Identify marker feats for all clusters in a one vs all manner based on scran's implementation of findMarkers.
findScranMarkers_one_vs_all(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
pval = 0.01,
logFC = 0.5,
min_feats = 10,
min_genes = NULL,
verbose = TRUE,
...
)giotto object
spatial unit
feature type
feat expression values to use
clusters to use
subset of clusters to use
filter on minimal p-value
filter on logFC
minimum feats to keep per cluster, overrides pval and logFC
deprecated, use min_feats
be verbose
additional parameters for the findMarkers function in scran
data.table with marker feats
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
#>
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
findScranMarkers_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