Identify marker feats for all clusters in a one vs all manner.
findMarkers_one_vs_all(
gobject,
feat_type = NULL,
spat_unit = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
method = c("scran", "gini", "mast"),
pval = 0.01,
logFC = 0.5,
min_feats = 10,
min_genes = NULL,
min_expr_gini_score = 0.5,
min_det_gini_score = 0.5,
detection_threshold = 0,
rank_score = 1,
adjust_columns = NULL,
verbose = TRUE,
...
)giotto object
feature type
spatial unit
feat expression values to use
clusters to use
selection of clusters to compare
method to use to detect differentially expressed feats
scran & mast: filter on minimal p-value
scan & mast: filter on logFC
minimum feats to keep per cluster, overrides pval and logFC
deprecated, use min_feats
gini: filter on minimum gini coefficient for expression
gini: filter minimum gini coefficient for detection
gini: detection threshold for feat expression
gini: rank scores to include
mast: column in pDataDT to adjust for (e.g. detection rate)
be verbose
additional parameters for the findMarkers function in scran or zlm function in MAST
data.table with marker feats
Wrapper for all one vs all functions to detect marker feats for clusters.
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : '/usr/bin/python3'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
findMarkers_one_vs_all(g, cluster_column = "leiden_clus")
#> using 'Scran' to detect marker feats. If used in published
#> research, please cite: Lun ATL, McCarthy DJ, Marioni JC (2016).
#> 'A step-by-step workflow for low-level analysis of single-cell RNA-seq
#> data with Bioconductor.'
#> F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2.
#> start with cluster 1start with cluster 2start with cluster 3start with cluster 4start with cluster 5start with cluster 6start with cluster 7
#> Top p.value FDR logFC feats cluster ranking
#> <int> <num> <num> <num> <char> <char> <num>
#> 1: 1 0.000354327 0.19546782 0.4994571 Arpp19 1 3
#> 2: 4 0.002932856 0.44588387 0.5955190 Pde1a 1 1
#> 3: 5 0.003516434 0.44588387 0.5293216 Ipcef1 1 2
#> 4: 6 0.008882113 0.71268782 0.4552590 Camk2d 1 6
#> 5: 7 0.013139650 0.71268782 0.4803266 Ngef 1 4
#> 6: 10 0.016171727 0.71268782 0.4738020 Tbr1 1 5
#> 7: 15 0.018401100 0.71268782 0.4359583 Myl4 1 8
#> 8: 16 0.020925524 0.71268782 0.4179815 Nptxr 1 10
#> 9: 17 0.021056054 0.71268782 0.4410319 Lamp5 1 7
#> 10: 33 0.041136065 0.76110482 0.4264191 Stx1a 1 9
#> 11: 1 0.002208825 0.53711117 0.5832163 Prr18 2 1
#> 12: 2 0.003332138 0.53711117 0.5181029 Carhsp1 2 2
#> 13: 3 0.004700090 0.53711117 0.4810502 Tprn 2 7
#> 14: 4 0.004921900 0.53711117 0.4670464 Padi2 2 9
#> 15: 5 0.005490721 0.53711117 0.4393166 S1pr5 2 11
#> 16: 7 0.006812177 0.53711117 0.3894486 Clic1 2 22
#> 17: 8 0.007043215 0.53711117 0.4882910 Tmbim1 2 6
#> 18: 9 0.007624607 0.53711117 0.5138513 Tmem88b 2 3
#> 19: 10 0.009028757 0.54975200 0.3764765 Cryab 2 26
