pwcoco

KO | EN

gitlite — search

#typescript #llm #go #android #rust #deep-learning #docker #macos #windows #awesome #cli #kubernetes #pytorch #open-source

(★ 43)

Pair-wise conditional analysis and colocalisation

pwcoco 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Pair-Wise Conditional analysis and Colocalisation analysis (PWCoCo)
A C++ implementation of the PWCoCo algorithm first described by Zheng, et al in their paper, [Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases](https://doi.org/10.1038/s41588-020-0682-6).

This tool integrates methods from [GCTA-COJO](https://cnsgenomics.com/software/gcta/#Overview) and the [coloc](https://chr1swallace.github.io/coloc/index.html) R package.

## Citation

Please cite our [pre-print](https://doi.org/10.1101/2022.08.08.503158)!

```
@article {Robinson2022.08.08.503158,
	author = {Robinson, Jamie W and Hemani, Gibran and Babaei, Mahsa Sheikhali and Huang, Yunfeng and Baird, Denis A and Tsai, Ellen A and Chen, Chia-Yen and Gaunt, Tom R and Zheng, Jie},
	title = {An efficient and robust tool for colocalisation: Pair-wise Conditional and Colocalisation (PWCoCo)},
	elocation-id = {2022.08.08.503158},
	year = {2022},
	doi = {10.1101/2022.08.08.503158},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2022/08/08/2022.08.08.503158},
	eprint = {https://www.biorxiv.org/content/early/2022/08/08/2022.08.08.503158.full.pdf},
	journal = {bioRxiv}
}
```

## Requirements
- C++17 or newer
- Cmake >= 3.10

Additional Libraries:
- Eigen >= 3.3.7
- spdlog >= 1.8.1

These additional libraries are bundled in `/include/` at the required versions for ease of the user.

## How to Build
Currently, only Unix and Windows are supported.

### Unix
To build on Unix systems, clone this repository and follow the code below:
```
mkdir build
cd build
cmake ..
make
```

If building on the University of Bristol's HPC, load the module `languages/gcc-9.1.0` and ensure this is the **only** gcc module loaded. Also, if you do not have a cmake module loaded, please load, for example, `tools/cmake-3.13.4`. This should be all you need to build the program.

### Windows

A .sln file is provided for Visual Studio 2019. Be aware OpenMP may not be enabled by default on VS and may need to be enabled manually.

## How to Use
PWCoCo is a command-line program. Here is a list of accepted flags with a description of each one:

**Required**
- `--bfile` - specifies the location of the reference dataset, normally from Plink, in the bed/bim/fam formats. Each of the bed/bim/fam files should have the same name and in the same directory.
- `--sum_stats1` - first file or folder containing summary statistics. Please see below "Input" section.
- `--sum_stats2` - second file or folder containing summary statistics. Please see below "Input" section.

For acceptable formats for these files, please see below.

**Optional**
- `--log` - specifies log name, default is "pwcoco_log.txt" and will save in the same folder from where the program is run.
- `--out` - prefix for the result files, default is "pwcoco_out".
- `--p_cutoff` - P value cutoff for SNPs to be selected by the stepwise selection process, default is 5e-8. Alternatively, the flags `--p_cutoff1` and `--p_cutoff2` may be used to specify dataset-specific P value cutoffs, relative to the order of the data given in the `--sum_stats` flags.
- `--chr` - when reading the reference files, the program will limit the analysis to those SNPs on this chromosome. 
- `--top_snp` - maximum number of SNPs that may be selected by the stepwise selection process, default is 1e10, i.e. a lot.
- `--ld_window` - distance (in kb) that, when exceeded, is assumed for SNPs to be in total LE, default is 1e7.
- `--collinear` - threshold that, when exceeded, determines if SNPs are collinear, default is 0.9.
- `--maf` - filters SNPs from the reference dataset according to this threshold, default is 0.1.
- `--freq_threshold` - SNPs in the phenotype datasets which differ by more than this amount in the reference dataset will be excluded, default is 0.2.
- `--init_h4` - PWCoCo will run an initial colocalisation on the unconditioned dataset. If the H4 for this analysis reaches this threshold, the program will terminate early. Default is 80 (i.e. 80%). Set to 0 if you would like the program to always continue regardless of the initial colocalisation result.
- `--out_cond` - would you like for the conditioned data to be saved as text files as well? Just including this flag will work (no extra argument following this flag is necessary).
- `--coloc_pp` - specify the three prior probability Ps: the next **three** arguments must be the P values, default is 1e-4, 1e-4 and 1e-5.
- `--n1` - also `--n2`, specify the sample size (see also next flag) for the corresponding summary statistics. 
- `--n1_case` - also `--n2_case`, specify the number of cases for the corresponding summary statistics.
- `--threads` - sets number of threads available for OpenMP multi-threaded functions, default is 8.
- `--verbose` - if this flag is given, PWCoCo will output files which can be used for debugging purposes. These files include SNPs which did not match the allele frequency given in the reference data and included SNPs within the analysis. Also sets `--out_cond` flag. (No extra argument following this flag is necessary).

