|
|
@@ -18,8 +18,7 @@ CALDER is a Hi-C analysis tool that allows: (1) compute chromatin domains from w
|
|
|
Due to reasons such as low data quality or large scale structrual variation, compartments can be unrealiablly called at one `bin_size` (equivalent to `resoltution` in the literature) but might be captured at another `bin_size`. We added an opitimized `bin_size` selection strategy to call reliable compartments. It is based on the observation from our large scale compartment analysis (https://www.nature.com/articles/s41467-021-22666-3) that, although compartments can change between different conditions, their overall correlation `cor(compartment_rank_1, compartment_rank_2)` is high (> 0.4).
|
|
|
<br>
|
|
|
<br>
|
|
|
-We define the consistency as , and choose the smallest `bin_size` such that no bigger `bin_size` can increase the consistency more than 0.05. For example, if consistency for `bin_size=10000` is 0.2 while for `bin_size=50000` is 0.6, we are more confident the latter is more reliable; if consistency for `bin_size=10000` is 0.5 while for `bin_size=50000` is 0.52, we would choose the former as it has higher resolution.
|
|
|
-Thus we will try mutiple `bin_sizes` and choose the compartments called at the smallest `bin_size` value thus no bigger `bin_size` has the
|
|
|
+Given a `bin_size` specified by user, we call compartment with extended `bin_sizes` and choose the smallest `bin_size` such that no bigger `bin_size` can increase the correclation with a reference compartment more than 0.05. For example, if correclation for `bin_size=10000` is 0.2 while for `bin_size=50000` is 0.6, we are more confident the latter is more reliable; if correclation for `bin_size=10000` is 0.5 while for `bin_size=50000` is 0.52, we would choose the former as it has higher resolution.
|
|
|
<br>
|
|
|
<br>
|
|
|
High quality compartment calls were generated for `hg19` (hic data from GSE63525), `hg38` (hic data from https://data.4dnucleome.org/files-processed/4DNFI1UEG1HD/), `mm9` (hic data from GSM3959427), `mm10` (hic data from http://hicfiles.s3.amazonaws.com/external/bonev/CN_mapq30.hic)
|
Yuanlong LIU