-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathCOVID-19paed_PBMCanalyses.Rmd
2198 lines (1802 loc) · 183 KB
/
COVID-19paed_PBMCanalyses.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
---
title: "COVID-19paed_PBMCanalyses_RL003"
author: "Rik G.H. Lindeboom"
date: "10/11/2021"
output: pdf_document
---
Code to process and analyse the PBMC data in Yosida et al. Nature, 2021. by Rik Lindeboom. Please reach out if anything is unclear, missing or wrong. Some meta data processing of patient information is omitted to comply with ethics.
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,fig.height = 10, fig.width = 10,tidy.opts=list(width.cutoff=60),tidy=TRUE)
```
```{r load required packages, message=FALSE}
set.seed(1)
library(Seurat)
library(tidyverse)
library(ggplot2)
library(harmony)
library(ComplexHeatmap)
library(sceasy)
library(reticulate)
library(SoupX)
library(data.table)
library(DoubletFinder)
library(cardelino)
library(reticulate)
source("/mnt/scripts/RL003_function_collection_GitHub.R")
loompy <- import('loompy')
library(randomcoloR)
library(circlize)
library(readr)
library(patchwork)
library(cowplot)
library(lme4)
library(Matrix)
library(numDeriv)
```
```{r label="make sample table",eval = FALSE}
manifest <- read.csv("/mnt/projects/RL003_allCitePbmcsTheta/CV001_KM_COVID Sample tracking table - Manifest_210715.tsv",stringsAsFactors = F,header = T,sep="\t")
sampleTable <- read.csv("/mnt/projects/RL003_allCitePbmcsTheta/Pooled_pbmc_CITEseq_summary_kw_210422_procForR.csv",stringsAsFactors = F,header = T)
manis <- read.csv("/mnt/projects/RL003_allCitePbmcsTheta/RL003_manifest.txt",stringsAsFactors = F,header = T,sep = "\t")
manis$numberOfDonors <- sapply(gsub("^(.).*","\\1",manis$sample_id), function(x) sum(sampleTable$pool_group==x))
manis$sample_name <- manis$sample_id
manis$sample_id <- paste(manis$GEX,manis$CITE,sep="-")
manis$donors <- as.character(sapply(gsub("^(.).*","\\1",manis$sample_name), function(x) unique(sampleTable$Sample.name[sampleTable$pool_group==x])))
manis$location_multiplexed_bam <- paste0("/archive/HCA/10X/",manis$sample_id,"/outs")
manis$irods_or_farm <- "irods"
manis$bam_file <- "possorted_genome_bam.bam"
manis$barcodesLoc <- paste0("/archive/HCA/10X/",manis$sample_id,"/outs/filtered_feature_bc_matrix")
manis$barcode_file <- "barcodes.tsv.gz"
manis$baiFilePresent <- T
manis$bamReady <- NA
for (i in 1:nrow(manis)) {
foo <- tryCatch(system(paste0("ils ",manis$location_multiplexed_bam[i],"/",manis$bam_file[i])))
if (foo==0) { manis$bamReady[i] <- T } else { manis$bamReady[i] <- F }
}
manis$alignment <- "cellranger"
manis$citeFile <- paste0(manis$GEX,"-",manis$CITE)
socTable <- manis[,c("sample_name","sample_id","donors","numberOfDonors","location_multiplexed_bam","irods_or_farm","bam_file","barcodesLoc","barcode_file","baiFilePresent")]
write.table(socTable,file = "/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/sampleTable.txt",col.names = T,row.names = F,quote = F,sep=",")
```
'socTable' is used as input for souporcell:
```{bash,eval = FALSE}
# sample_name,sample_id,donors,numberOfDonors,location_multiplexed_bam,irods_or_farm,bam_file,barcodesLoc,barcode_file,baiFilePresent
# K1-PBMC,CV001_KM9465380-CV001_KM9465395,K1-PBMC;K2-PBMC,5,/archive/HCA/10X/CV001_KM9465380-CV001_KM9465395/outs,irods,possorted_genome_bam.bam,/archive/HCA/10X/CV001_KM9465380-CV001_KM9465395/outs/filtered_feature_bc_matrix,barcodes.tsv.gz,TRUE
dos2unix ${multiplexedSampleTable}
while read -r samplePair; do
sample_id=`echo ${samplePair} | cut -f2 -d','`
selected_k=`echo ${samplePair} | cut -f4 -d','`
location_multiplexed_bam=`echo ${samplePair} | cut -f5 -d','`
irods_or_farm=`echo ${samplePair} | cut -f6 -d','`
bam_file=`echo ${samplePair} | cut -f7 -d','`
barcodesLoc=`echo ${samplePair} | cut -f8 -d','`
barcode_file=`echo ${samplePair} | cut -f9 -d','`
baiFilePresent=`echo ${samplePair} | cut -f10 -d','`
if ! [[ ${sample_id} == 'sample_id' ]] ; then
#sample_id=${sample_id}_extraGenotype
cd ${outDir};
mkdir ${sample_id};
cd ${sample_id};
if [[ ${irods_or_farm} == 'irods' ]]; then
iget -Kr ${barcodesLoc}/${barcode_file};
iget -Kr ${location_multiplexed_bam}/${bam_file};
fi;
if [[ $baiFilePresent == FALSE ]]; then
samtools index -@ ${maxThreads} ${bam_file};
else
iget -Kr ${location_multiplexed_bam}/${bam_file}.bai;
fi;
cd ..;
singularity exec -B $PWD /home/ubuntu/bin/souporcell_latest.sif souporcell_pipeline.py -i ${sample_id}/${bam_file} -b ${sample_id}/${barcode_file} -f /mnt/souporcell/genome.fa -t ${maxThreads} -o ${outDir}/${sample_id} --common_variants ${knownVariantsFile} --skip_remap True -k ${selected_k};
rm ${sample_id}/${bam_file}; rm ${sample_id}/${bam_file}.bai; rm ${sample_id}/${barcode_file};
fi;
done < ${multiplexedSampleTable};
```
Downloading and processing the data from our internal storage systems to create a rds containing the GEX and ADT data using Seurat
``` {r label="download data",eval = FALSE}
for (i in 1:nrow(manis)) {
downloadScData(cite=manis$citeFile[i], bcr=manis$BCR[i], tcr=manis$TCR[i], overwrite=F, alignment=manis$alignment[i], out_dir="/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/")
}
for (i in 1:nrow(manis)) {
currentSample <- try(processCiteSamples(sample=manis$citeFile[i], SoupX_rna=T, SoupX_adt=T, save_raw=F, doSct=F, data_dir="/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cite", min_cells=0, min_features=200))
write_rds(currentSample,file=paste0("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/",manis$citeFile[i],".rds"),compress="gz")
}
sampleList <- list()
for (i in 1:nrow(manis)) {
currentSample <- read_rds(paste0("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/",manis$citeFile[i],".rds"))
currentSample <- RenameCells(currentSample,add.cell.id = manis$citeFile[i])
sampleList[[manis$citeFile[i]]] <- currentSample
rm(currentSample)
}
cov <- merge(sampleList[[1]], y = sampleList[2:length(sampleList)],merge.data = TRUE, project = "covidPbmcs_oldNew")
cv <- multiModal_processing(object=cov,gex=T,adt=T,sct=T,gexAdtWnn=F,sctAdtWnn=F,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=F,doFreshSct=T)
[email protected]$dataset <- ifelse(is.na([email protected]$patient_id),"revision","original")
[email protected][,c("GEX","CITE","BCR","TCR","pool_name","pool_patients")] <- NA
for (i in unique([email protected]$orig.ident)) {
sample_name <- manifest$sample_id[manifest$Sanger.Sample.ID==gsub("(.*?)-.*","\\1",i)]
[email protected]$pool_name[[email protected]$orig.ident==i] <- sample_name
[email protected]$GEX[[email protected]$orig.ident==i] <- manifest$Sanger.Sample.ID[manifest$sample_id==sample_name & manifest$modality=="GEX"]
[email protected]$CITE[[email protected]$orig.ident==i] <- manifest$Sanger.Sample.ID[manifest$sample_id==sample_name & manifest$modality=="CITE"]
try([email protected]$BCR[[email protected]$orig.ident==i] <- manifest$Sanger.Sample.ID[manifest$sample_id==sample_name & manifest$modality=="BCR"])
try([email protected]$TCR[[email protected]$orig.ident==i] <- manifest$Sanger.Sample.ID[manifest$sample_id==sample_name & manifest$modality=="TCR"])
}
for (i in unique(sampleTable$Sample.name)) {
samples <- unlist(str_split(i,";"))
patientIds <- paste(sampleTable$Individal.Samples.ID[sampleTable$Sample.name==i],collapse=";")
[email protected]$pool_patients[[email protected]$pool_name%in%samples] <- patientIds
}
for (i in unique([email protected]$orig.ident[[email protected]$dataset=="original"])) {
[email protected]$pool_patients[[email protected]$orig.ident==i] <- paste(unique([email protected]$patient_id[[email protected]$orig.ident==i]),collapse=";")
}
```
We use souporcell to demultiplex the pooled sequencing libraries based on their genotypes (see bash code above).
