Fetch the table of top markers that pass the filtering
findMarkerTopTable( inSCE, log2fcThreshold = 1, fdrThreshold = 0.05, minClustExprPerc = 0.7, maxCtrlExprPerc = 0.4, minMeanExpr = 1, topN = 10 )
| inSCE | SingleCellExperiment inherited object. |
|---|---|
| log2fcThreshold | Only use DEGs with the absolute values of log2FC
larger than this value. Default |
| fdrThreshold | Only use DEGs with FDR value smaller than this value.
Default |
| minClustExprPerc | A numeric scalar. The minimum cutoff of the
percentage of cells in the cluster of interests that expressed the marker
gene. Default |
| maxCtrlExprPerc | A numeric scalar. The maximum cutoff of the
percentage of cells out of the cluster (control group) that expressed the
marker gene. Default |
| minMeanExpr | A numeric scalar. The minimum cutoff of the mean
expression value of the marker in the cluster of interests. Default |
| topN | An integer. Only to fetch this number of top markers for each
cluster in maximum, in terms of log2FC value. Use |
An organized data.frame object, with the top marker gene
information.
Users have to run findMarkerDiffExp() prior to using this
function to extract a top marker table.
data("mouseBrainSubsetSCE", package = "singleCellTK") mouseBrainSubsetSCE <- findMarkerDiffExp(mouseBrainSubsetSCE, useAssay = "logcounts", cluster = "level1class")#>#>#>findMarkerTopTable(mouseBrainSubsetSCE)#> Gene Log2_FC Pvalue FDR level1class clusterExprPerc #> 1112 Tyrobp 3.282906 6.733332e-07 0.0001720692 microglia 1.0000000 #> 1126 C1qb 3.281828 2.516695e-06 0.0001720692 microglia 0.9333333 #> 1127 Fcgr3 3.130255 2.516695e-06 0.0001720692 microglia 0.9333333 #> 1130 C1qa 3.862682 6.733332e-07 0.0001720692 microglia 1.0000000 #> 1131 Fcrls 2.955799 6.755317e-07 0.0001720692 microglia 1.0000000 #> 1228 Apoe 4.439115 3.642815e-06 0.0001796628 microglia 1.0000000 #> 1182 Ccl12 3.151363 8.600798e-06 0.0002501837 microglia 0.8666667 #> 1100 Pf4 3.466775 2.771660e-05 0.0004581158 microglia 0.8000000 #> 1114 Lyz2 3.873899 2.771660e-05 0.0004581158 microglia 0.8000000 #> 1150 Mrc1 2.954606 2.757165e-05 0.0004581158 microglia 0.8000000 #> 843 Mobp 4.075208 2.084154e-06 0.0001720692 oligodendrocytes 1.0000000 #> 855 Sept4 3.669662 1.241931e-06 0.0001720692 oligodendrocytes 1.0000000 #> 860 Gsn 3.521439 1.454160e-06 0.0001720692 oligodendrocytes 1.0000000 #> 897 Mog 5.161946 1.256717e-06 0.0001720692 oligodendrocytes 1.0000000 #> 936 Ugt8a 4.319125 2.403213e-06 0.0001720692 oligodendrocytes 1.0000000 #> 1001 Qdpr 4.061774 1.641748e-06 0.0001720692 oligodendrocytes 1.0000000 #> 1014 Ermn 4.318830 1.865414e-06 0.0001720692 oligodendrocytes 1.0000000 #> 1017 Cryab 4.001140 1.743790e-06 0.0001720692 oligodendrocytes 1.0000000 #> 1048 Apod 5.311107 2.203822e-06 0.0001720692 oligodendrocytes 1.0000000 #> 831 Tspan2 3.743799 3.649158e-06 0.0001796628 oligodendrocytes 1.0000000 #> ControlExprPerc clusterAveExpr #> 1112 0.00000000 3.282906 #> 1126 0.00000000 3.281828 #> 1127 0.00000000 3.130255 #> 1130 0.00000000 3.862682 #> 1131 0.00000000 2.955799 #> 1228 0.33333333 5.076598 #> 1182 0.00000000 3.151363 #> 1100 0.00000000 3.466775 #> 1114 0.00000000 3.873899 #> 1150 0.00000000 2.954606 #> 843 0.26666667 4.430003 #> 855 0.13333333 3.880991 #> 860 0.06666667 3.676234 #> 897 0.13333333 5.475308 #> 936 0.20000000 4.771216 #> 1001 0.06666667 4.261774 #> 1014 0.26666667 4.663492 #> 1017 0.20000000 4.306804 #> 1048 0.33333333 5.838233 #> 831 0.20000000 4.155127