R/runFindMarker.R
getFindMarkerTopTable.RdFetch the table of top markers that pass the filtering
getFindMarkerTopTable(
inSCE,
log2fcThreshold = 1,
fdrThreshold = 0.05,
minClustExprPerc = 0.7,
maxCtrlExprPerc = 0.4,
minMeanExpr = 1,
topN = 10
)
findMarkerTopTable(
inSCE,
log2fcThreshold = 1,
fdrThreshold = 0.05,
minClustExprPerc = 0.7,
maxCtrlExprPerc = 0.4,
minMeanExpr = 1,
topN = 10
)SingleCellExperiment inherited object.
Only use DEGs with the absolute values of log2FC
larger than this value. Default 1
Only use DEGs with FDR value smaller than this value.
Default 0.05
A numeric scalar. The minimum cutoff of the
percentage of cells in the cluster of interests that expressed the marker
gene. Default 0.7.
A numeric scalar. The maximum cutoff of the
percentage of cells out of the cluster (control group) that expressed the
marker gene. Default 0.4.
A numeric scalar. The minimum cutoff of the mean
expression value of the marker in the cluster of interests. Default 1.
An integer. Only to fetch this number of top markers for each
cluster in maximum, in terms of log2FC value. Use NULL to cancel the
top N subscription. Default 10.
An organized data.frame object, with the top marker gene
information.
Users have to run runFindMarker prior to using this
function to extract a top marker table.
data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runFindMarker(mouseBrainSubsetSCE,
useAssay = "logcounts",
cluster = "level1class")
#> Sat Mar 18 10:27:51 2023 ... Identifying markers for cluster 'microglia', using DE method 'wilcox'
#> Sat Mar 18 10:27:52 2023 ... Identifying markers for cluster 'oligodendrocytes', using DE method 'wilcox'
#> Sat Mar 18 10:27:52 2023 ... Organizing findMarker result
getFindMarkerTopTable(mouseBrainSubsetSCE)
#> Gene Log2_FC Pvalue FDR level1class clusterExprPerc
#> 1228 Apoe 4.439115 3.642815e-06 0.0001796628 microglia 1.0000000
#> 1114 Lyz2 3.873899 2.771660e-05 0.0004581158 microglia 0.8000000
#> 1130 C1qa 3.862682 6.733332e-07 0.0001720692 microglia 1.0000000
#> 1100 Pf4 3.466775 2.771660e-05 0.0004581158 microglia 0.8000000
#> 1112 Tyrobp 3.282906 6.733332e-07 0.0001720692 microglia 1.0000000
#> 1126 C1qb 3.281828 2.516695e-06 0.0001720692 microglia 0.9333333
#> 1182 Ccl12 3.151363 8.600798e-06 0.0002501837 microglia 0.8666667
#> 1127 Fcgr3 3.130255 2.516695e-06 0.0001720692 microglia 0.9333333
#> 1131 Fcrls 2.955799 6.755317e-07 0.0001720692 microglia 1.0000000
#> 1150 Mrc1 2.954606 2.757165e-05 0.0004581158 microglia 0.8000000
#> 1002 Mal 5.361403 7.495067e-06 0.0002501837 oligodendrocytes 1.0000000
#> 1048 Apod 5.311107 2.203822e-06 0.0001720692 oligodendrocytes 1.0000000
#> 897 Mog 5.161946 1.256717e-06 0.0001720692 oligodendrocytes 1.0000000
#> 906 Enpp2 4.327247 6.757710e-06 0.0002501837 oligodendrocytes 1.0000000
#> 936 Ugt8a 4.319125 2.403213e-06 0.0001720692 oligodendrocytes 1.0000000
#> 1014 Ermn 4.318830 1.865414e-06 0.0001720692 oligodendrocytes 1.0000000
#> 843 Mobp 4.075208 2.084154e-06 0.0001720692 oligodendrocytes 1.0000000
#> 1001 Qdpr 4.061774 1.641748e-06 0.0001720692 oligodendrocytes 1.0000000
#> 1017 Cryab 4.001140 1.743790e-06 0.0001720692 oligodendrocytes 1.0000000
#> 831 Tspan2 3.743799 3.649158e-06 0.0001796628 oligodendrocytes 1.0000000
#> ControlExprPerc clusterAveExpr
#> 1228 0.33333333 5.076598
#> 1114 0.00000000 3.873899
#> 1130 0.00000000 3.862682
#> 1100 0.00000000 3.466775
#> 1112 0.00000000 3.282906
#> 1126 0.00000000 3.281828
#> 1182 0.00000000 3.151363
#> 1127 0.00000000 3.130255
#> 1131 0.00000000 2.955799
#> 1150 0.00000000 2.954606
#> 1002 0.40000000 6.370450
#> 1048 0.33333333 5.838233
#> 897 0.13333333 5.475308
#> 906 0.40000000 5.093370
#> 936 0.20000000 4.771216
#> 1014 0.26666667 4.663492
#> 843 0.26666667 4.430003
#> 1001 0.06666667 4.261774
#> 1017 0.20000000 4.306804
#> 831 0.20000000 4.155127