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# Asaru Sim Documentation
<a href="images/asarusim_v2.svg"><img src="images/asarusim_v2.svg" align="middle" height="100" width="290" >

`AsaruSim` is an automated Nextflow workflow designed for simulating 10x single-cell Nanopore reads. This workflow aims to generate a gold standard dataset for the objective assessment and optimization of single-cell long-read methods.
`AsaruSim` AsaruSim is an automated Nextflow workflow designed for simulating 10x single-cell long read data from the count matrix level to the sequence level. It aimed at creating a gold standard dataset for the assessment and optimization of single-cell long-read methods.
Full [documentation](https://GenomiqueENS.github.io/AsaruSim/) is avialable [here](https://GenomiqueENS.github.io/AsaruSim/).

<a href="images/schema.png"><img src="images/schema.png" align="middle" height="650" width="920" >
## Prerequisites

## Requirements

This pipeline is powered by Nextflow workflow manager. All dependencies are automatically managed by Nextflow through a preconfigured Docker container, ensuring a seamless and reproducible installation process.

Before starting, ensure the following tools are installed and properly set up on your system:

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Here are the primary input parameters for configuring the workflow:

| Parameter | Description | Default Value |
|--------------------|---------------------------------------------------------------|-----------------------------------------------|
| `matrix` | Path to the count matrix csv file (required) | `test_data/matrix.csv` |
| `bc_counts` | Path to the barcode count file | `test_data/test_bc.csv` |
| `transcriptome` | Path to the reference transcriptome file (required) | `test_data/transcriptome.fa` |
| `features` | Matrix feature counts | `transcript_id` |
| `gtf` | Path to transcriptom annotation .gtf file | `null` |
| `cell_types_annotation` | Path to cell type annotation .csv file | `null` |
| Parameter | Description | Format | Default Value |
|--------------------|-----------------------------------------------------|----------|-----------------------------------------------|
| `matrix` | Path to the count matrix csv file (required) | .CSV | `test_data/matrix.csv` |
| `bc_counts` | Path to the barcode count file | .CSV | `test_data/test_bc.csv` |
| `transcriptome` | Path to the reference transcriptome file (required) | FASTA | `test_data/transcriptome.fa` |
| `features` | Matrix feature counts | STR | `transcript_id` |
| `gtf` | Path to transcriptom annotation .gtf file | GTF | `null` |
| `cell_types_annotation` | Path to cell type annotation .csv file | CSV | `null` |

### Error/Qscore Parameters

Configuration for error model:

| Parameter | Description | Default Value |
|--------------------|---------------------------------------------------------------|-----------------------------------------------|
| `trained_model` | Badread pre-trained error/Qscore model name | `nanopore2023` |
| `badread_identity` | Comma-separated values for Badread identity parameters | `"98,2,99"` |
| `error_model` | Custom error model file (optional) | `null` |
| `qscore_model` | Custom Q-score model file (optional) | `null` |
| `build_model` | to build your own error/Qscor model | `false` |
| `fastq_model` | reference real read (.fastq) to train error model (optional) | `false` |
| `ref_genome` | reference genome .fasta file (optional) | `false` |
| Parameter | Description | format | Default Value |
|--------------------|---------------------------------------------------------------|-------|--------------------------------------------|
| `trained_model` | Badread pre-trained error/Qscore model name | STR | `nanopore2023` |
| `badread_identity` | Comma-separated values for Badread identity parameters | STR | `"98,2,99"` |
| `error_model` | Custom error model file (optional) | TXT | `null` |
| `qscore_model` | Custom Q-score model file (optional) | TXT | `null` |
| `build_model` | to build your own error/Qscor model | STR | `false` |
| `fastq_model` | reference real read (.fastq) to train error model (optional) | FASTQ | `false` |
| `ref_genome` | reference genome .fasta file (optional) | FASTA | `false` |

### Additional Parameters

| Parameter | Description | Default Value |
|--------------------|---------------------------------------------------------------|-----------------------------------------------|
| `amp` | Amplification factor | `1` |
| `outdir` | Output directory for results | `"results"` |
| `projectName` | Name of the project | `"test_project"` |
| Parameter | Description | Format | Default Value |
|--------------------|--------------------------------------------------------|-------|-----------------------------------------------|
| `amp` | Amplification factor | INT | `1` |
| `outdir` | Output directory for results | PATH | `"results"` |
| `projectName` | Name of the project | STR | `"test_project"` |

### Run Parameters

Configuration for running the workflow:

| Parameter | Description | Default Value |
|-------------------|------------------------------------|---------------------------|
| `threads` | Number of threads to use | `4` |
| `container` | Docker container for the workflow | `'hamraouii/wf-SLSim'` |
| `docker.runOptions` | Docker run options to use | `'-u $(id -u):$(id -g)'` |
| Parameter | Description | Format | Default Value |
|-------------------|------------------------------------|-------------|---------------------------|
| `threads` | Number of threads to use | INT | `4` |
| `container` | Docker container for the workflow | STR | `'hamraouii/wf-SLSim'` |
| `docker.runOptions` | Docker run options to use | STR | `'-u $(id -u):$(id -g)'` |

### File format discription
#### `--bc_counts`
To simulate specific UMI counts per cell barcode with random transcripts, set the --bc_counts parameter to the path of a UMI counts .CSV file. This parameter eliminates the need for an input matrix, enabling the simulation of UMI counts where transcripts are chosed randomly.

example of UMI counts per CB file:
|CB |counts|
|--------------|------|
|ACGGCGATCGCGAGCC |1260|
|ACGGCGATCGCGAGCC |1104|

#### `--cell_types_annotation`
AsaruSim allows user to estimate this characteristic from an existing count table. To do so, the user need to set --sim_celltypes parameter to true and to provide the list of cell barcodes of each group (.CSV file) using --cell_types_annotation parameter:
|CB | cell_type|
|--------------|------|
|ACGGCGATCGCGAGCC| type 1|
|ACGGCGATCGCGAGCC| type 2|

AsaruSim will then use the provided matrix to estimate characteristic of each cell groups and generate a synthetic count matrix.


## Usage
User can choose among 4 ways to simulate template reads.
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