| Name | muxsim JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | A tool to simulate cell/guide matrices |
| upload_time | 2024-10-09 17:27:14 |
| maintainer | None |
| docs_url | None |
| author | Noam Teyssier |
| requires_python | >=3.11 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# muxsim
a python module for generate cell / guide matrices for demultiplex testing.
## Installation
```bash
pip install muxsim
```
## Usage
Muxsim is expected to be used as a python module. It has reasonable defaults for most use cases, but can be configured to your liking.
Here is a simple example of how to use muxsim:
```python
from muxsim import MuxSim
ms = MuxSim()
matrix = ms.sample()
```
The sampling scheme can be fully parameterized like so:
```python
from muxsim import MuxSim
ms = MuxSim(
num_cells=10000,
num_guides=100,
n=10.0,
p=0.1,
λ=0.8,
random_state=42,
)
matrix = ms.sample()
```
## Methods
The simulator, `muxsim`, is based on a Multinomial distribution, where the number of draws $(\mathcal{S})$ of cell $(i)$ is drawn from a Negative Binomial distribution representing the number of observed UMIs of that cell.
$$
\begin{align}
\mathbb{U}_i &\sim \text{Multinomial}(\mathbb{S}_i,\ f_i) \\
\mathbb{S}_i &\sim \text{NegativeBinomial}(n,\ p) \\
\end{align}
$$
The frequencies of the multinomial distribution are cell specific and sum to 1:
$$
\sum_{j=1}^{M}{f_{ij}} = 1
$$
The background frequencies are assumed to be equiprobable (where $`(\forall u,v \in M)(f_{iu} = f_{iv})`$ ), except for signal guides - which would be a scaled by some value $(r)$. The number of signal guides is chosen using a Poisson prior to simulate situations where the expected multiplicity of infection (MOI) can change:
$$
\mathbb{I}_i \sim \text{Poisson}(\lambda)
$$
The signal guides are chosen randomly from the guide set where the number of choices is equal to the MOI of that cell:
$$
\mathbb{C}_i \sim \text{Uniform}(M, \mathbb{I}_i)
$$
This allows us to then set the the signal guides at a rate $(r)$ above the background with the following expression:
$$
t_{ij} =
\begin{cases}
r,& \text{if } j \in \mathbb{C}_i \\
1,& \text{if } j \not\in \mathbb{C}_i
\end{cases}
$$
Which can then be turned into the frequency matrix:
$$
f_i = \frac{t_{i}}{\sum_{j=1}^{M}t_{ij}}
$$
Which forces $f_{ij}$ to sum to 1.
Raw data
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