Find top features for a given assay based on total number of counts for the
feature. Can specify a minimum cell count, or a lower percentile
bound to determine the set of variable features. Running this function will
store the total counts and percentile rank for each feature in the feature
metadata for the assay. To only compute the feature metadata, without
changing the variable features for the assay, set min.cutoff=NA.
Usage
FindTopFeatures(object, ...)
# Default S3 method
FindTopFeatures(object, assay = NULL, min.cutoff = "q5", verbose = TRUE, ...)
# S3 method for class 'Assay5'
FindTopFeatures(
object,
assay = NULL,
layer = "counts",
min.cutoff = "q5",
key = "topfeatures",
verbose = TRUE,
...
)
# S3 method for class 'StdAssay'
FindTopFeatures(
object,
assay = NULL,
layer = "counts",
min.cutoff = "q5",
key = "topfeatures",
verbose = TRUE,
...
)
# S3 method for class 'Seurat'
FindTopFeatures(
object,
assay = NULL,
layer = "counts",
min.cutoff = "q5",
key = "topfeatures",
verbose = TRUE,
...
)Arguments
- object
A SeuratObject::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 minimum number of counts for the feature to be included in the set of VariableFeatures. For example, setting to 10 will include features with >10 total counts in the set of VariableFeatures. If
NULL, include all features in VariableFeatures.- verbose
Display messages
- layer
Name of layer to use
- key
Key to use when storing the highly variable feature information in the assay.
Value
Returns a SeuratObject::Seurat() object
Examples
FindTopFeatures(object = atac_small[["peaks"]]["data"])
#> count percentile
#> chr1:9772-10660 10.054353 0.15
#> chr1:180712-181178 9.738032 0.11
#> chr1:181200-181607 10.101979 0.16
#> chr1:191183-192084 0.000000 0.10
#> chr1:267576-268461 10.519701 0.24
#> chr1:270850-271755 0.000000 0.10
#> chr1:273946-274792 0.000000 0.10
#> chr1:585753-586648 26.215333 0.42
#> chr1:605079-605959 9.944358 0.12
#> chr1:629538-630397 10.232028 0.19
#> chr1:633579-634474 58.550774 0.60
#> chr1:778263-779184 356.212854 0.97
#> chr1:816878-817773 82.698869 0.67
#> chr1:825323-825997 11.042938 0.28
#> chr1:827056-827941 220.555088 0.92
#> chr1:844135-844989 27.109755 0.46
#> chr1:854732-855551 0.000000 0.10
#> chr1:856152-857041 10.031365 0.14
#> chr1:857907-858672 58.671696 0.61
#> chr1:860139-860923 18.977213 0.34
#> chr1:865460-866306 10.871091 0.27
#> chr1:869458-870366 181.857881 0.86
#> chr1:876322-877105 18.540907 0.30
#> chr1:877256-878073 0.000000 0.10
#> chr1:890356-891196 10.232028 0.19
#> chr1:897006-897867 0.000000 0.10
#> chr1:898350-899223 19.047064 0.37
#> chr1:904344-905188 197.192436 0.90
#> chr1:906494-907390 58.830790 0.62
#> chr1:912460-913353 27.852778 0.50
#> chr1:920759-921626 33.716973 0.53
#> chr1:923353-924170 57.661807 0.59
#> chr1:925410-926122 18.592370 0.31
#> chr1:935093-935954 10.031365 0.14
#> chr1:940016-940932 64.810925 0.64
#> chr1:943054-943636 27.846665 0.49
#> chr1:955190-956101 19.431404 0.40
#> chr1:958863-959759 279.001301 0.93
#> chr1:960325-961048 85.143312 0.68
#> chr1:966548-967354 106.638381 0.74
#> chr1:975761-976723 94.229779 0.71
#> chr1:983865-984750 109.950806 0.75
#> chr1:993329-994164 10.819798 0.26
#> chr1:995556-996375 28.163445 0.52
#> chr1:998676-999441 63.984854 0.63
#> chr1:999740-1000366 94.406101 0.72
#> chr1:1000488-1001217 118.458473 0.76
#> chr1:1001698-1002476 128.116810 0.80
#> chr1:1004753-1005628 52.127239 0.57
#> chr1:1008923-1009812 10.319036 0.20
#> chr1:1012999-1013896 323.867500 0.96
#> chr1:1019089-1019953 187.410402 0.88
#> chr1:1027807-1028323 10.349807 0.21
#> chr1:1032718-1033630 211.030979 0.91
#> chr1:1038451-1039304 51.195599 0.56
#> chr1:1040389-1041273 163.252427 0.85
#> chr1:1059203-1060060 149.097449 0.82
#> chr1:1063699-1064592 146.506760 0.81
#> chr1:1068591-1069593 313.163683 0.95
#> chr1:1079455-1080338 102.172141 0.73
#> chr1:1092669-1093579 18.652362 0.32
#> chr1:1098941-1099797 119.984467 0.78
#> chr1:1103886-1104761 0.000000 0.10
#> chr1:1106514-1107086 10.819798 0.26
#> chr1:1107314-1108235 0.000000 0.10
#> chr1:1115790-1116694 384.649610 0.98
#> chr1:1121854-1122751 187.585820 0.89
#> chr1:1136258-1136912 18.137941 0.29
#> chr1:1137049-1137873 65.930934 0.65
#> chr1:1140665-1141034 0.000000 0.10
#> chr1:1143866-1144747 70.656491 0.66
#> chr1:1157456-1158077 150.248748 0.84
#> chr1:1165728-1166624 10.126671 0.17
#> chr1:1171563-1172488 26.795806 0.44
#> chr1:1173370-1174285 94.094365 0.70
#> chr1:1174988-1175803 10.483334 0.23
#> chr1:1188896-1189774 19.017941 0.36
#> chr1:1201061-1201938 149.960959 0.83
#> chr1:1206183-1207065 26.758039 0.43
#> chr1:1207866-1208674 85.937578 0.69
#> chr1:1208906-1209549 34.984299 0.54
#> chr1:1212684-1213459 123.532304 0.79
#> chr1:1216631-1217510 56.849865 0.58
#> chr1:1219105-1219995 18.980603 0.35
#> chr1:1221668-1222458 28.148528 0.51
#> chr1:1222590-1223380 10.381554 0.22
#> chr1:1231645-1232553 387.197891 0.99
#> chr1:1246286-1247224 19.940843 0.41
#> chr1:1250624-1251529 119.784187 0.77
#> chr1:1259851-1260705 0.000000 0.10
#> chr1:1261037-1261825 19.342616 0.38
#> chr1:1264764-1265656 27.028633 0.45
#> chr1:1273489-1274375 311.675182 0.94
#> chr1:1287495-1288325 19.375318 0.39
#> chr1:1289933-1290836 27.202227 0.48
#> chr1:1291564-1292473 43.747484 0.55
#> chr1:1299784-1300670 18.749991 0.33
#> chr1:1301689-1302543 27.131485 0.47
#> chr1:1305198-1306109 183.158538 0.87
#> chr1:1307720-1308738 418.773322 1.00
FindTopFeatures(object = atac_small[["peaks"]])
#> Finding variable features for layer counts
#> GRangesAssay data with 100 features for 100 cells
#> Variable features: 100
#> Annotation present: TRUE
#> Fragment files: 0
#> Motifs present: TRUE
#> Links present: 0
#> Region aggregation matrices: 0
FindTopFeatures(object = atac_small[["peaks"]])
#> Finding variable features for layer counts
#> GRangesAssay data with 100 features for 100 cells
#> Variable features: 100
#> Annotation present: TRUE
#> Fragment files: 0
#> Motifs present: TRUE
#> Links present: 0
#> Region aggregation matrices: 0
FindTopFeatures(atac_small)
#> Finding variable features for layer counts
#> An object of class Seurat
#> 150 features across 100 samples within 2 assays
#> Active assay: peaks (100 features, 100 variable features)
#> 2 layers present: counts, data
#> 1 other assay present: RNA
#> 2 dimensional reductions calculated: lsi, umap