 8.135508424 NA 6.375747279 5.390721988
## [761] NA 6.162740117 10.493762709 3.462687656 7.380260790
## [766] NA 2.462270687 NA 9.113047182 NA
## [771] 1.614973959 NA 5.052996892 NA 8.246316797
## [776] 1.295968214 NA 2.018919574 6.600896489 8.510776682
## [781] 8.902893472 8.520249912 6.841003665 2.077968970 2.081739005
## [786] 0.154837156 NA 9.031798331 NA NA
## [791] 0.204396593 7.967826970 7.689985283 2.757311361 NA
## [796] 6.744213105 NA 0.428114108 NA 8.092862628
## [801] 8.010041607 NA 4.686900768 NA 0.996101571
## [806] 1.308619607 NA 7.788152514 NA 3.817139226
## [811] NA 10.136150285 10.493762709 NA NA
## [816] NA 6.304330053 NA 0.000000000 8.871316749
## [821] 9.465544712 7.687629722 0.928591890 0.738165368 NA
## [826] 1.220399326 NA NA 4.157887474 NA
## [831] NA 0.000000000 8.187076767 2.340907074 NA
## [836] 0.000000000 5.481546566 8.523986214 4.620177695 8.938085468
## [841] NA NA NA 5.368444880 7.896820652
## [846] NA 1.115821885 8.594482672 NA 2.435278178
## [851] NA 5.984781489 5.525914938 9.363724832 4.663931009
## [856] 8.436564235 NA 2.069400796 NA 4.005655647
## [861] 1.980921517 8.185387339 0.199406705 2.625450915 NA
## [866] 7.575071525 8.683291630 7.036551002 2.505613693 6.972312884
## [871] 9.517867709 0.000000000 8.401926757 4.713182062 NA
## [876] 1.915562502 4.329887980 6.380098275 7.076080071 NA
## [881] 2.249289201 NA 0.000000000 6.248282430 NA
## [886] 8.185387339 5.493205189 NA NA 6.729735696
## [891] NA NA 3.631715863 8.677958939 2.154832897
## [896] 4.302693394 2.406266349 7.826089727 NA 3.175316032
## [901] 9.465650147 9.359955743 4.023603563 7.386810105 NA
## [906] NA 5.155798934 NA 9.319027700 7.107986219
## [911] 3.293005545 3.632250181 5.872801503 2.367972073 3.660889955
## [916] 0.077835548 7.039132900 0.652340160 0.530608358 5.921408593
## [921] 0.000000000 0.856492194 2.068918269 8.123044663 7.889769637
## [926] 7.791574554 8.783651628 NA NA NA
## [931] 8.328680384 NA 9.874797711 NA 3.332459421
## [936] NA 7.755648650 10.493762709 9.691650046 1.573962346
## [941] NA 2.633128291 NA NA 10.493762709
## [946] 10.004916900 NA 9.214264026 1.984701367 NA
## [951] 3.250481199 NA 7.367601897 8.730816367 2.757564852
## [956] 1.144833724 7.736340384 3.314711974 0.266128916 4.844093434
## [961] 6.548556078 7.048994290 NA NA 5.891000442
## [966] 9.099841825 2.744577330 NA 1.423807271 2.933069283
## [971] 10.493762709 NA 0.445176352 9.128771232 9.493897075
## [976] 1.479520402 1.939440045 8.309400084 NA 2.136725997
## [981] 1.894859856 9.110416369 NA NA 2.036041192
## [986] 1.859647822 NA 9.329896774 NA NA
## [991] NA 9.210069449 9.856798555 7.291257598 9.268836266
## [996] 7.834463209 2.222742111 NA 4.840001774 NA
## [1001] 4.677173390 NA NA NA 3.305602018
## [1006] 5.833255188 10.493762709 0.319122110 2.837083882 8.957219715