PWCoCo makes use of OpenMP to parallelise some tasks. This can greatly increase the performance of the tool and decrease the time required to run. It is advisable to use a compiler that utilises OpenMP version 3.0 (which is sadly not yet supported by Visual Studio). Furthermore, allowing the tool to make use of more threads should improve performance, especially with regards to the reference data loading. The reference panel loading and operations are the most intensive in the tool, so larger panels will require longer to parse -- in these instances, it would be preferable to use more threads so that performance is not greatly impacted.

## Example
Please see the [Wiki](https://github.com/jwr-git/pwcoco/wiki) for an example. The files to run this example are provided in `/data/`.

### Input
The reference files must be in Plink format (specified using the `--bfile` flag). This means a .bed, .bim and .fam file in the same directory with the same name. Including the file ending is not required for PWCoCo to access these.

There are two options and cases for the user as to how they provide their summary statistic files to the program. The `--sum_stats` flags can take a path to either a folder or a file. Both cases are explained below. In both cases, file endings do not particularly matter (so long as they are readable by PWCoCo) and delimiter also does not particularly matter (PWCoCo will attempt to determine the delimiter between tabs, commas or spaces).

#### Case 1 - Few analyses

If only a few analyses are required to be run (< 100, for example) then it is more efficient to run PWCoCo separately for each of the file pairs (e.g. exposure vs outcome). In that case, using the `--sum_stats1` and `--sum_stats2` flags to point to the summary statistic files is better, as it allows the user to specify flags which will speed up the reference data loading (e.g. `--chr`).

#### Case 2 - Many analyses - **BETA FEATURE**

If PWCoCo will be analysing many different datasets (> 100, for example) then it may be more efficient to point the `--sum_stats` flags to two separate folder locations (e.g. one folder for exposure data, another for outcome data). In this case, PWCoCo will look for files _with the same names and file endings_ in the two folders and use these as the first and second summary statistic files. If this is done, PWCoCo will load the entire reference panel into memory (and therefore will require a large amount of RAM) to use between analyses.

**This is a beta feature and may still be buggy. Please do not use this feature to run real analyses, and report any bugs you may experience.**

#### Notes and Tips for Efficiency

By far and away the slowest part of the program is loading, cleaning and preparing the reference data. Furthermore, as more samples or SNPs are added to the reference, so too does the complexity of the program increase. Therefore, PWCoCo offers the option to retain the reference data in memory between analyses, which, when conducting many analyses, can save time. However, even then, analyses may run slowly in which case we advise to bear in mind the following points:
- Consider splitting the reference panel into smaller regions more relevant for the analyses (using the `--chr` flag will reduce the memory footprint of the program but the entire .bim file will _still_ be required to be parsed. Therefore, splitting the reference data on chromosomes may actually increase performance).
- Group similar analyses using a custom loop. Combining this with split reference panels will also greatly increase overall performance.
- Consider using Plink to reduce the size of the reference panel to the region, or even SNPs, of interest. Plink will always be more efficient that PWCoCo at dealing with reference data in this way and so would increase performance for many anaylses.
- Increase the amount of threads available for the OpenMP functions using the `--threads` flag.

At the end of the day, PWCoCo is still built with performance and efficiency in mind and so will be more performant than other, similar methods; however, these steps should be in the mind of the user who wishes to conduct potentially thousands or more analyses to squeeze even more efficiency out of the tool.

#### Input File Formats
Phenotype files do not require a certain file format. Instead, they _must_ follow this structure:

`SNP	A1	A2	A1_freq	beta	se	p	{n	{case}}`

Column names do not matter, only the order of the data. The `n` and `case` columns are optional and should be given for phenotypes which are measured in case/control studies - where `n` will be the total sample size and `case` only case numbers. Including this column will cause the colocalisation to treat this as `cc` typed data (and not `quant` which only has `n`, total sample size, available). These may also be provided through the command line arguments.

### Output File Formats
The program by default will output a file with the ending `.coloc` which contains the results for each of the colocalisation analyses run:

`Dataset1	Dataset2	SNP1	SNP2	nsnps	H0	H1	H2	H3	H4	log_abf_all`

If the data has been unconditioned, then the SNP column will contain "unconditioned" instead of a SNP name. Please note that output files **are not** deleted or overwritten between runs. That means if you run the program twice with the same output file name, results will be appended to the output file. SNPs correspond to the same numbered phenotype file, e.g. SNP1 comes from sum_stats1.

The program will also output two extra files (ending in `.included`) listing the SNPs included in the analysis from each dataset. Finally, if the `out_cond` flag is set to true, the program will output the conditioned results after the conditional analysis in files ending with `.cojo`:

`Chr	SNP	bp	refA	freq	b	se	p	n	freq_geno	bC	bC_se	pC	rs12345`

The columns `freq`, `b`, `se` and `p` should be unaltered from the original dataset. The  columns postfixed "C" will be post-conditional analysis.  The final column will be named after the lead SNP of the association signal and will show the LD (r2) between the column SNP and the row SNP. The lead SNP will also be used in the name of the file to differentiate it from other conditional files from the same dataset.

## To Do
- Provide example files.
- Improve output.

pwcoco

pwcoco 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

pwcoco

pwcoco 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README