#> 20: 12 0.011597115 0.54975200 0.4520588 Phldb1 2 10
#> 21: 13 0.012453659 0.54975200 0.4894421 Grb14 2 5
#> 22: 15 0.015239247 0.54975200 0.4697248 Ppp1r14a 2 8
#> 23: 27 0.028652205 0.54975200 0.5009713 Tnnt1 2 4
#> 24: 11 0.015565349 0.81477908 0.3794660 Bcam 3 4
#> 25: 20 0.026477390 0.81477908 0.4633258 Grm1 3 2
#> 26: 22 0.028812026 0.83031020 0.4163589 Itih3 3 3
#> 27: 55 0.091050217 0.96701893 0.3757642 Plcb4 3 5
#> 28: 61 0.100930232 0.96701893 0.4724775 Prkcd 3 1
#> 29: 64 0.110921899 0.96701893 0.3306064 Aldh1a1 3 7
#> 30: 65 0.111962853 0.96701893 0.3296060 Vim 3 8
#> 31: 66 0.114585832 0.96701893 0.2897821 Prox1 3 10
#> 32: 73 0.133643639 0.96701893 0.3633652 Zic1 3 6
#> 33: 107 0.175926132 0.96701893 0.3074804 Vamp1 3 9
#> 34: 7 0.003429921 0.28637427 0.6202705 Pcdh8 4 2
#> 35: 9 0.004989616 0.34768541 0.5338170 Gss 4 5
#> 36: 10 0.005483997 0.34768541 0.6350344 Cygb 4 1
#> 37: 11 0.008053972 0.41605624 0.5981233 Sowaha 4 3
#> 38: 25 0.021696009 0.52964105 0.4588221 Homer3 4 9
#> 39: 31 0.027383336 0.53917299 0.5491473 Wipf3 4 4
#> 40: 33 0.029708388 0.53917299 0.5052576 C1ql2 4 7
#> 41: 38 0.033496196 0.53917299 0.5205546 Calb1 4 6
#> 42: 45 0.038269376 0.53917299 0.4568997 Vstm2l 4 10
#> 43: 73 0.076839208 0.66734326 0.4669022 Synpr 4 8
#> 44: 10 0.032781719 0.96693210 0.4073136 Ppp1r1a 5 5
#> 45: 13 0.035476975 0.96693210 0.3952556 Serpinh1 5 7
#> 46: 16 0.039171568 0.96693210 0.4033382 Cacng5 5 6
#> 47: 17 0.041923751 0.96693210 0.3813645 Suclg2 5 9
#> 48: 23 0.050830947 0.96693210 0.4847041 Bcl11b 5 1
#> 49: 29 0.065093223 0.96693210 0.3870576 Grb14 5 8
#> 50: 35 0.070743235 0.96693210 0.4102664 Pitpnm2 5 4
#> 51: 40 0.091234404 0.96693210 0.4110672 Sowaha 5 3
#> 52: 60 0.118368025 0.96693210 0.4153359 Fam163b 5 2
#> 53: 64 0.121852917 0.96693210 0.3794176 Rora 5 10
#> 54: 13 0.001389098 0.06774522 0.8375194 Smim1 6 1
#> 55: 20 0.008407944 0.24881180 0.6904204 Ttr 6 7
#> 56: 25 0.010250009 0.24881180 0.8264599 Hopx 6 2
#> 57: 30 0.012423050 0.24976015 0.7589815 Siglech 6 5
#> 58: 33 0.013357161 0.24976015 0.7804225 Cldn5 6 3
#> 59: 39 0.022190202 0.36073304 0.6920161 Homer2 6 6
#> 60: 44 0.029494591 0.42499024 0.6463815 Evi2a 6 8
#> 61: 45 0.030635500 0.43137095 0.7611254 Cabp7 6 4
#> 62: 47 0.032523726 0.43137095 0.6352972 Vsir 6 9
#> 63: 51 0.036742690 0.45676206 0.6266049 Cyp2d22 6 10
#> 64: 77 0.019024942 0.15664692 1.0917235 Sypl 7 4
#> 65: 80 0.038112923 0.30204492 1.0720757 Vwa1 7 6
#> 66: 87 0.065087126 0.46778865 1.0877216 Man1a 7 5
#> 67: 92 0.075133648 0.51776883 1.1714598 Olig2 7 2
#> 68: 103 0.095480764 0.58771655 1.0561925 Gng4 7 7
#> 69: 104 0.096534827 0.58849116 1.1651781 Myrf 7 3
#> 70: 111 0.112835941 0.64448637 0.9399060 Pbxip1 7 10
#> 71: 113 0.125182233 0.70234987 1.2522299 Pou3f1 7 1
#> 72: 117 0.133698127 0.72448387 0.9716501 Aspa 7 9
#> 73: 127 0.179546145 0.89631698 1.0321280 Lmo1 7 8
#> Top p.value FDR logFC feats cluster ranking