## [1011] 0.841222467 NA NA NA NA
## [1016] 0.463271754 1.419297131 NA 0.000000000 NA
## [1021] 8.167509384 NA 1.583041333 9.091174740 2.979208789
## [1026] 2.541775724 NA 0.000000000 NA 0.890784692
## [1031] NA 7.331942216 NA 0.194866786 9.450374984
## [1036] 5.974066638 8.168963413 NA NA 7.661357197
## [1041] 7.043080379 0.903458388 NA 5.472488481 1.762017978
## [1046] 2.451562016 NA NA 1.088377961 8.971773113
## [1051] NA 1.234637815 NA NA 1.726654267
## [1056] 9.539873082 9.164168410 0.625452964 6.202611068 NA
## [1061] NA 8.271385088 7.985966941 NA 8.502610848
## [1066] NA NA NA NA 9.259892013
## [1071] 5.218142455 NA 7.948943451 0.222636344 NA
## [1076] 9.673494798 7.947313801 4.772742013 5.557925604 1.942295464
## [1081] 0.416204661 2.557997681 9.407161371 4.189059598 0.253801677
## [1086] 6.779306923 0.820322020 1.200571812 9.279187127 6.464191143
## [1091] 2.791869761 4.912944426 NA 2.647225326 0.339617849
## [1096] 1.186878165 NA 8.410044932 7.476806646 3.107201792
## [1101] NA 0.306369412 0.000000000 4.577969594 2.240020949
## [1106] 10.047946410 NA NA 2.061281591 6.932024007
## [1111] 1.773716040 NA 8.553335672 5.473215526 2.617904814
## [1116] NA 3.403907757 2.263427552 1.204097989 1.345587459
## [1121] 0.000000000 6.544079091 NA 6.541541795 NA
## [1126] 7.739422002 NA 9.729600337 1.339955346 6.518607692
## [1131] 9.986442585 9.068245603 4.816210769 NA 0.437181641
## [1136] 2.391144018 10.493762709 NA 3.244804341 0.660572706
## [1141] NA NA NA 1.210712513 NA
## [1146] 0.000000000 NA 10.358286614 NA 5.887909168
## [1151] NA NA 9.739877427 7.788152514 6.646210369
## [1156] 6.825215581 10.034961692 NA 0.000000000 0.985793931
## [1161] 7.657507339 NA NA 8.856707593 NA
## [1166] NA 1.424616593 NA 8.606462696 3.652621906
## [1171] NA NA 0.903716060 7.977507864 9.823595240
## [1176] 7.004435186 8.550442493 8.213445916 4.161541422 NA
## [1181] 7.847082976 NA 0.000000000 NA 0.000000000
## [1186] 9.028666959 9.816717984 NA 8.595772257 NA
## [1191] 3.273554847 NA 1.788159209 8.638751055 9.565558411
## [1196] 10.223167341 0.000000000 5.532267334 10.006686288 3.328463779
## [1201] 2.045407061 7.338855562 7.267920224 2.237832683 NA
## [1206] NA NA 1.602825891 8.003668403 2.056734191
## [1211] NA 2.368227860 2.890383173 NA 1.562598014
## [1216] 8.865102365 NA 0.955951323 NA 8.600159969
## [1221] 1.901577077 NA 9.852239221 1.361857616 0.000000000
## [1226] 0.000000000 7.167841176 4.423422774 5.845608437 8.875982765
## [1231] NA NA 1.733688511 NA 6.382264563
## [1236] NA 0.000000000 NA NA NA
## [1241] 1.497273155 9.844994315 10.009136456 NA NA
## [1246] 6.014201892 NA NA 3.429913083 0.943177824
## [1251] 0.037169169 NA 4.985927731 NA NA
## [1256] NA 2.222742111 7.941966535 NA 4.513488567
## [1261] NA 2.754095895 NA 9.101618603 10.248495795
## [1266] 5.280506269 