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Fast inference of fine-scale recombination rates based on fused-LASSO

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# pyrho

Fast demography-aware inference of fine-scale recombination rates based on
fused-LASSO

## Table of Contents
* [Human Recombination Maps](#human-recombination-maps)
* [Human Population Sizes](#human-population-sizes)
* [Installation](#installation)
* [Usage](#usage)
    * [make_table](#make_table)
    * [hyperparam](#hyperparam)
    * [optimize](#optimize)
    * [compute_r2](#compute_r2)
* [Example](#example)
* [FAQ](#faq)
* [Citation](#citation)

NB: in version 0.1.0 we switched everything from coalescent units to
natural scale -- that is inputs should now be in terms of Ne, generations,
and per-base, per-generation mutation rates.  The output is now also
automatically scaled to be the per-generation recombination rate.

## Human Recombination Maps

We have inferred recombination maps for each of the 26 populations in phase 3
of the 1000 Genomes Project.  Those maps are available
[at this link](https://zenodo.org/records/11437540)
including maps inferred for the two most recent genome builds
hg19/GRCh37 and hg38/GRCh38.

The recombination maps for hg38/GRCh38 are now also available for simulations using
the wonderful [stdpopsim](https://github.com/popsim-consortium/stdpopsim) package.

## Human Population Sizes

When making the recombination maps for the 1000 Genomes Project populations we used
[smc++](https://github.com/popgenmethods/smcpp)
to infer population size histories for each population.
Those size histories are plotted in
[Figure 2 of the pyrho paper](https://advances.sciencemag.org/content/5/10/eaaw9206/),
and the data used to make those plots are available in `smcpp_popsizes_1kg.csv`.
The `x` column is time in years
assuming a generation time of 29 years,
and the `y` column
contains the population size in Ne.

Installation
------------

pyrho is compatible with both python 2 and python 3.
pyrho makes use of a number of external packages including the excellent
[numba](https://github.com/numba/numba),
[msprime](https://github.com/tskit-dev/msprime),
and
[cyvcf2](https://github.com/brentp/cyvcf2)
packages.  As such it is recommended to install pyrho in a
virtual environment.

If using
[conda](https://conda.io/en/master/)
this can be accomplished by running

```
conda create -n my-pyrho-env
conda activate my-pyrho-env
```

or using

```
virtualenv my-pyrho-env
source my-pyrho-env/bin/activate
```

Once you have set up and activated your virtual environment, you must
first install
[ldpop](https://github.com/popgenmethods/ldpop).
Change to a directory where you want to store ldpop and then run

```
git clone https://github.com/popgenmethods/ldpop.git ldpop
pip install ldpop/
```

Note that this will create a directory ldpop.

pyrho makes use of
[msprime](https://github.com/tskit-dev/msprime),
which requires
[gsl](https://www.gnu.org/software/gsl/)
and
[hdf](https://www.hdfgroup.org).
pyrho also has a dependency on
[openssl](https://www.openssl.org).
If you do not have these installed, these can be installed using
`apt-get`, `yum`, `conda`, `brew` etc...

For example, to install openssl on Ubuntu run:

```
sudo apt-get install libssl-dev
```

You will also need to have cython and pytables.  If you do not yet have them
installed, run

```
pip install cython pytables
```

You should be able to then just clone and install pyrho by running

```
git clone https://github.com/popgenmethods/pyrho.git pyrho
pip install pyrho/
```

You can check that 
everything is running smoothly by running

```python -m pytest pyrho/tests/tests.py```

NB: the first time you run pyrho, numba will compile and cache a number
of functions, which can take up to ~30 seconds.

Usage
-----

pyrho has a command line interface and consists of a number of separate
commands. A typical workflow is to first use
[make_table](#make_table)
to build a lookup table and then use
[hyperparam](#hyperparam)
to find reasonable hyperparameter settings for your data. Finally, use
[optimize](#optimize)
to infer a fine-scale recombination map from data. There is also a command,
[compute_r2](#compute_r2),
that computes statistics of the theoretical distribution of r2.

### make_table

Before performing any inference, you must first compute a lookup table.  A
standard use case would be

