# CALDER user manuel

CALDER is a Hi-C analysis tool that allows: (1) compute chromatin domains from whole chromosome contacts; (2) derive their non-linear hierarchical organization and obtain sub-compartments; (3) compute nested sub-domains within each chromatin domain from short-range contacts. CALDER is currently implemented in R.


![Alt text](./img/CALDER_methods.png "CALDER methods")

## We added multiple new features in version 2.0:

* Support for other genomes
* Support hic format generated by Juicer tools (https://github.com/aidenlab/juicer)
* Opimized resolution selection
* 


Although compartments can change between different cell conditions, from our large scale analysis we found the overall correlation between two conditions are high. We input compartment of reference dataset. When the computed compartment has low correlation with this reference, it indicates false calling.



# Installation


## Make sure all dependencies have been installed:

* R.utils (>= 2.9.0),
* doParallel (>= 1.0.15),
* ape (>= 5.3),
* dendextend (>= 1.12.0),
* fitdistrplus (>= 1.0.14),
* igraph (>= 1.2.4.1),
* Matrix (>= 1.2.17),
* rARPACK (>= 0.11.0),
* factoextra (>= 1.0.5),
* maptools (>= 0.9.5),
* data.table (>= 1.12.2),
* fields (>= 9.8.3),
* GenomicRanges (>= 1.36.0)
* ggplot2 (>= 3.3.5)
* strawr (>= 0.0.9)

## Clone its repository and install it from source:

`git clone https://github.com/CSOgroup/CALDER.git`

`install.packages(path_to_CALDER, repos = NULL, type="source")` ## install from the cloned source file


Please contact yliueagle@googlemail.com for any questions about installation.

## install CALDER and dependencies automaticly:

```
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GenomicRanges")
install.packages("remotes")
remotes::install_github("CSOgroup/CALDER")
```

# Usage

The input data of CALDER is a three-column text file storing the contact table of a full chromosome (zipped format is acceptable, as long as it can be read by `data.table::fread`). Each row represents a contact record `pos_x, pos_y, contact_value`, which is the same format as that generated by the `dump` command of juicer (https://github.com/aidenlab/juicer/wiki/Data-Extraction):	

	16050000	16050000	10106.306
	16050000	16060000	2259.247
	16060000	16060000	7748.551
	16050000	16070000	1251.3663
	16060000	16070000	4456.1245
	16070000	16070000	4211.7393
	16050000	16080000	522.0705
	16060000	16080000	983.1761
	16070000	16080000	1996.749
	...

A demo dataset is included in the repository `CALDER/inst/extdata/mat_chr22_10kb_ob.txt.gz` and can be accessed by `system.file("extdata", "mat_chr22_10kb_ob.txt.gz", package='CALDER')` once CALDER is installed. This data contains contact values of GM12878 on chr22 binned at 10kb (Rao et al. 2014) 

CALDER contains three modules: (1) compute chromatin domains; (2) derive their hierarchical organization and obtain sub-compartments; (3) compute nested sub-domains within each compartment domain.

### To run three modules in a single step:
```
CALDER_main(contact_mat_file, 
			chr, 
			bin_size, 
			out_dir, 
			sub_domains=TRUE, 
			save_intermediate_data=FALSE,
			genome='hg19')
```

### To run three modules in seperated steps:
```
# This will not compute sub-domains, but save the intermediate_data that can be used to compute sub-domains latter on
CALDER_main(contact_mat_file, 
			chr, 
			bin_size, 
			out_dir, 
			sub_domains=FALSE, 
			save_intermediate_data=TRUE,
			genome='hg19') 

# (optional depends on needs) Compute sub-domains using intermediate_data_file that was previous saved in the out_dir (named as chrxx_intermediate_data.Rds)
CALDER_sub_domains(intermediate_data_file, 
				   chr, 
				   out_dir, 
				   bin_size) 
```

### Paramters:

* `contact_mat_file`: path to the contact table of a chromosome
* `chr`: chromosome number. Either numeric or character, will be pasted to the output name
* `bin_size`: numeric, the size of a bin in consistent with the contact table
* `out_dir`: the output directory
* `sub_domains`: logical, whether to compute nested sub-domains
* `save_intermediate_data`: logical. If TRUE, an intermediate_data will be saved. This file can be used for computing nested sub-domains later on
* `genome`: string. Specifies the genome assembly (Default="hg19").

### Output:

#### chrxx_domain_hierachy.tsv
* information of compartment domain and their hierarchical organization. The hierarchical structure is fully represented by `compartment_label`, for example, `B.2.2.2` and `B.2.2.1` are two sub-branches of `B.2.2`. The `pos_end` column specifies all compartment domain borders, except when it is marked as `gap`, which indicates it is the border of a gap chromsome region that has too few contacts and was excluded from the analysis (e.g., due to low mappability, deletion, technique flaw) 

#### chrxx_sub_compartments.bed
* a .bed file containing the sub-compartment information, that can be visualized in IGV. Different colors were used to distinguish compartments (at the resolution of 8 sub-compartments)  

#### chrxx_domain_boundaries.bed
* a .bed file containing the chromatin domains boundaries, that can be visualized in IGV

#### chrxx_nested_boundaries.bed
* a .bed file containing the nested sub-domain boundaries, that can be visualized in IGV

#### chrxx_intermediate_data.Rds
* an Rds file storing the intermediate_data that can be used to compute nested sub-domains (if CALDER is run in two seperated steps)

#### chrxx_log.txt, chrxx_sub_domains_log.txt
* log file storing the status and running time of each step



### Runnig time:
For the computational requirement, running CALDER on the GM12878 Hi-C dataset at bin size of 40kb took **36 minutes** to derive the chromatin domains and their hierarchy for all chromosomes (i.e., CALDER Step1 and Step2); **13 minutes** to derive the nested sub-domains (i.e., CALDER Step3). At the bin size of 10kb, it took **1 h 44 minutes and 55 minutes** correspondingly (server information: 40 cores, 64GB Ram, Intel(R) Xeon(R) Silver 4210 CPU @ 2.20GHz). The evaluation was done using a single core although CALDER can be run in a parallel manner.

### Demo run:

```
library(CALDER)

contact_mat_file = system.file("extdata", "mat_chr22_10kb_ob.txt.gz", package = 'CALDER')

CALDER_main(contact_mat_file, chr=22, bin_size=10E3, out_dir='./GM12878', sub_domains=TRUE, save_intermediate_data=FALSE)
```

The saved .bed files can be view directly through IGV:

![Alt text](./img/IGV_results.png "IGV")

# Citation

If you use CALDER in your work, please cite: https://www.nature.com/articles/s41467-021-22666-3


# Contact information

* Author: Yuanlong LIU
* Affiliation: Computational Systems Oncology group, Department of Computational Biology, University of Lausanne, Switzerland
* Email: yliueagle@googlemail.com