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Copy file name to clipboardExpand all lines: README.md
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A python tool to investigate vertical signal properties of imaging-based spatial transcriptomics data.
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## introduction
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## Introduction
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Much of spatial biology uses microscopic tissue slices to study the spatial distribution of cells and molecules. In the process, tissue slices are often interpreted as 2D representations of 3D biological structures - which can introduce artefacts and inconsistencies in the data whenever structures overlap in the thin vertical dimension of the slice:
Ovrl.py is a quality-control tool for spatial transcriptomics data that can help analysts find sources of vertical signal inconsistency in their data.
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**Ovrl.py** is a quality-control tool for spatial transcriptomics data that can help analysts find sources of vertical signal inconsistency in their data.
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It is works with imaging-based spatial transcriptomics data, such as 10x genomics' Xenium or vizgen's MERFISH platforms.
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The main feature of the tool is the production of 'signal integrity maps' that can help analysts identify sources of signal inconsistency in their data.
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Users can also use the built-in 3D visualisation tool to explore regions of signal inconsistency in their data on a molecular level.
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## installation
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## Installation
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The tool can be installed using the requirements.txt file in the root directory of the repository.
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To install the necessary tools and dependencies for this project, follow the steps outlined below. These instructions will guide you through setting up the environment for both standard use and interactive analysis with Jupyter notebooks.
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```bash
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pip install -e .
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```
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In order to use the ipython notebooks and perform interactive analysis, you will need to install the jupyter package also. For the tutorials, pyarrow and fastparquet are also required.
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> Ensure that Python (>= 3.6 and < 3.13) and pip are installed on your machine before proceeding.
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```bash
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pip install jupyter pyarrow fastparquet
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```
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Steps for Installation
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-----------------------
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1.**Clone the Repository**
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First, ensure that you have cloned the repository to your local machine. If you haven't already done so, use the following commands:
To install the ovrlpy package, execute the following command:
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````bash
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pip install .
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````
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This installs the package based on the current state of the source files.
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3. **Set Up for Interactive Analysis (Optional)**
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If you plan to use Jupyter notebooks for interactive analysis or the project's tutorials, you'll need to install some additional packages: **Jupyter**. Install them using:
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## quickstart
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````bash
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pip install jupyter
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````
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## Quickstart
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-----------------------
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The simplest use case of ovrlpy is the creation of a signal integrity map from a spatial transcriptomics dataset.
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In a first step, we define a number of parameters for the analysis:
Copy file name to clipboardExpand all lines: docs/source/installation.rst
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cd ovrl.py
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2. **Install the Package in Editable Mode**
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2. **Install the Package**
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To install the ovrlpy package, execute the following command:
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.. note::
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Ensure that Python (>= 3.6 and < 3.13) and pip are installed on your machine before proceeding.
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.. code-block:: bash
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3. **Set Up for Interactive Analysis (Optional)**
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If you plan to use Jupyter notebooks for interactive analysis or the project's tutorials, you'll need to install some additional packages: **Jupyter**, **pyarrow**, and **fastparquet**. Install them using:
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If you plan to use Jupyter notebooks for interactive analysis or the project's tutorials, you'll need to install some additional packages: **Jupyter**. Install them using:
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.. code-block:: bash
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pip install jupyter pyarrow fastparquet
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pip install jupyter
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Summary of Commands
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# Step 3: Install Jupyter and other packages for interactive analysis
Copy file name to clipboardExpand all lines: docs/source/tutorials/index.rst
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Tutorials
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Tutorials
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==========================
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We will demonstrate an example usage of ovrlpy on 3 different datasets (`Xenium Brain <https://www.10xgenomics.com/products/xenium-in-situ/mouse-brain-dataset-explorer>`_,
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