Manual inspection and itteration revealed that some failed to detect all genotypes because noise from one genotype is clustered into two, this is fixed by adding once more cluster for some samples where no match is found,
souporcell genotype doublets are used to 'train' DoubletFinder to find more doublets
``` {r label="add souporcell clusters to data for patient id assignment and for doublet detection",eval = FALSE}
for (i in unique([email protected]$orig.ident)) {
soc_out <- read.csv(paste0("/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/",i,"/clusters.tsv"),header=T,stringsAsFactor=F,sep="\t")
soc_out$matched_barcode <- paste(i,soc_out$barcode,sep = "_")
soc_out <- soc_out[soc_out$matched_barcode%in%rownames([email protected]),]
[email protected][soc_out$matched_barcode,colnames(soc_out)[colnames(soc_out)!="matched_barcode"]] <- soc_out[,colnames(soc_out)[colnames(soc_out)!="matched_barcode"]]
}
# Some failed to detect all genotypes because noise from one genotype is clustered into two, this is fixed by adding once more cluster for some samples where no match is found:
for (i in unique([email protected]$orig.ident)) {
if (file.exists(paste0("/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/",i,"_extraGenotype/clusters.tsv"))) {
soc_out <- read.csv(paste0("/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/",i,"_extraGenotype/clusters.tsv"),header=T,stringsAsFactor=F,sep="\t")
} else {
soc_out <- read.csv(paste0("/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/",i,"/clusters.tsv"),header=T,stringsAsFactor=F,sep="\t")
}
soc_out$matched_barcode <- paste(i,soc_out$barcode,sep = "_")
soc_out <- soc_out[soc_out$matched_barcode%in%rownames([email protected]),]
[email protected][soc_out$matched_barcode,colnames(soc_out)[colnames(soc_out)!="matched_barcode"]] <- soc_out[,colnames(soc_out)[colnames(soc_out)!="matched_barcode"]]
}
[email protected]$df_classification_onSinglets <- NA
for (i in unique([email protected]$orig.ident)) {
cv_subset <- subset(cv,cells=rownames([email protected])[[email protected]$orig.ident==i])
cv_subset <- runDoubletFinderOnSouporcellOutput(object=cv_subset)
[email protected][rownames([email protected]),c("pANN","DF.classifications","doubletFinder_params","df_classification_onSinglets")] <- [email protected][,c("pANN","DF.classifications","doubletFinder_params","df_classification_onSinglets")]
rm(cv_subset)
}
```
We've used the nasal GEX libraries (which were not multiplexed) to generate patient genotypes, which we match to souporcell clusters to assign patient ids to souporcell clusters. This required some minor manual adjustments for mismatches using the automated assignment.
```{r label="compare souporcell genotypes with nasal genotypes to identify patients",eval = FALSE}
ref_gt <- load_GT_vcf("/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/newNasals.dedup.280421.vcf.gz",na.rm = F)
allCors <- as.data.frame(ref_gt$GT[0,],stringsAsFactors = F)
[email protected]$matched_NB_sample <- NA
[email protected]$matched_NB_sample_overlap <- NA
for (i in unique([email protected]$orig.ident)) {
if (file.exists(paste0("/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision/",i,"_extraGenotype/clusters.tsv"))) {
myCors <- compareGenotype(ref_gt=ref_gt,souporcell_output_dir="/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision",sample_id=paste0(i,"_extraGenotype"))
} else {
myCors <- compareGenotype(ref_gt=ref_gt,souporcell_output_dir="/mnt/projects/RL003_allCitePbmcsTheta/souporcell_revision",sample_id=i)
}
specificCors <- myCors[,sampleTable$sangerId_matchedSample[!is.na(sampleTable$sangerId_matchedSample) & sampleTable$pool_group%in%gsub("^([A-Z]*?)[0-9].*","\\1",[email protected]$pool_name[[email protected]$orig.ident==i])]]
if (ncol(specificCors)==0) { print("no matching genotypes present in ref")} else {
specificCors <- specificCors[unique([email protected]$assignment[[email protected]$status=="singlet" & [email protected]$orig.ident==i]),]
for (myCluster in rownames(specificCors)[order(decreasing=T,apply(specificCors,1,max))]) {
mostLikelyPatient <- names(specificCors[myCluster,][order(specificCors[myCluster,],decreasing = T)])[1]
if (mostLikelyPatient%in%[email protected]$matched_NB_sample[[email protected]$orig.ident==i & !is.na([email protected]$matched_NB_sample)]) {
[email protected]$matched_NB_sample[[email protected]$orig.ident==i & [email protected]$assignment==myCluster] <- paste0(mostLikelyPatient,"_fail")
rownames(myCors)[rownames(myCors)==myCluster] <- paste0(mostLikelyPatient,"_fail;cluster",myCluster)
} else {
[email protected]$matched_NB_sample[[email protected]$orig.ident==i & [email protected]$assignment==myCluster] <- mostLikelyPatient
rownames(myCors)[rownames(myCors)==myCluster] <- paste0(mostLikelyPatient,";cluster",myCluster)
}
[email protected]$matched_NB_sample_overlap[[email protected]$orig.ident==i & [email protected]$assignment==myCluster] <- as.numeric(specificCors[myCluster,][order(specificCors[myCluster,],decreasing = T)][1])
}
}
rownames(myCors) <- paste0(unique([email protected]$pool_name[[email protected]$orig.ident==i]),";",rownames(myCors))
if (any(colnames(myCors)!=colnames(allCors))) { halt }
allCors <- rbind(allCors,myCors)
}
allCors <- allCors[order(rownames(allCors),decreasing = F),]
columnOrder <- c()
for (i in unique(gsub("([A-Z]*?)[0-9]*-.*","\\1",rownames(allCors)))){
columnOrder <- c(columnOrder,which(colnames(allCors)%in%sampleTable$sangerId_matchedSample[sampleTable$pool_group==i & !is.na(sampleTable$sangerId_matchedSample)]))
}
allCors <- allCors[,c(columnOrder,(1:ncol(allCors))[!(1:ncol(allCors))%in%columnOrder])]
#allCors_wNormalGenotypes <- allCors
Heatmap(allCors,cluster_rows = F,cluster_columns = F,row_names_gp = gpar(cex=.55))
#Heatmap(allCors_wNormalGenotypes,cluster_rows = F,cluster_columns = F,row_names_gp = gpar(cex=.55))