```
pyrho make_table --samplesize <n> --approx --moran_pop_size <N> \
--numthreads <par> --mu <mu> --outfile <outfile> \
--popsizes <size1>,<size2>,<size3> --epochtimes <breakpoint1>,<breakpoint2>
```

which indicates that we should compute a lookup table for a sample of size
```<n>```
, from a population where at present the effective size _in number of diploids_ is 
```<size1>```
, at 
```<breakpoint1>```
generations in the past the size was 
```<size2>```
diploids and so on, with a per-generation
mutation rate 
```<mu>```
The ```--numthreads``` option tells pyrho to use 
```<par>```
processors
when computing the lookup table. Finally, ```--approx``` with ```--moran_pop_size```
tells pyrho to compute an approximate lookup table for a larger sample size
```<N>```
and then downsample to a table for size
```<n>```
. In general, we have found that while setting 
```<N>```
about 25-50% larger than
```<n>```
results in accuracy indistinguishable from using the exact model,
this added accuracy does not generally impact the inferred recombination rates.
As such, we recommend setting ```<N>``` equal to ```<n>```.
Without using the --approx flag, pyrho can
compute lookup tables for 
```<n>```
< ~50, whereas with the --approx flag, pyrho
can handle sample sizes in the hundreds (e.g., 
```<n>```
= 200,
```<N>```
= 200) with little loss in accuracy for the inferred recombination rates.
If the two-locus likelihoods need to be extremely accurate, then it might be
preferable to set e.g., ```<n>``` = 200 and ```<N>``` = 256.

In ```v0.2.0``` we altered the default behavior in computing likelihoods,
using a slightly more lax convergence criteria in the numerics.  This results
in significantly faster runtimes at essentially no loss in accuracy,
but if highly accurate two-locus likelihoods are required, the old behavior
can be recovered using ```--high_accuracy_stationary```.

The output is an hdf format table containing all of the pre-computed
likelihoods needed to run [hyperparam](#hyperparam), [optimize](#optimize),
and [compute_r2](#compute_r2).

Note that make_table used to conssume significant amounts of memory, but that
has been improved, e.g., N=210 requires
about 20G of RAM using the ```--approx``` flag.

To see a full list of options and their meaning, run 
```pyrho make_table --help```.

### hyperparam

After computing a lookup table with
[make_table](#make_table),
it is a good idea to find reasonable settings for the main hyperparameters
of pyrho: the window size and the smoothness penalty.  This command
simulates data and performs optimization under a number of different
hyperparameter settings and outputs the accuracy in terms of a number of
different metrics.  A typical usage is

```
pyrho hyperparam --samplesize <n> --tablefile <make_table_output> \
--mu <mu> --ploidy <ploidy> \
--popsizes <size1>,<size2>,<size3> --epochtimes <breakpoint1>,<breakpoint2> \
--outfile <output_file>
```

where ```<n>``` is your haploid sample size, ```<make_table_output>``` is
the output of running [make_table](#make_table), and
```<mu>```, ```<size1>```..., and ```<breakpoint1>```... are as above. 
```<ploidy>``` should be set to ```1``` if using phased data and ```2``` for
unphased genotype data. Ploidies other than ```1``` or ```2``` are not
currently supported.

The output is a table containing various measures of accuracy of the inferred
maps compared to the recombination maps used in the simulations (drawn from
the [hapmap](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1880871/) recombination map).  The optimal hyperparameters will
depend on your application -- it may be important to maximize a particular
measure of correlation or to minimize the L2 norm between the true and
inferred maps. In any case, you can use the results in the output table to
choose the hyperparameters for running [optimize](#optimize).

To see a full list of options and their meaning, run 
```pyrho hyperparam --help```.

### optimize

After computing a lookup table using [make_table](#make_table) and choosing
reasonable hyperparameters (optionally using [hyperparam](#hyperparam)) you
are ready to infer recombination maps from real data.

pyrho supports data in fasta format and LDhat's sites and locs format, but it
is easiest to directly use VCF formatted data.  pyrho supports VCF, bgzipped
VCF, and BCF format files. If using LDhat's formats see the 
[note about using sites and locs](#a-note-about-ldhat-format-input).

