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Method for inferring path posterior probabilities and abundances from pangenome graph read alignments

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# rpvg

Method for inferring the expression of haplotype-specific transcript paths in a pantranscriptome using RNA-seq reads aligned to a spliced pangenome graph.

For each paired-end RNA-seq read mapped to a [vg](https://github.com/vgteam/vg) pangenome graph the probability of it originating from each transcript path is calculated from the mapping quality, mapping score and estimated fragment length distribution. These read-path probabilities are used to calculate posterior probabilities of haplotype combinations and infer transcript abundances.

### Installation

#### Linux binary

A static binary for Linux is available [here](https://github.com/jonassibbesen/rpvg/releases/latest) for the latest release.

#### Compilation

*rpvg* requires that [CMake](https://cmake.org) (3.10 or higher), [protobuf](https://github.com/protocolbuffers/protobuf) (version 3), [HTSlib](https://github.com/samtools/htslib), [Jansson](https://github.com/akheron/jansson) and OpenMP are installed before compilation. Most of these can easily be installed using *agt-get* on Linux (see Dockerfile) or [Homebrew](https://brew.sh) on Mac. To compile *rpvg* run the following:

1. `git clone --recursive https://github.com/jonassibbesen/rpvg.git`
2. `cd rpvg && mkdir build && cd build`
3. `cmake ..`
4. `make -j <threads>`

Compiling *rpvg* should take 5-10 minutes using 4 threads (`-j`). *rpvg* has been successfully built and tested on Linux (CentOS Linux 7 with GCC 8.1.0 and Ubuntu 18.04 with GCC 7.5.0) and Mac (macOS 10.14.6 with Clang 10.0.1 and macOS 13.2.1 with Clang 15.0.7).

#### Docker container

A Docker container of the latest commit to master is available [here](https://quay.io/repository/jonassibbesen/rpvg). On modern CPUs using the Docker container might be slower than compiling *rpvg* since it is not taking advantage of newer instructions in order for the container to be more compatible.

### Running rpvg

*rpvg* requires the following five arguments:

```
rpvg -g graph.xg -p paths.gbwt -a alignments.gamp -o rpvg_results -i <inference-model>
```

The prefix used for all output files are given using `-o`. The number of threads can be given using `-t`.

An example dataset containing a small pantranscriptome and 100,000 read pairs is available under [example](https://github.com/jonassibbesen/rpvg/tree/master/example). To infer the expression of the 36,120 haplotype-specific transcripts in the pantranscriptome using 4 threads use the following command within the *example* folder:

```
../bin/rpvg -t 4 -g graph.xg -p pantranscriptome.gbwt -f pantranscriptome.txt.gz -a mpmap_align.gamp -o rpvg --inference-model haplotype-transcripts
```

This should take less than a minute to run and will create two files:

* *rpvg.txt*: Contains the estimated marginal haplotype probability and expression value for each haplotype-specific transcript in the pantranscriptome.
* *rpvg_joint.txt*: Contains the estimated joint probability of each haplotype combination (e.g. diplotype) for each transcript in the pantranscriptome and the corresponding estimated haplotype-specific transcript expression values (only combinations with a probability at or above the precision threshold are written (see option --prob-precision)).

#### Paths:

The pantranscriptome paths should be compressed and indexed using the [GBWT](https://github.com/jltsiren/gbwt). For transcriptome analyses a GBWT with transcript paths can be created using the `vg rna` subcommand in [vg](https://github.com/vgteam/vg). See the [Transcriptomic analyses](https://github.com/vgteam/vg/wiki/Transcriptomic-analyses) wiki on the vg github for more information on how to use `vg rna`.

To decrease the computation time of *rpvg* it is recommended that a [r-index](https://github.com/jltsiren/gbwt/wiki/Fast-Locate) of the paths is supplied together with the GBWT index. The `vg gbwt` subcommand in vg can be used to construct the r-index from a GBWT index (see the [VG GBWT Subcommand](https://github.com/vgteam/vg/wiki/VG-GBWT-Subcommand) wiki on the vg github). The name of the r-index should be the same as the GBWT index with an added *.ri* extension (e.g. *paths.gbwt.ri*).