for (i in unique([email protected]$matched_NB_sample[!is.na([email protected]$matched_NB_sample) & !grepl("fail",[email protected]$matched_NB_sample)])) {
[email protected]$patient_id[!is.na([email protected]$matched_NB_sample) & [email protected]$matched_NB_sample==i] <- unique(sampleTable$Individal.Samples.ID[sampleTable$sangerId_matchedSample==i & !is.na(sampleTable$sangerId_matchedSample)])
[email protected]$state[!is.na([email protected]$matched_NB_sample) & [email protected]$matched_NB_sample==i] <- unique(sampleTable$state[sampleTable$sangerId_matchedSample==i & !is.na(sampleTable$sangerId_matchedSample)])
}
#Fix samples where no nasal data is available by assigning the only missing genotype
[email protected]$patient_id[[email protected]$pool_name=="H2-PBMC" & [email protected]$matched_NB_sample=="CV001_KM9166445_fail" & !is.na([email protected]$matched_NB_sample)] <- "PC7"
[email protected]$patient_id[[email protected]$pool_name=="K1-PBMC" & [email protected]$matched_NB_sample=="CV001_KM9465377_fail" & !is.na([email protected]$matched_NB_sample)] <- "PP11"
[email protected]$patient_id[[email protected]$pool_name=="K2-PBMC" & [email protected]$matched_NB_sample=="CV001_KM9465377_fail" & !is.na([email protected]$matched_NB_sample)] <- "PP11"
[email protected]$patient_id[[email protected]$pool_name%in%c("Q3-PBMC","Q4-PBMC") & [email protected]$matched_NB_sample=="CV001_KM9166355_fail" & !is.na([email protected]$matched_NB_sample)] <- "not_ready"
[email protected]$patient_id[[email protected]$pool_name%in%c("O1-PBMC","O2-PBMC","P1-PBMC","P2-PBMC")] <- "not_ready"
[email protected]$state[[email protected]$pool_name%in%c("O1-PBMC","O2-PBMC","P1-PBMC","P2-PBMC")] <- "Post-COVID"
```
``` {r label="initial filtering and processing",fig.width=10,fig.height=10,eval = FALSE}
cv[["percent.mt"]] <- PercentageFeatureSet(cv, pattern = "^MT-")
cv <- subset(cv, percent.mt < 10)
cv <- subset(cv,cells=rownames([email protected][([email protected]$df_classification_onSinglets=="singlet" & [email protected]$status=="singlet"),]))
cv <- subset(cv,cells=rownames([email protected][!grepl("CV001_KM9465",[email protected]$orig.ident),])) # Remove bad samples
cv <- FindNeighbors(cv, dims = 1:30,reduction = "harmony_RNA",graph.name="rna_snn")
cv <- FindClusters(cv, graph.name = "rna_snn", resolution = c(.5,4,32),algorithm = 4, method = "igraph", verbose = FALSE)
cv <- FindNeighbors(cv, dims = 1:30,reduction = "harmony_ADT",graph.name="adt_snn")
cv <- FindClusters(cv, graph.name = "adt_snn", resolution = c(.5,4,32),algorithm = 4, method = "igraph", verbose = FALSE)
cv <- FindClusters(cv, graph.name = "wsnn_rnaAdt", resolution = c(.5,4,32),algorithm = 4, method = "igraph", verbose = FALSE)
write_rds(cv,"/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.rds",compress = "gz")
```
We annotate Leiden clusters using cell type markers by subsetting large cell type compartments and reclustering to increase the resolution
``` {r label="subset and subcluster again with new hvgs",fig.height=10,fig.width=10,eval = FALSE}
[email protected]$subset <- NA
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("1","6","7","8")] <- "T"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("2","5","10")] <- "TNK"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("11")] <- "cycling"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("3","9")] <- "MonoDCs"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("12")] <- "platelets"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("4")] <- "B"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("14")] <- "HSPC"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("13","15")] <- "separateCelltypes"
resolutions <- c(0.5,4)
for (i in unique([email protected]$subset)) {
if (!file.exists(paste0("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_",i,".rds"))) {
try(rm(cv_subset))
gc()
cv <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.rds")
[email protected]$subset <- NA
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("1","6","7","8")] <- "T"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("2","5","10")] <- "TNK"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("11")] <- "cycling"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("3","9")] <- "MonoDCs"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("12")] <- "platelets"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("4")] <- "B"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("14")] <- "HSPC"
[email protected]$subset[[email protected]$rna_snn_res.0.5%in%c("13","15")] <- "separateCelltypes"
cv_subset <- subset(cv,cells=rownames([email protected][[email protected]$subset==i,]))
rm(cv)
gc()
cv_subset <- multiModal_processing(object=cv_subset,gex=T,adt=T,sct=T,gexAdtWnn=T,sctAdtWnn=T,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=T,doFreshSct=T)
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_RNA",graph.name="rna_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_ADT",graph.name="adt_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_SCT",graph.name="sct_snn")
cv_subset <- FindClusters(cv_subset, graph.name = "rna_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "adt_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "sct_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "wsnn_rnaAdt", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "wsnn_sctAdt", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- combineSmallWnnClusters(object=cv_subset,resolutions=resolutions,graphNames=c("wsnn_rnaAdt","wsnn_sctAdt"),minClusterSize=100)
write_rds(cv_subset,file=paste0("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_",i,".rds"),compress="gz")
}
}
```
In the chunks below we go over each compartment that is subsetted above to annotate cell types manually.
