Mapping cells at the group-level
We can start by trying to map cells from the query data to GWPS at
the group-level, where cells of the same condition can be assigned
together. Here, we map cells according to their perturbation labels, and
obtain a dataframe summarizing these assignments. This can be slow due
to the size of GWPS, but faster results can be obtained using
parallelization with the mc.cores parameter.
jost <- FingerprintCells(jost,
condition_meta = 'gene',
control_class = 'neg',
dictionary = gwps_dictionary,
group_level = TRUE)
#> The following 541 features are not present in the SCT assay:LINC01128, INTS11, MRPL20-AS1, FNDC10, CENPS, SPEN-AS1, MICOS10, ELOA-AS1, ELOA, MACO1, ATP5IF1, SNHG3, YARS1, TMEM35B, ADPRS, UTP11, TUT4, CZIB, IFT25, MIGA1, SELENOF, KYAT3, LINC02609, DPH5-DT, EEIG2, SARS1, ATP5PB, LINC02802, LINC02805, H2AC20, PRUNE1, GBA1, ENTREP3, KHDC4, MIR9-1HG, NAXE, METTL25B, FIRRM, GAS5, COP1, ODR4, RO60, UTP25, PACC1, FLVCR1-DT, EPRS1, H3-3A, COQ8A, H2AC25, RAB4A-AS1...
#> Processing data...
#> Preparing...
#> Classifying...
group_level_summary <- SummarizeResults(jost, group_level = TRUE,
condition_meta = 'gene',
control_class = 'neg')
print(group_level_summary[1:5,])
#> top_credible_set.gene
#> RPL9 SPATA5L1,URB2,ABCF1,NLE1,SPATA5,RPL3,CENPP,HERC1,EIF6,SURF6,DDX51,TMA16,CINP,RSL1D1,MYBBP1A,CARF,UBE2E1,KRR1,NOC3L,FBXL14,RPL9
#> GATA1 HMBOX1,GATA1
#> POLR1D TAF1C,RPL3,TAF1D,GTF3C6,POLR1E,POLR1C,CD3EAP,RPL26,GNL2,RPL37
#> POLR2H UXT,NUMA1,C1QBP,PFDN2,MED26,RPAP2,POLR2B,ERCC2,GPN3
#> RPS14 KRR1,WDR3,FBXL14,AATF,RIOK1,RPS12,RIOK2,DDX52,DIMT1,DDX10,ZNF84,FCF1,RPS3,WDR36,NOP10,RPS14,RPS2,RPS19,DDX47,TSR2,RPP30,POP4,RRP9,POP1,ZNHIT6,USP36,UTP20,RPS3A,DNTTIP2,RPS20,RPS15A,ABT1,IMPA2,RPP14,ZCCHC9,ESF1,BUD23,LAMB1,RPS10-NUDT3,RRP12
#> top_lbf.gene
#> RPL9 2882.97
#> GATA1 2762.02
#> POLR1D 2093.92
#> POLR2H 2056.41
#> RPS14 1900.83
#> all_credible_sets.gene
#> RPL9 SPATA5L1,URB2,ABCF1,NLE1,SPATA5,RPL3,CENPP,HERC1,EIF6,SURF6,DDX51,TMA16,CINP,RSL1D1,MYBBP1A,CARF,UBE2E1,KRR1,NOC3L,FBXL14,RPL9
#> GATA1 HMBOX1,GATA1
#> POLR1D TAF1C,RPL3,TAF1D,GTF3C6,POLR1E,POLR1C,CD3EAP,RPL26,GNL2,RPL37
#> POLR2H UXT,NUMA1,C1QBP,PFDN2,MED26,RPAP2,POLR2B,ERCC2,GPN3
#> RPS14 KRR1,WDR3,FBXL14,AATF,RIOK1,RPS12,RIOK2,DDX52,DIMT1,DDX10,ZNF84,FCF1,RPS3,WDR36,NOP10,RPS14,RPS2,RPS19,DDX47,TSR2,RPP30,POP4,RRP9,POP1,ZNHIT6,USP36,UTP20,RPS3A,DNTTIP2,RPS20,RPS15A,ABT1,IMPA2,RPP14,ZCCHC9,ESF1,BUD23,LAMB1,RPS10-NUDT3,RRP12
#> all_lbfs.gene
#> RPL9 2882.97
#> GATA1 2762.02
#> POLR1D 2093.92
#> POLR2H 2056.41
#> RPS14 1900.83
#> all_signs.gene
#> RPL9 +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+
#> GATA1 +,+
#> POLR1D +,+,+,+,+,+,+,+,+,+
#> POLR2H +,+,+,+,+,+,+,+,+
#> RPS14 +,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+,+Some of these assignments involve very long credible sets, which
makes sense given that many of these perturbations are of genes
belonging to large complexes. We can visualize these assignments using
Long Island City plots. Below, we should an example of an assignment
with a long credible set, and one with a short credible set. We could
alternatively sort the fingerprints in alphabetical order by setting the
order argument appropriately.
LongIslandCityPlot(jost, query = 'RPS14', condition_meta = 'gene',
control_class = 'neg', dictionary = gwps_dictionary,
group_level = T, cs = 10)
LongIslandCityPlot(jost, query = 'BCR', condition_meta = 'gene',
control_class = 'neg', dictionary = gwps_dictionary,
group_level = T, cs = 10)These plots show the conditional posterior mean coefficients for each
fingerprint on the y-axis. However, in large-scale settings, this can be
slow to compute. For a faster version, we can specify the argument
approx = TRUE to instead show marginal correlations on the
y-axis, e.g.:
LongIslandCityPlot(jost, query = 'RPS14', condition_meta = 'gene',
control_class = 'neg', dictionary = gwps_dictionary,
group_level = T, cs = 10, approx = TRUE)
LongIslandCityPlot(jost, query = 'BCR', condition_meta = 'gene',
control_class = 'neg', dictionary = gwps_dictionary,
group_level = T, cs = 10, approx = TRUE)
To understand what genes were most responsible for driving these
matches, we can use the ExplainMatch function. Here, the
visualizations are automatically made with the stored pseudobulk GWPS
object inside the precomputed dictionary, since we don’t have the full
GWPS object loaded in.
ExplainMatch(jost, query = 'BCR', match = 'BCR',
condition_meta = 'gene', control_class = 'neg',
dictionary = gwps_dictionary, group_level = T,
num_genes = 25)