2.069400796 8.748609491 NA 2.722579655
## [1271] 0.738237472 7.812217480 0.648855120 2.134237102 1.957923223
## [1276] 0.709180083 NA NA 5.061114645 NA
## [1281] 2.964208172 6.422170059 5.610124026 9.835717783 7.717852762
## [1286] NA 3.360451679 2.526801424 8.530833974 1.428295909
## [1291] 0.314561729 7.975555126 5.733947393 8.097657916 5.194891627
## [1296] NA 5.581114879 0.901325838 2.217636123 NA
## [1301] 8.930180754 4.329336437 NA 1.848893745 NA
## [1306] 8.423226334 7.581719193 0.000000000 NA NA
## [1311] 3.413247661 NA 0.000000000 9.065778797 8.445803083
## [1316] 0.982236542 NA NA NA NA
## [1321] 0.579256191 7.351629564 NA NA 1.492858765
## [1326] 6.298646584 1.784520341 NA 9.873371962 0.000000000
## [1331] 9.216841648 5.973767989 0.879298569 8.799369266 3.800523025
## [1336] NA 6.280622826 8.230512196 NA 7.128205210
## [1341] 1.488055780 5.855234349 2.845389861 1.952160794 NA
## [1346] NA NA 1.446397542 2.468112016 NA
## [1351] 8.990194774 7.712338018 2.700348777 NA 10.493762709
## [1356] 7.107742592 9.154291658 8.227036242 NA 4.535000471
## [1361] NA 0.381063676 7.498985690 NA 1.421453120
## [1366] 1.257713098 NA 5.030499421 8.224247266 8.530833974
## [1371] 2.178635906 2.999233255 NA 4.393877750 0.734170803
## [1376] 4.936225135 0.376833608 NA 5.477340082 2.415564731
## [1381] 3.731308728 8.206796822 3.746836809 6.031548480 10.331569221
## [1386] NA 0.000000000 6.962673738 7.489202183 NA
## [1391] 1.904914120 8.218636204 9.933279420 9.568095149 NA
## [1396] 2.591763519 1.806274125 3.765813764 NA 8.884393663
## [1401] 3.831806225 5.227563857 5.965142310 NA NA
## [1406] 3.176934906 0.000000000 6.726202452 10.065083409 NA
## [1411] NA 3.993259255 5.897707277 1.540426235 9.881839385
## [1416] 5.399103974 NA NA 3.204993507 NA
## [1421] 2.735812577 NA NA 1.813007374 5.134722974
## [1426] NA 5.224392298 NA 6.448470076 3.739684552
## [1431] NA 8.563259390 3.438899901 7.171390817 1.907334043
## [1436] NA 8.037654069 2.365547230 NA 0.000000000
## [1441] 7.556232784 1.170467297 9.527254511 NA 1.466218094
## [1446] 7.139321561 0.397609257 0.000000000 NA 10.493762709
## [1451] NA NA 7.770534948 9.229332085 NA
## [1456] 8.508470389 7.079061824 NA 7.892382277 5.735622273
## [1461] 10.417086205 4.262610125 7.629989789 NA 0.797579654
## [1466] NA 2.748751237 6.626338633 8.463652206 2.283733051
## [1471] 9.449512552 7.192718170 8.493345981 6.973388725 NA
## [1476] NA NA NA NA NA
## [1481] 8.215812773 0.333970400 3.346750995 NA NA
## [1486] NA 5.984781489 1.356511083 0.000000000 1.674755109
## [1491] 5.663599795 2.027717666 9.117785910 2.786995213 NA
## [1496] 10.049362340 10.012706134 NA 7.456959782 3.643763518
## [1501] 8.988166341 7.726488871 NA 2.473532317 8.298903405
## [1506] 9.400283516 NA NA NA 8.447692184
## [1511] 0.124579448 8.356803212 2.943264976 