Find top binary features for a given assay based on total number of cells containing feature. Can specify a minumum cell count, or a lower percentile bound.
FindTopFeatures(object, ...) # S3 method for default FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) # S3 method for Assay FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...) # S3 method for Seurat FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...)
| object | A Seurat object |
|---|---|
| ... | Arguments passed to other methods |
| assay | Name of assay to use |
| min.cutoff | Cutoff for feature to be included in the VariableFeatures for the object. This can be a percentile specified as 'q' followed by the minimum percentile, for example 'q5' to set the top 95% most common features as the VariableFeatures for the object. Alternatively, this can be an integer specifying the minumum number of cells containing the feature for the feature to be included in the set of VariableFeatures. For example, setting to 10 will include features in >10 cells in the set of VariableFeatures. If NULL, include all features in VariableFeatures. |
| verbose | Display messages |
Returns a Seurat object
FindTopFeatures(object = atac_small[['peaks']][])#> count percentile #> chr1:1549446-1552535 384.421944 1.00 #> chr1:1051006-1053102 341.512218 0.99 #> chr1:1240091-1245762 319.659471 0.98 #> chr1:1333514-1336003 300.128621 0.97 #> chr1:1309645-1311492 299.927352 0.96 #> chr1:928630-937949 294.707234 0.95 #> chr1:1446312-1448163 269.016136 0.94 #> chr1:1166366-1168282 264.148882 0.93 #> chr1:1259506-1261414 262.969914 0.92 #> chr1:1562519-1567986 261.216606 0.91 #> chr1:713460-714823 257.353734 0.90 #> chr1:1341794-1343522 254.901296 0.89 #> chr1:894021-896898 245.403197 0.88 #> chr1:1509089-1511050 228.388433 0.87 #> chr1:954372-958413 219.098498 0.86 #> chr1:1406028-1408272 208.368431 0.85 #> chr1:1283373-1285555 203.939847 0.84 #> chr1:1207999-1210168 202.543213 0.83 #> chr1:1002063-1006231 193.486246 0.82 #> chr1:948133-951142 186.806665 0.81 #> chr1:994056-995654 184.388904 0.80 #> chr1:973840-977754 182.952115 0.79 #> chr1:762106-763359 177.534036 0.78 #> chr1:1306174-1308201 169.244301 0.77 #> chr1:1135873-1137356 161.040182 0.76 #> chr1:1147592-1153647 158.486810 0.75 #> chr1:1092026-1094240 147.737760 0.74 #> chr1:967806-970136 130.152180 0.73 #> chr1:1140204-1144858 122.152509 0.72 #> chr1:998895-1000200 118.534531 0.71 #> chr1:1057192-1058404 114.583058 0.70 #> chr1:1534779-1536172 104.895893 0.69 #> chr1:901313-902847 102.117257 0.68 #> chr1:839520-841123 92.894393 0.67 #> chr1:804872-805761 86.921822 0.66 #> chr1:858464-861548 86.682437 0.65 #> chr1:1288121-1291291 80.785359 0.64 #> chr1:752422-753038 76.060931 0.63 #> chr1:1014697-1015912 70.245184 0.62 #> chr1:875427-878705 70.218939 0.61 #> chr1:1440010-1441302 68.959631 0.60 #> chr1:1071616-1073471 68.136922 0.59 #> chr1:1369325-1371497 62.783037 0.58 #> chr1:1185856-1186893 60.889982 0.57 #> chr1:1226995-1227705 60.547551 0.56 #> chr1:919270-919976 59.310339 0.55 #> chr1:1397565-1398360 48.370455 0.54 #> chr1:939991-941002 45.654622 0.53 #> chr1:1542207-1543000 39.939140 0.52 #> chr1:1034158-1035389 39.110858 0.51 #> chr1:779589-780271 38.180004 0.50 #> chr1:911152-912038 38.004690 0.49 #> chr1:1475360-1476487 37.872139 0.48 #> chr1:925760-926165 30.236590 0.47 #> chr1:944476-944998 29.771544 0.46 #> chr1:1361935-1362825 29.113191 0.45 #> chr1:1154588-1155301 29.011545 0.44 #> chr1:1108888-1109641 27.756782 0.43 #> chr1:1079247-1080264 27.277069 0.42 #> chr1:1532974-1533319 24.165297 0.41 #> chr1:1553343-1553743 21.303150 0.40 #> chr1:1337323-1337799 20.401258 0.39 #> chr1:1293753-1295053 20.204558 0.38 #> chr1:856165-857031 19.657287 0.37 #> chr1:841866-842572 19.222005 0.36 #> chr1:565107-565550 13.122365 0.35 #> chr1:1559561-1559642 13.122365 0.35 #> chr1:1225538-1225956 11.618295 0.33 #> chr1:1555436-1556258 11.618295 0.33 #> chr1:1246658-1247019 11.512935 0.31 #> chr1:926725-927135 11.330616 0.30 #> chr1:1195330-1196057 11.250574 0.29 #> chr1:1059663-1059950 11.176467 0.28 #> chr1:1273069-1273470 11.176467 0.28 #> chr1:1557272-1558191 11.107475 0.26 #> chr1:971362-971398 10.871091 0.25 #> chr1:1287098-1287386 10.871091 0.25 #> chr1:1376178-1376717 10.871091 0.25 #> chr1:870713-871075 10.724490 0.22 #> chr1:1157222-1157824 10.724490 0.22 #> chr1:1235400-1235791 10.724490 0.22 #> chr1:978275-978602 10.483334 0.19 #> chr1:1134508-1135192 10.483334 0.19 #> chr1:801120-801338 10.008893 0.17 #> chr1:1107056-1107833 10.008893 0.17 #> chr1:1173312-1173499 10.008893 0.17 #> chr1:793516-793741 9.883736 0.14 #> chr1:1181881-1182281 9.883736 0.14 #> chr1:1229029-1229400 9.883736 0.14 #> chr1:1532373-1532401 9.883736 0.14 #> chr1:569174-569639 0.000000 0.10 #> chr1:873704-873830 0.000000 0.10 #> chr1:898315-898318 0.000000 0.10 #> chr1:962218-962655 0.000000 0.10 #> chr1:997359-997774 0.000000 0.10 #> chr1:1101355-1101731 0.000000 0.10 #> chr1:1124547-1124802 0.000000 0.10 #> chr1:1237343-1237711 0.000000 0.10 #> chr1:1280218-1280357 0.000000 0.10 #> chr1:1355337-1355731 0.000000 0.10FindTopFeatures(object = atac_small[['peaks']])#> Assay data with 100 features for 100 cells #> Top 10 variable features: #> chr1:1549446-1552535, chr1:1051006-1053102, chr1:1240091-1245762, #> chr1:1333514-1336003, chr1:1309645-1311492, chr1:928630-937949, #> chr1:1166366-1168282, chr1:1446312-1448163, chr1:1562519-1567986, #> chr1:1259506-1261414FindTopFeatures(atac_small)#> An object of class Seurat #> 300 features across 100 samples within 3 assays #> Active assay: peaks (100 features, 100 variable features) #> 2 other assays present: bins, RNA #> 2 dimensional reductions calculated: lsi, umap