#### Inference models:

*rpvg* currently contains four different inference models. Each model have been written with a particular path type and corresponding inference problem in mind:

* `haplotypes`: Infers joint posterior probabilities of haplotype combinations (e.g. diplotypes). For diploid ploidy it uses a branch and bound like algorithm to infer the most probable combination of haplotypes. A faster, less accurate Gibbs sampling scheme can be enabled using `--use-hap-gibbs`, which scales much better for higher ploidies. The maximum ploidy can be given using `-y`.

* `transcripts`: Infers abundances using a Expectation Maximization (EM) algorithm. A file containing the transcript origin of each path in the pantranscriptome (`--write-info` output from `vg rna`) is needed to get transcript abundances if the pantranscriptome contains haplotype-specific transcripts. This can be given using `-f`. The haplotype probabilities are marginalized in the inference algorithm when this file is given.

* `strains`: Infers abundances using a combination of weighted minimum path cover and EM. **Note that this algorithm has not yet been properly evaluated**.

* `haplotype-transcripts`: Infers abundances using a combination of haplotype sampling and EM. The most probable haplotype combinations are inferred using the same algorithm as used in the `haplotypes` inference model. By default, equivalent haplotypes are inferred for all clustered transcripts, however independent inference of haplotypes for each transcript can be enabled using the `--ind-hap-inference` option. The inference algorithm requires a file (`-f`) containing the haplotype and transcript origin of each path in the pantranscriptome (`--write-info` output from `vg rna`).

#### Alignment types:

*rpvg* supports mapped reads (both primary and multi-mapping alignments) in the vg alignment format (.gam) and the [vg mpmap](https://github.com/vgteam/vg/wiki/Multipath-alignments-and-vg-mpmap) multipath alignment format (.gmap). Using multipath alignment from [vg mpmap](https://github.com/vgteam/vg/wiki/Multipath-alignments-and-vg-mpmap) is recommended.

* Use `-u` if the input alignments are single-path (*.gam*) format. Default is multipath alignments from [vg mpmap](https://github.com/vgteam/vg/wiki/Multipath-alignments-and-vg-mpmap).
* Use `-e` if the reads are from a strand-specific protocol. Supports the following library types: 
  * Use value `fr` when the first read is from the forward strand and the second read is from the reverse strand.
  * Use value `rf` when the first read is from the reverse stand and the second read is from the forward strand.
* Use `-s` for single-end reads. Note that the fragment length distribution will still be used for calculating the effective path length.
* Use `-l` for single-molecule long-reads. This is identical to the single-end mode (`-s`), but does not use effective path length normalization.

Note that *rpvg* assumes that the default scoring parameters were used for the alignment using either vg map or vg mpmap.

#### Fragment length distribution:

The fragment length distribution parameters are learned by *rpvg*. However, in order to learn this the maximum expected fragment length is needed. This is calculated from the expected fragment length distribution mean and standard deviation, which can be given using `-m` and `-d`, respectively. If these are not given the method will look for the parameters in the alignment file and pick the first values that it finds. The input parameters (`-m` and `-d`) are overwritten by the values estimated by *rpvg* when calculating the read-path probabilities. When the input is single-end reads (`-s`) the expected mean (`-m`) and standard deviation (`-d`) is required as it can not be estimated by *rpvg* and is needed for the effective path length calculation.

### Citing rpvg

Sibbesen, J. A., Eizenga, J. M. *et al.* Haplotype-aware pantranscriptome analyses using spliced pangenome graphs. [Nature Methods](https://doi.org/10.1038/s41592-022-01731-9) **20**, 239–247 (2023).

### Acknowledgement

We thank the developers of the [tools and libraries](https://github.com/jonassibbesen/rpvg/tree/master/deps) used by rpvg.

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