A typical usage is

```
pyrho optimize --vcffile <data> --windowsize <w> --blockpenalty <bpen> \
--tablefile <make_table_output> --ploidy <ploidy> --outfile <output_file> \
--numthreads <par>
```

with ```<data>``` being a VCF, bgzipped VCF, or BCF file containing a
single chromosome, ```<w>``` and
```<bpen>``` being hyperparameters chosen using [hyperparam](#hyperparam),
and ```<ploidy>``` should be ```1``` for phased data and ```2``` for unphased
data.

The output file has three columns -- the first column is the zero-indexed
start of an interval, the second column is the end of the interval
(non-inclusive), and the third column is r, which is the per-base
per-generation recombination rate in that interval.

To see a full list of options and their meaning, run
```pyrho optimize --help```.

pyrho optimize can be slow if there is a lot of missing data.
Consider removing SNPs with more than a few missing genotypes
to increase the speed.  Another option is to use the
`--fast_missing` option.  This option uses a bit more memory,
and throws away some data, but can be substantially faster
for datasets with lots of missing data, and does not require
pre-filtering SNPs for missingness.

#### A note about LDhat format input

The preferred input format for pyrho is a VCF format file containing a single
chrmosome.  LDhat format files may also be used with the following important
caveat.

If using LDhat formatted data (i.e., a sites file and a locs file) note that
we use a slighly different convention than LDhat (sorry!).  For unphased data,
LDhat uses the convention 0 = homozygous reference, 1 = homozygous alternate,
2 = heterozygous.  We do *not* use this convention.

We use 0 = homozygous reference, 1 = heterozygous, 2 = homozygous alternate,
and N = missing.  That is, each entry should be the number of alternate
alleles, or N for missing.
We otherwise follow the formatting (including the headers) of LDhat as
described [here](http://ldhat.sourceforge.net).

### compute_r2

[compute_r2](#compute_r2) computes statistics of the theoretical distribution
of r2 using a lookup table generated using
[make_table](#make_table). In particular, it can compute the mean and/or
quantiles of the distribution of r2.

A typical usage is

```
pyrho compute_r2 --tablefile <make_table_output> --samplesize <n> \
--quantiles 0.25,0.5,0.75 --compute_mean
```
which will compute the 25th, 50th, and 75th percentiles as well as the mean
of the distribution of r2 for the lookup table stored in
```<make_table_output>```. It is possible to compute statistics for a smaller
sample size by setting ```<n>``` to be less than the sample size for which
```<make_table_output>``` was computed.

Example
-------

The example folder contains a well-commented shell script example.sh which
runs through a typical use-case, taking a VCF file and the output of smc++
and ultimately computing a fine-scale recombination map.

FAQ
-------

#### Does `pyrho` return the population-scaled recombination rate "rho" or the per-generation recombination rate "r"?

Short answer: `pyrho` infers the per-generation, per-base recombination rate "r".

Long answer: internally `pyrho` infers "rho" and then uses the user-specified Ne to
convert "rho" to "r".  This scaling works well in humans, but has not been tested
extensively in species with different mutation rates or ratios of mutation rate
to recombination rate.  Overall, differences across the genome will be stable (i.e.,
relative rates) but the absolute scaling should be taken with some degree of
skepticism, especially when working with species whose evolutionary parameters
differ from those of humans.

#### Help!  `pyrho optimize` has been running for 3 days and is not done yet!

Missing data makes the default implementation of `pyrho optimize` run __very__ slowly.
To get reasonable runtimes either remove sites with more that a few missing genotypes
or use the `--fast-missing` flag.  The `--fast-missing` flag causes `pyrho` to only
use individuals who are genotyped at both loci when calculating two-locus likelihoods.
This effectively throws away the information provided by individuals who are genotyped
at only one of the two loci.  This information is not particularly useful for inferring
recombination rates, and ignoring it can result in a significant speedup in the
presence of missing data.

Citation
--------

If you use pyrho please cite

[Spence, J.P. and Song, Y.S. Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations. Science Advances, Vol. 5, No. 10, eaaw9206 (2019).](https://doi.org/10.1126/sciadv.aaw9206)

and

[Kamm, J.A.\*, Spence, J.P.\*, Chan, J., and Song, Y.S. Two-locus likelihoods under variable population size and fine-scale recombination rate estimation. Genetics, Vol. 203 No. 3 (2016) 1381-1399.](http://www.genetics.org/content/203/3/1381)

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