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I’ve been using it to annotate scRNAseq datasets from purified Multiple Myeloma samples, in order to identify and remove cells that were not properly "purified-out" before sequencing. I was originally using the downsampled 2020 (preprint) version of the TICA and SingleR and got results that made sense (90-99% of the cells were B cells, Plasma B cells or Proliferative B cells depending on the sample of interest). However, when I tried to update and use the final version of the TICAtlas, the annotation stopped making any sense (see example below).
2020 version:
class
counts
proportion
B cells
8
0.0018497
Plasma B cells
4312
0.9969942
Proliferative B cells
2
0.0004624
Th17 cells
3
0.0006936
Same sample annotated with the 2021 version:
class
counts
proportion
B cells
49
0.0113295
B cells proliferative
4
0.0009249
CD4 effector memory
11
0.0025434
CD4 naive-memory
16
0.0036994
CD4 recently activated
633
0.1463584
CD4 transitional memory
16
0.0036994
CD8 cytotoxic
342
0.0790751
CD8 effector memory
226
0.0522543
CD8 pre-exhausted
30
0.0069364
CD8 terminally exhausted
77
0.0178035
Macro. and mono. prolif.
2
0.0004624
Macrophages SPP1
381
0.0880925
Mast cells
2294
0.5304046
mDC
11
0.0025434
Monocytes
36
0.0083237
NK
1
0.0002312
Plasma B cells
42
0.0097110
T cells naive
37
0.0085549
T cells proliferative
6
0.0013873
T cells regulatory
46
0.0106358
T helper cells
17
0.0039306
TAMs C1QC
46
0.0106358
TAMs proinflamatory
2
0.0004624
I’m not sure what I’m doing wrong. Interestingly, when I try to annotate the 2021 object using the 2020 object as reference, I get the following result:
class
counts
proportion
B cells
128
0.0512
cDC
16
0.0064
Cytotoxic CD8 T cells
73
0.0292
Effector memory CD8 T cells
10
0.0040
M2 TAMs
143
0.0572
mDC
192
0.0768
Monocytes
58
0.0232
Naive T cells
739
0.2956
pDC
20
0.0080
Plasma B cells
62
0.0248
Proinflamatory TAMs
3
0.0012
Proliferative B cells
25
0.0100
Proliferative monocytes and macrophages
53
0.0212
Proliferative T cells
110
0.0440
Regulatory T cells
126
0.0504
T helper cells
713
0.2852
Terminally exhausted CD8 T cells
3
0.0012
Th17 cells
25
0.0100
Transitional memory CD4 T cells
1
0.0004
...even though I have initially 100 cells of each subtype. Same results when annotating the 2020 object using 2021 as reference:
class
counts
proportion
B cells
72
0.0028993
B cells proliferative
758
0.0305227
CD4 effector memory
262
0.0105501
CD4 naive-memory
680
0.0273818
CD4 recently activated
2842
0.1144399
CD4 transitional memory
532
0.0214222
CD8 cytotoxic
178
0.0071676
CD8 effector memory
35
0.0014094
CD8 pre-exhausted
18
0.0007248
CD8 terminally exhausted
76
0.0030603
cDC
43
0.0017315
Macro. and mono. prolif.
451
0.0181606
Macrophages SPP1
11907
0.4794636
Mast cells
5391
0.2170814
mDC
72
0.0028993
Monocytes
317
0.0127648
NK
52
0.0020939
pDC
21
0.0008456
Plasma B cells
63
0.0025368
T cells naive
147
0.0059193
T cells proliferative
41
0.0016510
T cells regulatory
341
0.0137312
T helper cells
313
0.0126037
TAMs C1QC
133
0.0053556
TAMs proinflamatory
89
0.0035838
(expected: 1000 cells of each subtype)
I also tried to annotate my samples with the full 2021 Atlas (takes forever...) but the problem stays the same, so it does not seem to be linked to the fact that the new downsampled version contains 10x less cells. Furthermore, the huge discrepancies when annotating one Atlas with the other is rather suspicious.
Any idea of what’s happening?
Cheers,
Nils
The text was updated successfully, but these errors were encountered:
Hi and thank you for providing TICA.
I’ve been using it to annotate scRNAseq datasets from purified Multiple Myeloma samples, in order to identify and remove cells that were not properly "purified-out" before sequencing. I was originally using the downsampled 2020 (preprint) version of the TICA and SingleR and got results that made sense (90-99% of the cells were B cells, Plasma B cells or Proliferative B cells depending on the sample of interest). However, when I tried to update and use the final version of the TICAtlas, the annotation stopped making any sense (see example below).
2020 version:
Same sample annotated with the 2021 version:
I’m not sure what I’m doing wrong. Interestingly, when I try to annotate the 2021 object using the 2020 object as reference, I get the following result:
...even though I have initially 100 cells of each subtype. Same results when annotating the 2020 object using 2021 as reference:
(expected: 1000 cells of each subtype)
I also tried to annotate my samples with the full 2021 Atlas (takes forever...) but the problem stays the same, so it does not seem to be linked to the fact that the new downsampled version contains 10x less cells. Furthermore, the huge discrepancies when annotating one Atlas with the other is rather suspicious.
Any idea of what’s happening?
Cheers,
Nils
The text was updated successfully, but these errors were encountered: