|
|
@@ -36,11 +36,11 @@ Note that this strategy is currently only available for `hg19`, `hg38`, `mm9` an
|
|
|
|
|
|
Although CALDER was mainly tested on human and mouse dataset, it can be applied on dataset from other genomes. One additional information is required in such case: a `feature_track` that is presumably positively correlated with compartment score (thus higher values in A than in B compartment). This information will be used for correctly determing the `A/B` direction. Some suggested tracks are gene density, H3K27ac, H3K4me1, H3K4me2, H3K4me3, H3K36me3 (or negative transform of H3K9me3) signals. Note that this information will not alter the hierarchical compartment/TAD structure, and can come from any external study with matched genome.
|
|
|
|
|
|
-
|
|
|
- library(rtracklayer)
|
|
|
- feature_track = import('/mnt/etemp/Yuanlong/4.Tmp/ENCFF934YOE.bigWig')
|
|
|
- feature_track = data.table::as.data.table(feature_track)[, c(1:3, 6)]
|
|
|
-
|
|
|
+```
|
|
|
+library(rtracklayer)
|
|
|
+feature_track = import('/mnt/etemp/Yuanlong/4.Tmp/ENCFF934YOE.bigWig')
|
|
|
+feature_track = data.table::as.data.table(feature_track)[, c(1:3, 6)]
|
|
|
+```
|
|
|
chr start end score
|
|
|
chr1 534179 534353 2.80512
|
|
|
chr1 534354 572399 0
|
Yuanlong LIU