Best approach for identifying top proteins in a single cluster in single-cell analysis
1
I have followed the Seurat workflow for clustering and successfully identified the individual clusters based on gene expression. Now, I want to identify and rank the top 10 highly expressed proteins within a single cluster from a set of 100 proteins. What would be the most appropriate test to perform in this case? Would calculating the average expression be sufficient, or is there a better approach? Please note, I am not comparing expression across clusters (e.g. cluster 1 vs. cluster 2), as in differential expression analysis.
ADT
CITEseq
Single-cell
Seurat
• 386 views
For ranking the top 10 highly expressed proteins within a single cluster without performing differential expression analysis across clusters, calculating the average expression of each protein in that cluster would be appropriate. Furthermore, you can add robustness by considering factors like the percentage of cells expressing the protein and variability across the cluster.
#for an instance these 10 proteins in which I m interested in:
my_proteins <- c("RAB31","DYNLT1","CD6","SLC16A3","TCEA3","CAST","CAP1","ALOX5","NOG","IFI35")
#you can simply use DotPlot function of Seurat to find the Average Expression (avg.exp) and Percent Expressed (pct.exp)
x <- DotPlot(object = pbmc, features = my_proteins)
x$data
avg.exp pct.exp features.plot id avg.exp.scaled
RAB31 0.000000000 0.0000000 RAB31 0 -0.859246147
DYNLT1 0.542604963 9.6491228 DYNLT1 0 -0.589111268
CD6 0.929876534 18.2748538 CD6 0 1.382608015
SLC16A3 0.052566628 1.1695906 SLC16A3 0 -0.789887207
TCEA3 0.495651342 8.4795322 TCEA3 0 2.250951593
CAST 0.350948031 7.7485380 CAST 0 -1.157371681
CAP1 1.687091107 30.4093567 CAP1 0 -1.575507894
ALOX5 0.123738798 2.0467836 ALOX5 0 -0.600642706
NOG 0.208829681 3.5087719 NOG 0 2.500000000
IFI35 0.462932098 9.0643275 IFI35 0 -0.739214199
RAB311 1.030589576 18.0873181 RAB31 1 1.665462561
DYNLT11 2.283880667 38.4615385 DYNLT1 1 1.594728330
CD61 0.065054977 1.6632017 CD6 1 -0.813184820
SLC16A31 0.959112032 16.0083160 SLC16A3 1 1.141282978
TCEA31 0.056845331 1.4553015 TCEA3 1 -0.388361263
CAST1 1.255305484 25.9875260 CAST 1 1.059962999
CAP11 2.446626408 37.8378378 CAP1 1 -0.627471139
ALOX51 0.810028214 17.2557173 ALOX5 1 1.063510075
NOG1 0.010729295 0.4158004 NOG 1 -0.324100527
IFI351 1.880683835 28.8981289 IFI35 1 1.242460588
RAB312 0.006360401 0.2100840 RAB31 2 -0.836647369
DYNLT12 1.193551089 15.7563025 DYNLT1 2 0.428446295
CD62 0.836573034 19.1176471 CD6 2 1.199555557
SLC16A32 0.129271765 2.9411765 SLC16A3 2 -0.571232804
TCEA32 0.242413217 6.5126050 TCEA3 2 0.841086164
CAST2 0.741097766 18.0672269 CAST 2 -0.059651710
CAP12 2.361364958 43.6974790 CAP1 2 -0.722865505
ALOX52 0.108799320 2.9411765 ALOX5 2 -0.647366519
NOG2 0.012720022 0.4201681 NOG 2 -0.293027709
IFI352 1.059002174 16.5966387 IFI35 2 0.260356898
RAB313 0.070882242 1.4534884 RAB31 3 -0.615150685
DYNLT13 0.336271756 5.8139535 DYNLT1 3 -1.004138846
CD63 0.111832147 2.6162791 CD6 3 -0.654408470