``` {r label="annotate seperate celltypes subset",eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_separateCelltypes.rds")
FeaturePlot(cv_subset,reduction = "umapAfterHarmony_SCT",features = c("pANN","log_prob_doublet","log_prob_singleton"))
gexOnlyList <- c("CD3D","CCR7","SELL","CD27","CD4","CD40LG","CD8A","GZMH","IL2RA","FOXP3","IKZF2","TRGV9","TRDV2","TRAV1-2","SLC4A10","MKI67","NCR1","NCAM1","FXYD7","FCGR3A","CD14","C1QA","CLEC4C","IL3RA","AXL","SIGLEC6","CLEC9A","FCER1A","FCER2","CXCR5","CD19","CCR6","IGHD","MS4A1","TNFRSF13B","ENTPD1","KIT","CD34","PPBP","PF4","HBB")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = T,label=T,group.by = "dataset")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = T,label=T,group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
FeaturePlot(cv_subset,dims = c(1,2),reduction = "umapAfterHarmony_RNA",features = c("HBB","IL3RA","CLEC4C","MKI67"))
foo <- FetchData(cv_subset,c("HBB","IL3RA","CLEC4C","CLEC10A"))
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(HBB+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(IL3RA+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(CLEC4C+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(CLEC10A+1))) + geom_point() + theme_classic()
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$HBB)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$IL3RA)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$CLEC4C)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$CLEC10A)
Idents(cv_subset) <- [email protected]$rna_snn_res.0.5
clust7Markers <- FindMarkers(object = cv_subset,ident.1 = "7",assay = "RNA",ident.2 = "1",logfc.threshold = .5,min.pct = .25,only.pos = T) # Seems to be so called AS-DC, markers are AXL SIGLEC6
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("2")] <- "Red Blood Cells"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("1","3","4","5")] <- "pDC"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("7")] <- "AS-DC"
[email protected]$cell_annot_revision[is.na([email protected]$cell_annot_revision)] <- "Doublets"
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
DotPlot(cv_subset,features = gexOnlyList,group.by = "cell_annot_revision",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_separateCelltypes_annotMeta.rds")
```
```{r annot hpsc subcluster,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_HSPC.rds")
DefaultAssay(object = cv_subset) <- "RNA"
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "sct_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "sct_snn_res.0.5")
gexOnlyList <- c("CD3D","CCR7","SELL","CD27","CD4","CD40LG","CD8A","GZMH","IL2RA","FOXP3","IKZF2","TRGV9","TRDV2","TRAV1-2","SLC4A10","MKI67","NCR1","NCAM1","FXYD7","FCGR3A","CD14","C1QA","CLEC4C","IL3RA","AXL","SIGLEC6","CLEC9A","FCER1A","FCER2","CXCR5","CD19","CCR6","IGHD","MS4A1","TNFRSF13B","ENTPD1","KIT","CD34","PPBP","PF4","HBB")
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
FeaturePlot(cv_subset,dims = c(1,2),reduction = "umapAfterHarmony_RNA",features = c("HBB","IL3RA","CLEC4C","MKI67"))
foo <- FetchData(cv_subset,c("TPSAB1","CPA3","TPSB2","C1QC","EPX","PRG2"))
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(TPSAB1+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(CPA3+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(TPSB2+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(C1QC+1))) + geom_point() + theme_classic()
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$TPSAB1)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$CPA3)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$TPSB2)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$C1QC)
Idents(cv_subset) <- paste([email protected]$rna_snn_res.0.5)
clust10Markers <- FindMarkers(object = cv_subset,ident.1 = "10",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust1Markers <- FindMarkers(object = cv_subset,ident.1 = "1",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("TPSAB1","CPA3","KIT","PRSS57","HPGDS","GATA2","TNFSF10","TRIM63","IGHA1","IGHA2","FCER1A"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("SEPP1","AMICA1","GNLY","KIAA1598","IL8","FTL","ALAS2","PTPLAD2","MS4A7","APOE"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("SPINK2","MTND1P23","AL450405.1","BEX3","HBG2","GUCY1A1","AL513365.1","SMIM24","LAPTM4B","CRHBP"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("HDC","MS4A2","PRG2","MS4A3","TPSAB1","TPSB2","EPX","CLC"),cluster.idents = T) + RotatedAxis()
#DotPlot(cv,features = c("HDC","MS4A2","PRG2","MS4A3","TPSAB1"),group.by = "rna_snn_res.32",cluster.idents = T) + RotatedAxis()
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- "HPSCs"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("11")] <- "HPSCs IFN induced"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("10")] <- "Mast & Eosinophils"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("8")] <- "Doublets"
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_HSPC_annotMeta.rds")
```
```{r annot cycling subcluster,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_cycling.rds")
DefaultAssay(object = cv_subset) <- "RNA"
DimPlot(cv_subset,reduction="wnn.umap_sctAdt",shuffle = T,raster = F,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="wnn.umap_rnaAdt",shuffle = T,raster = F,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_ADT",shuffle = T,raster = F,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "Immune_All_High")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "sct_snn_res.0.5")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "sct_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "sct_snn_res.0.5")
gexOnlyList <- c("CD3D","CCR7","SELL","CD27","CD4","CD40LG","CD8A","GZMH","IL2RA","FOXP3","IKZF2","TRGV9","TRDV2","TRAV1-2","SLC4A10","MKI67","NCR1","NCAM1","FXYD7","FCGR3A","CD14","C1QA","CLEC4C","IL3RA","AXL","SIGLEC6","CLEC9A","FCER1A","FCER2","CXCR5","CD19","CCR6","IGHD","MS4A1","TNFRSF13B","ENTPD1","adt_AB-KIT","adt_AB-CD34","KIT","CD34","PPBP","PF4","HBB")
DotPlot(cv_subset,features = gexOnlyList,group.by = "sct_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "dataset")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Immune_All_High")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
Idents(cv_subset) <- [email protected]$rna_snn_res.0.5
clustMarkers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clustMarkers[order(clustMarkers$pct.2-clustMarkers$pct.1),]
FeaturePlot(cv_subset,dims = c(1,2),reduction = "umapAfterHarmony_RNA",features = c("HBB","IL3RA","CLEC4C","MKI67"))
foo <- FetchData(cv_subset,c("TPSAB1","CPA3","TPSB2","C1QC","EPX","PRG2"))
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(TPSAB1+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(CPA3+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(TPSB2+1))) + geom_point() + theme_classic()
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(C1QC+1))) + geom_point() + theme_classic()
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$TPSAB1)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$CPA3)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$TPSB2)
dplot(x = foo$UMAP_1,y = foo$UMAP_2 ,z = foo$C1QC)
Idents(cv_subset) <- paste([email protected]$rna_snn_res.0.5)
#Idents(cv_subset) <- paste([email protected]$sct_snn_res.0.5)
clust10Markers <- FindMarkers(object = cv_subset,ident.1 = "10",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust1Markers <- FindMarkers(object = cv_subset,ident.1 = "1",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust6Markers <- FindMarkers(object = cv_subset,ident.1 = "6",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust4Markers <- FindMarkers(object = cv_subset,ident.1 = "4",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("TPSAB1","CPA3","KIT","PRSS57","HPGDS","GATA2","TNFSF10","TRIM63","IGHA1","IGHA2","FCER1A"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("SEPP1","AMICA1","GNLY","KIAA1598","IL8","FTL","ALAS2","PTPLAD2","MS4A7","APOE"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("SPINK2","MTND1P23","AL450405.1","BEX3","HBG2","GUCY1A1","AL513365.1","SMIM24","LAPTM4B","CRHBP"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("HDC","MS4A2","PRG2","MS4A3","TPSAB1","TPSB2","EPX","CLC"),cluster.idents = T) + RotatedAxis()
