finest 0.0.2

FineST: Fine-grained Spatial Transcriptomic

About

FineST (Fine-grained Spatial Transcriptomics), a statistical model and toolbox to identify the super-resolved ligand-receptor interaction with spatial co-expression (i.e., spatial association).

Uniquely, FineST can distinguish co-expressed ligand-receptor pairs (LR pairs) from spatially separating pairs at sub-spot level or single-cell level, and identify the super-resolved interaction.

It comprises two main steps:

1. Fine-grained ligand-receptor pair discovery;

2. Cell-cell communication pattern classification;

3. Pathway enrichment analysis.

With the analytical testing method, FineST accurately predicts ST gene expression and outperforms TESLA and iStar at both spot and gene levels in terms of the root mean square error (RMSE) and Pearson correlation coefficient (PCC) between the predicted gene expressions and ground truth.

It comprises two main steps:

1. global selection spatialdm_global to identify significantly interacting LR pairs;

2. local selection spatialdm_local to identify local spots for each interaction.

Installation

FineST is available through PyPI. To install, type the following command line and add -U for updates:

pip install -U FineST

Alternatively, you can install from this GitHub repository for latest (often development) version by the following command line:

pip install -U git+https://github.com/LingyuLi-math/FineST

Installation time: < 1 min

Alternatively,

$ git clone https://github.com/LingyuLi-math/FineST.git
$ conda create --name FineST python=3.8
$ conda activate FineST
$ cd FineST
$ pip install -r requirements.txt

Typically installation is expected to be completed within a few minutes. Then install pytorch. You may refer to pytorch installation.

$ conda install pytorch=1.7.1 torchvision torchaudio cudatoolkit=11.0 -c pytorch

Once the installation is complete, you can verify the installation using the following command:

python
>>> import torch
>>> print(torch.__version__)
>>> print(torch.cuda.is_available())

Quick example

Using the build-in NPC dataset as an example, the following Python script will predict super-resolution ST gene expression and compute the p-value indicating whether a certain Ligand-Receptor is spatially co-expressed.

Detailed Manual

The full manual is at finest-rtd-tutorial.