SLC16A33 0.065569041 1.1627907 SLC16A3 3 -0.751723286
TCEA33 0.000000000 0.0000000 TCEA3 3 -0.808558406
CAST3 0.688991895 12.2093023 CAST 3 -0.191113611
CAP13 2.374941021 34.8837209 CAP1 3 -0.707515132
ALOX53 0.919568650 16.5697674 ALOX5 3 1.268641769
NOG3 0.000000000 0.0000000 NOG 3 -0.492632666
IFI353 0.988544579 13.3720930 IFI35 3 0.158526274
RAB314 0.018124610 0.3436426 RAB31 4 -0.795222508
DYNLT14 0.844044568 15.4639175 DYNLT1 4 -0.073210098
CD64 0.809035275 14.0893471 CD6 4 1.143748674
SLC16A34 0.104128222 2.0618557 SLC16A3 4 -0.641225973
TCEA34 0.064638057 1.0309278 TCEA3 4 -0.332526970
CAST4 0.954840427 16.4948454 CAST 4 0.441347092
CAP14 3.882988151 46.0481100 CAP1 4 0.699209557
ALOX54 0.069709282 1.3745704 ALOX5 4 -0.772665832
NOG4 0.000000000 0.0000000 NOG 4 -0.492632666
IFI354 0.663050354 13.0584192 IFI35 4 -0.364247145
RAB315 0.722740362 20.9876543 RAB31 5 1.079451227
DYNLT15 1.753660256 46.2962963 DYNLT1 5 1.085746588
CD65 0.053015658 2.4691358 CD6 5 -0.855178803
SLC16A35 1.489386144 36.4197531 SLC16A3 5 1.885902012
TCEA35 0.022471203 0.6172840 TCEA3 5 -0.639665592
CAST5 0.588905193 19.1358025 CAST 5 -0.455416181
CAP15 4.382971430 75.3086420 CAP1 5 1.070459135
ALOX55 0.936051242 29.0123457 ALOX5 5 1.298490728
NOG5 0.000000000 0.0000000 NOG 5 -0.492632666
IFI355 1.586798381 44.4444444 IFI35 5 0.927752365
RAB316 0.000000000 0.0000000 RAB31 6 -0.859246147
DYNLT16 1.141027970 18.7096774 DYNLT1 6 0.358395923
CD66 0.419189591 7.7419355 CD6 6 0.247198707
SLC16A36 0.616712141 10.3225806 SLC16A3 6 0.544154990
TCEA36 0.054614547 1.2903226 TCEA3 6 -0.404420476
CAST6 1.697716548 23.8709677 CAST 6 1.834959993
CAP16 5.083685643 57.4193548 CAP1 6 1.536486993
ALOX56 0.000000000 0.0000000 ALOX5 6 -1.007921897
NOG6 0.000000000 0.0000000 NOG 6 -0.492632666
IFI356 1.649406833 25.1612903 IFI35 6 0.997693972
RAB317 0.621684709 25.0000000 RAB31 7 0.863984931
DYNLT17 0.765453718 34.3750000 DYNLT1 7 -0.199096666
CD67 0.127005438 6.2500000 CD6 7 -0.604337642
SLC16A37 0.415894551 25.0000000 SLC16A3 7 0.131868595
TCEA37 0.155522094 9.3750000 TCEA3 7 0.290053356
CAST7 0.697538909 31.2500000 CAST 7 -0.169273963
CAP17 3.066737690 78.1250000 CAP1 7 0.002601404
ALOX57 0.499256540 21.8750000 ALOX5 7 0.405876279
NOG7 0.070145903 3.1250000 NOG 7 0.577970946
IFI357 0.517834170 12.5000000 IFI35 7 -0.631466369
RAB318 0.406520363 15.3846154 RAB31 8 0.356614136
DYNLT18 0.086673889 7.6923077 DYNLT1 8 -1.601760259
CD68 0.000000000 0.0000000 CD6 8 -1.046001217
SLC16A38 0.000000000 0.0000000 SLC16A3 8 -0.949139305
TCEA38 0.000000000 0.0000000 TCEA3 8 -0.808558406
CAST8 0.306100585 7.6923077 CAST 8 -1.303442938
CAP18 3.425541285 30.7692308 CAP1 8 0.324602582
ALOX58 0.000000000 0.0000000 ALOX5 8 -1.007921897
NOG8 0.000000000 0.0000000 NOG 8 -0.492632666
IFI358 0.000000000 0.0000000 IFI35 8 -1.851862385
However, if your data has outliers or is not normally distributed, using the median expression instead of the mean may be more robust.
before adding your answer.
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