#DotPlot(cv,features = c("HDC","MS4A2","PRG2","MS4A3","TPSAB1"),group.by = "rna_snn_res.32",cluster.idents = T) + RotatedAxis()
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- "Cycling"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("7","12","13")] <- "Doublets"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("9","1")] <- "Plasma cells"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("8")] <- "Plasmablasts"
DotPlot(cv_subset,features = gexOnlyList,group.by = "cell_annot_revision",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_cycling_annotMeta.rds")
```
```{r subset plasmablasts,eval = FALSE}
# Subset plasmablasts to separate isotypes
resolutions <- c(0.5,4)
cv_subset <- subset(cv_subset,cells=rownames([email protected][[email protected]$cell_annot_revision%in%c("Plasma cells","Plasmablasts"),]))
[email protected]$type_sample <- gsub("(..).*","\\1",[email protected]$patient_id)
gc()
cv_subset <- multiModal_processing(object=cv_subset,gex=T,adt=T,sct=T,gexAdtWnn=T,sctAdtWnn=T,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=T,doFreshSct=T)
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_RNA",graph.name="rna_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_ADT",graph.name="adt_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_SCT",graph.name="sct_snn")
cv_subset <- FindClusters(cv_subset, graph.name = "rna_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "adt_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "sct_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("6")] <- "Doublets"
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_plasmas_annotMeta.rds")
cv_subset <- subset(cv_subset,cells=rownames([email protected][[email protected]$rna_snn_res.0.5!="6",])) # These are doublets
cv_subset <- multiModal_processing(object=cv_subset,gex=T,adt=T,sct=T,gexAdtWnn=T,sctAdtWnn=T,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=T,doFreshSct=T)
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_RNA",graph.name="rna_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_ADT",graph.name="adt_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_SCT",graph.name="sct_snn")
cv_subset <- FindClusters(cv_subset, graph.name = "rna_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "adt_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "sct_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
#write_rds(cv_subset,file="farm/cov_oldNewMerged_filtered_badAdtRem.subset_plasmas.rds",compress="gz")
DefaultAssay(cv_subset) <- "RNA"
Idents(cv_subset) <- [email protected]$rna_snn_res.0.5
clust05Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
Idents(cv_subset) <- [email protected]$rna_snn_res.4
clust4Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust05Markers <- clust05Markers[order(clust05Markers$pct.2-clust05Markers$pct.1),]
clust4Markers <- clust4Markers[order(clust4Markers$pct.2-clust4Markers$pct.1),]
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,group.by = "projected_annot_rik",cols = myColsForCellTypes)
DimPlot(cv_subset,reduction="umapAfterHarmony_ADT",shuffle = T,raster = F,group.by = "projected_annot_rik",cols = myColsForCellTypes)
DimPlot(cv_subset,reduction="wnn.umap_rnaAdt",shuffle = T,raster = F,group.by = "projected_annot_rik",cols = myColsForCellTypes)
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "dataset")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "projected_annot_rik",cols = myColsForCellTypes)
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Immune_All_Low")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Immune_Blood_Low")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "orig.ident") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "patient_id") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
markerList <- c("NKG7",'NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D',"HLA-DRA","S100A4","S100A6","CCL4","CCL5","GZMH","GZMB","GZMK","IL32","IFNG","IFI6","IRF7","IFIT3","PRF1","FCGR3A","adt_AB-FCGR3A","FCER1G","rnaTr","adt_AB-CD8A","CD8A","CD8B","adt_AB-CD4","NCR1","adt_AB-NCR1","PPBP","PF4","KLRB1","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9","IL7R","KLRC3","GNLY","CD27","adt_AB-CD27","SELL","TIGIT","CXCR4","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-2","adt_AB-PTPRC-3")
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = c("IFNG","TBX21","TNF","GATA3","IL4","IL5","RORC","IL17A","IL17F","IL21","CCL5","PHLDA1","LYAR","ODF2L","IL7R","PDE4D"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("IFNG","TBX21","TNF","GATA3","IL4","IL5","RORC","IL17A","IL17F","IL21","CCL5","PHLDA1","LYAR","ODF2L","IL7R","PDE4D"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
# Not really any clustering of isotypes
```
```{r annot tnk subcluster,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_TNK.rds")
DefaultAssay(object = cv_subset) <- "RNA"
DimPlot(cv_subset,reduction="wnn.umap_sctAdt",shuffle = T,raster = F,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_ADT",shuffle = T,raster = F,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="wnn.umap_rnaAdt",shuffle = T,raster = F,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "projected_annot_rik")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Immune_All_High")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "adt_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "adt_snn_res.0.5")
gexOnlyList <- c("CD3D","CCR7","SELL","CD27","CD4","CD40LG","CD8A","GZMH","IL2RA","FOXP3","IKZF2","TRGV9","TRDV2","TRAV1-2","SLC4A10","MKI67","NCR1","NCAM1","FXYD7","FCGR3A","CD14","C1QA","CLEC4C","IL3RA","AXL","SIGLEC6","CLEC9A","FCER1A","FCER2","CXCR5","CD19","CCR6","IGHD","MS4A1","TNFRSF13B","ENTPD1","KIT","CD34","PPBP","PF4","HBB")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "dataset") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "projected_annot_rik",cols = myColsForCellTypes) + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Immune_All_Low") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.32") + NoLegend()
#markerList <- c("CD8A","CD8B","CCR7","GZMB",paste0("adt_AB-",c("PTPRC-2","PTPRC-3","CD4","CD8A")))
markerList <- c('NCR1','NCAM1','FCGR3A','FCER1G','adt_AB-CD8A')
markerList <- c('NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D')
markerList <- c('KLRB1','CD3G','FGFBP2')
markerList <- c("FOXP3","IL2RA","CTLA4")
markerList <- c('GNLY','NKG7','GZMK')
#markerList <- c('GNLY','NKG7','CD3D')
# g/d t cells and mait
markerList <- c("SLC4A10", "TRAV1-2", "TRBV6-2","adt_AB-TRAV7")
markerList <- c("TRDV1","TRDV2","TRGV9","TRDC","TRGC1","TRGC2",paste0("adt_AB-",c("TRAV24","TRAV7","TRBV13","TRGV9","TRDV2")))
DotPlot(cv_subset,features = markerList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5") + NoLegend()
DotPlot(cv_subset,features = markerList,group.by = "rna_snn_res.4",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4") + NoLegend()
foo <- FetchData(cv_subset,markerList)
foo$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
for (i in markerList) {print(ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(get(i)+1))) + geom_point(cex=.1) + theme_classic() + ggtitle(i))}
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(rnaTr+1))) + geom_point(cex=.1) + theme_classic() + ggtitle("TR RNA")
#CTLS
markerList <- c("CD8A","CD8B","CCR7","GZMB","GZMH",paste0("adt_AB-",c("PTPRC-2","PTPRC-3","CD4","CD8A")))
#DotPlot(cv_subset,features = markerList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
#DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5") + NoLegend()
DotPlot(cv_subset,features = markerList,group.by = "rna_snn_res.4",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4") + NoLegend()
foo <- FetchData(cv_subset,c(markerList,"nCount_RNA"))
foo$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
for (i in markerList) {print(ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(get(i)+1))) + geom_point(cex=.1) + theme_classic() + ggtitle(i))}
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(rnaTr+1))) + geom_point(cex=.1) + theme_classic() + ggtitle("TR RNA")
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=nCount_RNA)) + geom_point(cex=.1) + theme_classic() + ggtitle("nCount")
for (i in markerList) {print(dplot(x=foo$UMAP_1,y=foo$UMAP_2,z=foo[,i]));title(i)}