NA 7.885957477
## [1516] 5.826830063 7.555576495 1.563033220 4.465128237 9.056243200
## [1521] 3.015227954 3.604547290 0.000000000 8.546617535 8.436564235
## [1526] NA 7.662870696 7.608901943 5.800438527 7.231536246
## [1531] 6.842118008 1.572729791 NA NA NA
## [1536] 0.944374020 NA 2.384208004 9.352020503 5.028654257
## [1541] 2.185667773 NA NA 9.784663042 NA
## [1546] 2.686274713 NA NA 0.727578091 NA
## [1551] NA 7.385523635 9.415364186 NA 7.588050561
## [1556] 9.725209929 NA 4.684879493 1.262467687 0.000000000
## [1561] NA 1.517421411 6.738669926 NA 8.558918876
## [1566] 0.662876108 0.000000000 9.682285703 5.882961760 5.090140691
## [1571] 1.841376404 2.845120110 7.399527898 3.117997609 NA
## [1576] NA NA 6.378764953 7.102585665 2.178597671
## [1581] NA 0.000000000 9.421625958 NA NA
## [1586] 5.792628346 1.155387496 NA 2.350308222 NA
## [1591] 2.473532317 NA 9.017065566 NA NA
## [1596] 9.756660658 6.237882468 NA 4.434770674 NA
## [1601] 8.989155521 0.000000000 NA NA 8.510081620
## [1606] 7.182100370 0.462669117 1.791427613 NA NA
## [1611] 5.024881275 8.577309674 8.335498492 NA 7.896797216
## [1616] NA NA 0.003606092 NA 3.429913083
## [1621] NA NA NA NA 3.580113150
## [1626] 7.610716764 9.577638376 5.036022991 NA 1.381531846
## [1631] 0.551233010 NA 0.672769380 8.811928425 9.300657832
## [1636] NA NA 0.000000000 9.649836568 2.592839418
## [1641] 7.518453159 NA 1.639225944 NA 6.636758788
## [1646] 2.125953692 NA 9.008071282 NA 1.879368858
## [1651] NA 4.011719080 9.853883294 7.041398836 1.616168812
## [1656] NA NA NA 3.586421491 NA
## [1661] 9.412019992 NA 2.404561849Which genes are you interested in?
gene.list <- c("CCR7","LDHB", "S100A9", "LTB", "CD79A")plot.val1 <- c(3, 0.8, 3, 4.25, 2)
plot.val2 <- c(7, 0.8, 2.75, 2, 2)loess_data1 <- as.data.frame(sub1@assays$RNA@data[gene.list, ])loess_data1 <- loess_data1[,order(t1)]temp1 <- loess_data1
temp1 <- t(temp1)
temp1 <- as.data.frame(temp1)
temp1$index <- 1:nrow(temp1)
temp1$ct <- sub1@meta.data$celltype[order(t1)]
plot.val1 <- c(3, 0.8, 3, 4.25, 2)
test <- c(sort(temp1[, 1])[.80*length(temp1[, 1])],
+ quantile(temp1[,2], 0.80),
+ quantile(temp1[,3], 0.80),
+ quantile(temp1[,4], 0.80),
+ quantile(temp1[,5], 0.80))
max(temp1[,1])## [1] 3.371166plot_list <- list()
j = 1
k=6
for (i in 1:length(gene.list)) {
print(paste(i, gene.list[i], sep = "_"))
p1 <- ggplot(temp1, aes_string(y = gene.list[i] , x = temp1[, (ncol(temp1) -1)])) + geom_smooth(method = loess) + coord_cartesian(ylim = c(0, plot.val1[i]))
plot_list[[j]] <- p1
j = j + 1
}## [1] "1_CCR7"
## [1] "2_LDHB"
## [1] "3_S100A9"
## [1] "4_LTB"
## [1] "5_CD79A"multiplot(plotlist = plot_list, cols = 2)## Warning: 'multiplot' is deprecated.
## Use 'gridExtra::grid.arrange' instead.
## See help("Deprecated")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'