SCANPY_GUI

A GUI based frameware for single cell RNA-Seq data analysis using scanpy. This tool runs on Python >= 3.7
It takes only 10x Genomics h5 files for the analysis. An example of the file is given in data folder which contains single cell RNA-Seq data of mouse model as an example.

Installation:

1. Download and extract the content of the folder.

2. Using terminal, navigate to the extracted folder. Eg. cd /path/to/folder.

3. Install the necessary python packages in requirements.txt using pip:

pip install -r requirements.txt

To run the tool

Open terminal and execute try.py by entering the following command:

python3 try.py

Open a browser and enter the following in the address bar:

127.0.0.1:5000

or

localhost

Follow the steps given below to perform the analysis

The dataset in the given steps below is available at 10xgenomics website:

https://cf.10xgenomics.com/samples/cell-exp/6.1.0/40k_NSCLC_DTC_3p_HT_nextgem_Multiplex/40k_NSCLC_DTC_3p_HT_nextgem_Multiplex_count_raw_feature_bc_matrix.h5

Step 1: Upload the file in .h5 format

Step 2: Pre-process the data. Select the minimum number of cells a gene is expressed and minimum genes to be expressed in a cell. Enter the mitochondrial gene annotation symbol

Step 3: Visualize the data and further preprocess it by entering the maximum transcripts and maximum pct mitochondrial genes. Next:

Normalise the data.

Perform Logarithmic Transformation

Calculate the highly variable genes

Step 4: Principal Component Analysis. Select the optimal number of principal components from the scree plot. Visualize the PC1 vs PC2 plot for the genes.

Step 5: Uniform Manifold Approximation and Projection (UMAP). A default clustering is done using leiden algorithm. Visualize the UMAP1 vs UMAP2 plot for the genes.

Created By:
Adhiraj Nath
PhD Candidate, IIT Guwahati