Idents(cv_subset) <- paste([email protected]$rna_snn_res.0.5)
clust12Markers <- FindMarkers(object = cv_subset,ident.1 = "1",ident.2 = "4",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F)
clust12MarkersAdt <- FindMarkers(object = cv_subset,ident.1 = "12",ident.2 = "3",assay = "ADT",logfc.threshold = .5,min.pct = .25,only.pos = F)
clust1Markers <- FindMarkers(object = cv_subset,ident.1 = "1",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("6")] <- "g/d T"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("1")] <- "CD4 CTL"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("8")] <- "MAIT"
#[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("1")] <- "AS-DC"
#[email protected]$cell_annot_revision[is.na([email protected]$cell_annot_revision)] <- "Doublets"
```
```{r sub-subcluster the tnk cluster,eval = FALSE}
[email protected]$subsubcluster <- "subT"
[email protected]$subsubcluster[[email protected]$rna_snn_res.0.5%in%c("4")] <- "subTNK"
[email protected]$subsubcluster[[email protected]$rna_snn_res.0.5%in%c("1","6")] <- "subNK"
cv <- cv_subset
for (i in unique([email protected]$subsubcluster)) {
if (!file.exists(paste0("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_",i,".rds"))) {
cv_subset <- subset(cv,cells=rownames([email protected][[email protected]$subsubcluster==i,]))
gc()
cv_subset <- multiModal_processing(object=cv_subset,gex=T,adt=T,sct=T,gexAdtWnn=T,sctAdtWnn=T,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=T,doFreshSct=T)
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_RNA",graph.name="rna_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_ADT",graph.name="adt_snn")
cv_subset <- FindNeighbors(cv_subset, dims = 1:30,reduction = "harmony_SCT",graph.name="sct_snn")
cv_subset <- FindClusters(cv_subset, graph.name = "rna_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "adt_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
cv_subset <- FindClusters(cv_subset, graph.name = "sct_snn", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
#cv_subset <- FindClusters(cv_subset, graph.name = "wsnn_rnaAdt", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
#cv_subset <- FindClusters(cv_subset, graph.name = "wsnn_sctAdt", resolution = resolutions,algorithm = 4, method = "igraph", verbose = FALSE)
#cv_subset <- combineSmallWnnClusters(object=cv_subset,resolutions=resolutions,graphNames=c("wsnn_rnaAdt","wsnn_sctAdt"),minClusterSize=100)
write_rds(cv_subset,file=paste0("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_",i,".rds"),compress="gz")
}
}
```
```{r annot subsubcluster w NKs,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_subNK.rds")
[email protected]$type_sample <- gsub("(..).*","\\1",[email protected]$patient_id)
[email protected]$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "sct_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "orig.ident") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "patient_id") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
# markerList <- c('NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D')
#
# foo <- FetchData(cv_subset,c(markerList,"nCount_RNA"))
# foo$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
# foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
# for (i in markerList) {print(ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(get(i)+1))) + geom_point(cex=.1) + theme_classic() + ggtitle(i))}
# ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(rnaTr+1))) + geom_point(cex=.1) + theme_classic() + ggtitle("TR RNA")
# ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=nCount_RNA)) + geom_point(cex=.1) + theme_classic() + ggtitle("nCount")
# for (i in markerList) {print(dplot(x=foo$UMAP_1,y=foo$UMAP_2,z=foo[,i]));title(i)}
# Idents(cv_subset) <- paste([email protected]$rna_snn_res.32)
# clust385Markers <- FindMarkers(object = cv_subset,ident.1 = "385",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F)
# Idents(cv_subset) <- paste([email protected]$rna_snn_res.0.5)
# clust2Markers <- FindMarkers(object = cv_subset,ident.1 = "2",ident.2 = "1",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) # S100A4/6 high FCGR3A high CCL4/5 high GZMH/B high
# Idents(cv_subset) <- paste([email protected]$sct_snn_res.4)
# clust20Markers <- FindMarkers(object = cv_subset,ident.1 = "20",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) # IFN activated
# clust45Markers <- FindMarkers(object = cv_subset,ident.1 = "45",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) # cycling
# clust29Markers <- FindMarkers(object = cv_subset,ident.1 = "29",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) # AP12 specific AP1-complex up
# clust41Markers <- FindMarkers(object = cv_subset,ident.1 = "41",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) # IL7R expressing
# clust38Markers <- FindMarkers(object = cv_subset,ident.1 = "38",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) # HLA-DR expressing
# clust9Markers <- FindMarkers(object = cv_subset,ident.1 = "9",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) #
# clust21Markers <- FindMarkers(object = cv_subset,ident.1 = "21",assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = F) #
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "sct_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "sct_snn_res.4")
markerList <- c("NKG7",'NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D',"HLA-DRA","S100A4","S100A6","CCL4","CCL5","GZMH","GZMB","IL32","IFNG","IFI6","IRF7","IFIT3","PRF1","FCGR3A","adt_AB-FCGR3A","FCER1G","rnaTr","adt_AB-CD8A","CD8A","CD8B","adt_AB-CD4","NCR1","adt_AB-NCR1","KLRD1","adt_AB-KLRD1","TRAV24","adt_AB-TRAV24")
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,group.by = "sct_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,group.by = "sct_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis()
foo <- FetchData(cv_subset,c(markerList,"nCount_RNA"))
foo$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
#foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
for (i in markerList) {print(ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(get(i)+1))) + geom_point(cex=.1) + theme_classic() + ggtitle(i))}
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(rnaTr+1))) + geom_point(cex=.1) + theme_classic() + ggtitle("TR RNA")
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=nCount_RNA)) + geom_point(cex=.1) + theme_classic() + ggtitle("nCount")
for (i in markerList) {print(dplot(x=foo$UMAP_1,y=foo$UMAP_2,z=foo[,i]));title(i)}
```
``` {r annotate,eval = FALSE}
cv_subset_nk <- cv_subset
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- "NK FCER1G+"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("2")] <- "NK"
[email protected]$cell_annot_revision[[email protected]$sct_snn_res.0.5%in%c("4")] <- "NK CD56 bright"
[email protected]$cell_annot_revision[[email protected]$sct_snn_res.4%in%c("33")] <- "NKT"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("36")] <- "cycling"
[email protected]$cell_annot_revision[[email protected]$sct_snn_res.4%in%c("38")] <- "NK HLA-DR+"
[email protected]$cell_annot_revision[[email protected]$sct_snn_res.4%in%c("20")] <- "NK IFN induced"
[email protected]$cell_annot_revision[[email protected]$sct_snn_res.4%in%c("41")] <- "ILC"
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_subNK_annotMeta.rds")
DimPlot(cv_subset_nk,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
DimPlot(cv_subset_nk,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
DotPlot(cv_subset_nk,group.by = "cell_annot_revision",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
```
```{r annot subsubcluster w TNKs,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_subTNK.rds")
[email protected]$type_sample <- gsub("(..).*","\\1",[email protected]$patient_id)
[email protected]$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "sct_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "orig.ident") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "patient_id") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
markerList <- c("NKG7",'NCAM1','adt_AB-NCAM1',"CD3G",'CD3D','adt_AB-CD3D',"HLA-DRA","CCL4","CCL5","GZMH","GZMB","IL32","IFNG","IFI6","IRF7","IFI44L","PRF1","FCGR3A","adt_AB-FCGR3A","FCER1G","rnaTr","adt_AB-CD8A","CD8A","CD8B","adt_AB-CD4","NCR1","adt_AB-NCR1","KLRD1","adt_AB-KLRD1","TRAV24","adt_AB-TRAV24","TRDV2","TRGV9","IL7R","SELL","MX1")
