Large intestine is the largest habitat for microbiota as well as the largest immune tissues. To help people understand how aging affect the large intestine physiology, I collect and visualize the Tabula muris data.
- The data was first downloaded from Tabula muris senis. A data set generated by 10X (droplet) and SMARTseq (facs) are hosted in the following path
/Data-objects/tabula-muris-senis-droplet-processed-official-annotations.h5adand/Data-objects/tabula-muris-senis-facs-processed-official-annotations.h5ad, respectively. - The droplet dataset contains only 3' mRNA seqeunces but the facs data set contains full-length mRNA sequences.
- For the large intestine, only facs data set were used since droplet data set only contains one age group.
- The downloaded h5ad files were subset, filtered, and normalized by my Python script.
- To produce tables for Tableau, the data were further process by my R script.
- The main toolkit for pre-process and data subset is scanpy and pandas.
- The IDE is Visual Studio code.
- Visualization tools are Tableau Public and Biorender.
- The data visualization could be found on My Tableau page.
github.com/pocession/Tabula_muris/
├── subset: csv files for subsetted data
│ ├── *.csv
├── figures : pdf or png files for output data
│ ├── *.pdf
| ├── *.png
├── output : csv files for output data, used by Tableau
│ ├── *.csv
├── scRNA_differentialAnalysis.ipynb: the python script for processing the input data
├── SscRNA_subsetting.ipynb: the python script for subsetting the original data
├── readme.md
├── *.h5ad: original h5ad files
The processed data is provided in 'csv' formats and stored in the follwoing folder.
