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@@ -35,10 +35,13 @@ Note that this strategy is currently only available for `hg19`, `hg38`, `mm9` an
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### Introduction of CALDER analysis for other genomes
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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.
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+<br>
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+<br>
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+`feature_track` should be 4 column data.frame or data.table format, and can be generated directly from conventional format such as bed or wig, such as the following example:
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```
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library(rtracklayer)
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-feature_track = import('/mnt/etemp/Yuanlong/4.Tmp/ENCFF934YOE.bigWig')
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+feature_track = import('ENCFF934YOE.bigWig') ## from ENCODE
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feature_track = data.table::as.data.table(feature_track)[, c(1:3, 6)]
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```
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chr start end score
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@@ -52,7 +55,6 @@ feature_track = data.table::as.data.table(feature_track)[, c(1:3, 6)]
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chrY 59032414 59032415 0.96625
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chrY 59032416 59032456 0.92023
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chrY 59032457 59032578 0.78875
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- ...
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# Installation
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Yuanlong LIU