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
#DotPlot(cv_subset,group.by = "sct_snn_res.0.5",features = markerList,cluster.idents = T,) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
#DotPlot(cv_subset,group.by = "sct_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(subset(cv_subset,cells=rownames([email protected][[email protected]$rna_snn_res.4%in%c("3","6","8","9","19","1","30","7","27","20","22","26","34"),])),group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DimPlot(subset(cv_subset,cells=rownames([email protected][[email protected]$rna_snn_res.4%in%c("3","6","8","9","19","1","30","7","27","20","22","26","34"),])),reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
foo <- FetchData(cv_subset,c(markerList,"nCount_RNA"))
foo$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
#foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
for (i in markerList) {print(ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(get(i)+1))) + geom_point(cex=.1) + theme_classic() + ggtitle(i))}
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(rnaTr+1))) + geom_point(cex=.1) + theme_classic() + ggtitle("TR RNA")
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log10(nCount_RNA))) + geom_point(cex=.1) + theme_classic() + ggtitle("nCount")
for (i in markerList) {print(dplot(x=foo$UMAP_1,y=foo$UMAP_2,z=foo[,i]));title(i)}
cv_subset_tnk <- cv_subset
```
```{r annotate TNK,eval = FALSE}
DimPlot(cv_subset_tnk,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,group.by = "type_sample")
Idents(cv_subset_tnk) <- [email protected]$rna_snn_res.4
clustMarkers <- FindAllMarkers(object = cv_subset_tnk,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clustMarkers[order(clustMarkers$pct.2-clustMarkers$pct.1),]
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- as.character([email protected]$rna_snn_res.4)
[email protected]$cell_annot_revision <- "T CD8 CTL"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("25","11","10","33")] <- "T CD8 EM"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("13")] <- "NK CD56 bright"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("23")] <- "NK IFN induced"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("32")] <- "T CD8 CTL IFN induced"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("5")] <- "T g/d"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("31")] <- "T CD4 CTL"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("1","9","3")] <- "NK FCER1G+"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("6","8","19","30","7","27","20","22","34")] <- "NK"
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_subTNK_annotMeta.rds")
DimPlot(cv_subset_tnk,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
DimPlot(cv_subset_tnk,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$cell_annot_revision))+2))
DotPlot(cv_subset_tnk,group.by = "cell_annot_revision",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
```
```{r annot subsubcluster T,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_subT.rds")
[email protected]$type_sample <- gsub("(..).*","\\1",[email protected]$patient_id)
[email protected]$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
Idents(cv_subset) <- [email protected]$rna_snn_res.0.5
clust05Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
Idents(cv_subset) <- [email protected]$rna_snn_res.4
clust4Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust05Markers[order(clust05Markers$pct.2-clust05Markers$pct.1),]
clust4Markers[order(clust4Markers$pct.2-clust4Markers$pct.1),]
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "orig.ident") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "patient_id") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
markerList <- c("NKG7",'NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D',"HLA-DRA","S100A4","S100A6","CCL4","CCL5","GZMH","GZMB","GZMK","IL32","IFNG","IFI6","IRF7","IFIT3","PRF1","FCGR3A","adt_AB-FCGR3A","FCER1G","rnaTr","adt_AB-CD8A","CD8A","CD8B","adt_AB-CD4","NCR1","adt_AB-NCR1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9","IL7R","KLRC3","GNLY","CD27","adt_AB-CD27","SELL","TIGIT","CXCR4","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-2","adt_AB-PTPRC-3","SLC4A10", "NCR3", "KLRB1")
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
foo <- FetchData(cv_subset,c(markerList,"nCount_RNA"))
foo$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
#foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
foo[,c("UMAP_1","UMAP_2")] <- as.data.frame(cv_subset@[email protected])
for (i in markerList) {print(ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(get(i)+1))) + geom_point(cex=.1) + theme_classic() + ggtitle(i))}
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=log2(rnaTr+1))) + geom_point(cex=.1) + theme_classic() + ggtitle("TR RNA")
ggplot(foo[sample(1:nrow(foo)),],aes(UMAP_1,UMAP_2,col=nCount_RNA)) + geom_point(cex=.1) + theme_classic() + ggtitle("nCount")
for (i in markerList) {print(dplot(x=foo$UMAP_1,y=foo$UMAP_2,z=foo[,i]));title(i)}
cv_subset_t <- cv_subset
#tem: CCR7lo SELLlo CX3CR1hi CD27lo IL7Rhi CD27- CD45RA- "30","1",
#tcm: CCR7hi SELLhi CX3CR1lo CD27hi IL7Rhi CD27+ CD45RA- "35","36","17","7","15","25","19"
#temra: CCR7- IL7Rlo CD27- CD45RA+ "5","3","24",
```
```{r annotate T cells,eval = FALSE}
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- "T CD8 CTL"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("30")] <- "T CD8 em"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("1")] <- "MAIT"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("35","36","17","7","15","25","19","6")] <- "T CD8 cm"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("5","3","24")] <- "T CD8 emra"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("4","11")] <- "T g/d"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("34","8","9")] <- "T CD4 CTL"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("33")] <- "Doublets"
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_subT_annotMaitMeta.rds")
DotPlot(cv_subset,group.by = "cell_annot_revision",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","GZMH","PRF1","SLC4A10", "NCR3", "KLRB1"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","GZMH","PRF1","SLC4A10", "NCR3", "KLRB1"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
```
``` {r annot T compartment (naive etc),eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_T.rds")
[email protected]$type_sample <- gsub("(..).*","\\1",[email protected]$patient_id)
[email protected]$rnaTr <- rowSums(FetchData(cv_subset,rownames(cv_subset[["RNA"]])[grep("^TR.*V[0-9].*",rownames(cv_subset[["RNA"]]))]))
Idents(cv_subset) <- [email protected]$rna_snn_res.0.5
clust05Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
Idents(cv_subset) <- [email protected]$rna_snn_res.4
clust4Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
Idents(cv_subset) <- [email protected]$rna_snn_res.32
clust32Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust05Markers[order(clust05Markers$pct.2-clust05Markers$pct.1),]
clust4Markers[order(clust4Markers$pct.2-clust4Markers$pct.1),]
clust32Markers[order(clust32Markers$pct.2-clust32Markers$pct.1),]
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "orig.ident") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "patient_id") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
markerList <- c("NKG7",'NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D',"HLA-DRA","S100A4","S100A6","CCL4","CCL5","GZMH","GZMB","GZMK","IL32","IFNG","IFI6","IRF7","IFIT3","PRF1","FCGR3A","adt_AB-FCGR3A","FCER1G","rnaTr","adt_AB-CD8A","CD8A","CD8B","adt_AB-CD4","NCR1","adt_AB-NCR1","PPBP","PF4","KLRB1","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9","IL7R","KLRC3","GNLY","CD27","adt_AB-CD27","SELL","TIGIT","CXCR4","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-2","adt_AB-PTPRC-3")
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = c("IFNG","TBX21","TNF","GATA3","IL4","IL5","RORC","IL17A","IL17F","IL21","CCL5","PHLDA1","LYAR","ODF2L","IL7R","PDE4D"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("IFNG","TBX21","TNF","GATA3","IL4","IL5","RORC","IL17A","IL17F","IL21","CCL5","PHLDA1","LYAR","ODF2L","IL7R","PDE4D"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
