https://github.com/ctlab/samovar

Algorithms package for generating model metagenomes with specified properties
https://github.com/ctlab/samovar

artificial-data metagenomics

Last synced: 8 months ago
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Algorithms package for generating model metagenomes with specified properties

Host: GitHub
URL: https://github.com/ctlab/samovar
Owner: ctlab
License: mit
Created: 2024-01-11T13:02:31.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2025-06-07T13:12:21.000Z (about 1 year ago)
Last Synced: 2025-06-07T14:20:55.885Z (about 1 year ago)
Topics: artificial-data, metagenomics
Language: Python
Homepage:
Size: 34.6 MB
Stars: 5
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE.md

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README

# SamovaR
### Automated re-profiling & benchmarking of metagenomic tools based on artificial data generation

[![R package](https://github.com/ctlab/samovar/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ctlab/samovar/actions/workflows/R-CMD-check.yaml)
[![python package](https://github.com/ctlab/samovar/actions/workflows/python-package.yml/badge.svg)](https://github.com/ctlab/samovar/actions/workflows/python-package.yaml)

There is a fundamental problem in modern ***metagenomics***: there are huge differences between methodological approaches that strongly influence the results, while remaining outside the attention of researchers.

The use of golden practice and open code, while allowing data to be analyzed reproducibly, locks scientists into a single, far from perfect approach, with its own bias.

Therefore, we propose an approach that utilizes de novo generation of the artificial metagenomes - `SamovaR`.

## Installation

### Quick Installation

Warning: beta

Use installation script:

```bash
git clone https://github.com/ctlab/samovar
cd samovar
chmod +x install.sh
./install.sh
```

***Attention**: the script automatically detects custom R library paths from `.Renviron` (R_LIBS) or `.Rprofile` (libPaths())*

### Manual Installation

Install **R** package:

```r
devtools::install_github("https://github.com/ctlab/samovar/")
```

***Attention:*** *check that samovar can be loaded with* ```Rscript -e 'library(samovar)'```, *especially in case of several R versions installed*

Install **python** package:

```bash
git clone https://github.com/ctlab/samovar
cd samovar
pip install -e .
```
***Attention:*** *most samovar usage require properly configurated file in build/config.json*

## Usage
### Cross-validation and re-profiling

Example usage:
```bash
# Generate reads for benchmarking (skip for real data)
samovar generate \
--genome_dir $SAMOVAR/data/test_genomes/meta \
--host_genome $SAMOVAR/data/test_genomes/host/9606.fna \
--output_dir samovar

# Generate pipeline (for example, kraken2 + kaiju )
## specify --input_dir for real data
samovar preprocess \
--output_dir samovar \
--kraken2-test "kraken2 $DB_KRAKEN2" \
--kaiju-test "kaiju $DB_KAIJU"

# Run the pipeline(s)
samovar exec --output-dir samovar
```

Results and flexibility of the tool can be improved with specification of config files. Please folow wiki, or see {samovar_function} -h

Manual example:
```bash
cd samovar
bash workflow/pipeline.sh
```

subgraph Processing
Metagenomes --> C[Initial annotation]
A3 --> C
A3 --> F
A3 --> E[Metagenome generation]
C --> E
E --> F[Re-annotation]
end

subgraph Results
F --> G1[Annotators scores]
F --> ML
subgraph Re-profiling
C --> R
ML --> R[Corrected results]
end
C --> C1[Cross-validation]
end

style Input fill:#90ee9020,stroke:#333,stroke-width:2px
style Metagenomes fill:#b2ee9020,stroke:#333,stroke-width:2px
style Processing fill:#ee90bf20,stroke:#333,stroke-width:2px
style Results fill:#90d8ee20,stroke:#333,stroke-width:2px
style Re-profiling fill:#90a4ee20,stroke:#333,stroke-width:2px
```

### Artificial metagenome reneration
Basic usage described in **vignettes** and **wiki**

You can also try the generator with **web** shiny app

#### R generation

See description or source a vignette

``` r
library(samovaR)

# download data
teatree <- GMrepo_type2data(number_to_process = 2000)

# filter
tealeaves <- teatree %>%
teatree_trim(treshhold_species = 3, treshhold_samples = 3, treshhold_amount = 10^(-3))

# normalizing
teabag <- tealeaves %>%
tealeaves_pack()

# clustering
concotion <- teabag %>%
teabag_brew(min_cluster_size = 4, max_cluster_size = 6)

# building samovar
samovar <- concotion %>%
concotion_pour()

# generating new data
new_data <- samovar %>%
samovar_boil(n = 100)
```

Documentation for the **R package**

#### Pipeline

## Components

- **R** package `samova.R` for the artificial abundance table generation
- Pipeline for the automated benchmarking and re-profiling

## Project Structure

B --> B1[R/]
B --> B2[man/]
B --> B3[vignettes/]

C --> C1[workflow/]
C --> C2[src/]

G --> H[shiny/]
```

## References
- Chechenina А., Vaulin N., Ivanov A., Ulyantsev V. Development of in-silico models of metagenomic communities with given properties and a pipeline for their generation. Bioinformatics Institute 2022/23 URL: https://elibrary.ru/item.asp?id=60029330

## Dependencies

```mermaid
%%{init: {'theme': 'base', 'themeVariables': { 'fontSize': '16px', 'fontFamily': 'arial', 'primaryColor': '#fff', 'primaryTextColor': '#000', 'primaryBorderColor': '#000', 'lineColor': '#000', 'secondaryColor': '#fff', 'tertiaryColor': '#fff'}}}%%
graph LR
subgraph "R Package Dependencies"
subgraph "Main"
direction LR
tidyverse
scclust
Matrix
methods
end

subgraph "Visualization"
direction LR
ggplot
plotly
ggnewscale
end

subgraph "API"
direction LR
httr
jsonlite
xml2
end
end

subgraph "Automated Benchmarking"
subgraph "Major"
direction LR
samova.R
R::yaml
SnakeMake
InSilicoSeq
end

subgraph "Python packages"
direction LR
numpy
pandas
requests
ete3
scikit-learn
end
end

linkStyle default stroke:#000
```

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https://github.com/ctlab/samovar

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