#Fairly shallow
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("4")] <- "T low quality"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("5")] <- "T regulatory"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("6")] <- "T CD4 IFN induced"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("1")] <- "T CD4 Naive"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("2")] <- "T CD4 Helper"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("3")] <- "T CD8 Naive"
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_T_annotMeta.rds")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
```
```{r look at monoDC compartment,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_MonoDCs.rds")
[email protected]$type_sample <- gsub("(..).*","\\1",[email protected]$patient_id)
Idents(cv_subset) <- [email protected]$rna_snn_res.0.5
clust05Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
Idents(cv_subset) <- [email protected]$rna_snn_res.4
clust4Markers <- FindAllMarkers(object = cv_subset,assay = "RNA",logfc.threshold = .5,min.pct = .25,only.pos = T)
clust05Markers <- clust05Markers[order(clust05Markers$pct.2-clust05Markers$pct.1),]
clust4Markers <- clust4Markers[order(clust4Markers$pct.2-clust4Markers$pct.1),]
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "orig.ident") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "patient_id") + NoLegend()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "type_sample",cols = randomcoloR::distinctColorPalette(length(unique([email protected]$type_sample))+2))
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DotPlot(cv_subset, features = c('CD14','FCGR3A','C1QA','CLEC9A','CLEC10A','CD1C','PTPRC','PPBP','PF4','MS4A1','NEAT1',"IFI6","IRF7","IFI44L"), group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset, features = c('CD14','FCGR3A','C1QA','CLEC9A','CLEC10A','CD1C','PTPRC','PPBP','PF4','MS4A1','NEAT1',"IFI6","IRF7","IFI44L"), group.by = "rna_snn_res.4",cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
markerList <- c("NKG7",'NCAM1','adt_AB-NCAM1','CD3D','adt_AB-CD3D',"HLA-DRA","S100A4","S100A6","CCL4","CCL5","GZMH","GZMB","GZMK","IL32","IFNG","IFI6","IRF7","IFIT3","PRF1","FCGR3A","adt_AB-FCGR3A","FCER1G","rnaTr","adt_AB-CD8A","CD8A","CD8B","adt_AB-CD4","NCR1","adt_AB-NCR1","PPBP","PF4","KLRB1","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9","IL7R","KLRC3","GNLY","CD27","adt_AB-CD27","SELL","TIGIT","CXCR4","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-2","adt_AB-PTPRC-3")
gexOnlyList <- c("CD3D","CCR7","SELL","CD27","CD4","CD40LG","CD8A","GZMH","IL2RA","FOXP3","IKZF2","TRGV9","TRDV2","TRAV1-2","SLC4A10","MKI67","NCR1","NCAM1","FXYD7","FCGR3A","CD14","C1QA","CLEC4C","IL3RA","AXL","SIGLEC6","CLEC9A","FCER1A","FCER2","CXCR5","CD19","CCR6","IGHD","MS4A1","TNFRSF13B","ENTPD1","KIT","CD34","PPBP","PF4","HBB","HDC","MS4A2","PRG2","MS4A3","TPSAB1","TPSB2","EPX","CLC","IFI6","IRF7","IFI44L")
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = markerList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = gexOnlyList,cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("adt_AB-PTPRC-2","adt_AB-CD4","adt_AB-CD8A","IL7R","CD27","CCR7","SELL","CX3CR1","adt_AB-CX3CR1","adt_AB-PTPRC-1","adt_AB-PTPRC-3","GZMH","PRF1","PPBP","PF4","TRDV2","adt_AB-TRDV2","TRGV9","adt_AB-TRGV9"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.0.5",features = c("IL6","GPBAR1","CXCL10","IFNG","TBX21","TNF","GATA3","IL4","IL5","RORC","IL17A","IL17F","IL21","CCL5","PHLDA1","LYAR","ODF2L","IL7R","PDE4D"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset,group.by = "rna_snn_res.4",features = c("IL6","GPBAR1","CXCL10","IFNG","TBX21","TNF","GATA3","IL4","IL5","RORC","IL17A","IL17F","IL21","CCL5","PHLDA1","LYAR","ODF2L","IL7R","PDE4D"),cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- "Monocyte CD14"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("36","24","16","5")] <- "Monocyte CD14 IFN-induced"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("39")] <- "Monocyte CD14 IL6+"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("41","1","47","45","6","26")] <- "Monocyte CD16"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("32")] <- "Monocyte CD16 IFN-induced"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("30")] <- "Monocyte CD16+C1"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("48","22","43","38")] <- "doublets"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("49")] <- "cDC1"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.4%in%c("3")] <- "cDC2"
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.4")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
DotPlot(cv_subset, features = c("IL6","GPBAR1","CXCL10",'CD14','FCGR3A','C1QA','CLEC9A','CLEC10A','CD1C','PTPRC','PPBP','PF4','MS4A1','NEAT1',"IFI6","IRF7","IFI44L"), group.by = "rna_snn_res.4",cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
DotPlot(cv_subset, features = c("IL6","GPBAR1","CXCL10",'CD14','FCGR3A','C1QA','CLEC9A','CLEC10A','CD1C','PTPRC','PPBP','PF4','MS4A1','NEAT1',"IFI6","IRF7","IFI44L"), group.by = "cell_annot_revision",cluster.idents = T) + RotatedAxis() + theme(axis.text=element_text(size=7))
#write_rds([email protected],file = "/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_MonoDCs_annotMeta.rds")
cv_subset <- subset(cv_subset,cells=rownames([email protected])[grepl("Monocyte",[email protected]$cell_annot_revision)])
cv_subset <- multiModal_processing(object=cv_subset,gex=T,adt=F,sct=F,gexAdtWnn=F,sctAdtWnn=F,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=F,doFreshSct=F)
cv_subset <- multiModal_processing(object=cv_subset,gex=F,adt=F,sct=T,gexAdtWnn=F,sctAdtWnn=F,doHarmony=T,npca=30,regress_cellcycle_gex=F,makeFinalWnnUmap=F,doFreshSct=T)
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
DimPlot(cv_subset,reduction="umapAfterHarmony_SCT",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")
```
```{r look at platelet compartment,eval = FALSE}
cv_subset <- read_rds("/mnt/projects/RL003_allCitePbmcsTheta/dataRevision/cov_oldNewMerged_filtered_badAdtRem.subset_platelets.rds")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = c("log_prob_doublet","log_prob_singleton"),group.by = "sct_snn_res.0.5")
DotPlot(cv_subset,features = c("pANN","log_prob_doublet","log_prob_singleton"),group.by = "sct_snn_res.0.5")
gexOnlyList <- c("CD3D","CCR7","SELL","CD27","CD4","CD40LG","CD8A","GZMH","IL2RA","FOXP3","IKZF2","TRGV9","TRDV2","TRAV1-2","SLC4A10","MKI67","NCR1","NCAM1","FXYD7","FCGR3A","CD14","C1QA","CLEC4C","IL3RA","AXL","SIGLEC6","CLEC9A","FCER1A","FCER2","CXCR5","CD19","CCR6","IGHD","MS4A1","TNFRSF13B","ENTPD1","adt_AB-KIT","adt_AB-CD34","KIT","CD34","PPBP","PF4","HBB")
DotPlot(cv_subset,features = gexOnlyList,group.by = "sct_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "dataset")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "Phase")
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "rna_snn_res.0.5")
DotPlot(cv_subset,features = gexOnlyList,group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("TPSAB1","CPA3","KIT","PRSS57","HPGDS","GATA2","TNFSF10","TRIM63","IGHA1","IGHA2","FCER1A"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("SEPP1","AMICA1","GNLY","KIAA1598","IL8","FTL","ALAS2","PTPLAD2","MS4A7","APOE"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("SPINK2","MTND1P23","AL450405.1","BEX3","HBG2","GUCY1A1","AL513365.1","SMIM24","LAPTM4B","CRHBP"),group.by = "rna_snn_res.0.5",cluster.idents = T) + RotatedAxis()
DotPlot(cv_subset,features = c("HDC","MS4A2","PRG2","MS4A3","TPSAB1","TPSB2","EPX","CLC"),cluster.idents = T) + RotatedAxis()
#DotPlot(cv,features = c("HDC","MS4A2","PRG2","MS4A3","TPSAB1"),group.by = "rna_snn_res.32",cluster.idents = T) + RotatedAxis()
[email protected]$cell_annot_revision <- NA
[email protected]$cell_annot_revision <- "Doublets"
[email protected]$cell_annot_revision[[email protected]$rna_snn_res.0.5%in%c("7","1","3")] <- "Platelets"
DotPlot(cv_subset,features = gexOnlyList,group.by = "cell_annot_revision",cluster.idents = T) + RotatedAxis()
DimPlot(cv_subset,reduction="umapAfterHarmony_RNA",shuffle = T,raster = F,label=T,group.by = "cell_annot_revision")