---
agentTools:
  projectIndex: https://docs.cancergenomicscloud.org/llms.txt
---

# Data Studio Interactive Analyses

## Overview

**Data Studio Interactive Analyses** is a public project created with the goal of enhancing end-to-end bioinformatics analysis on the CGC. The suite is currently comprised of the following interactive analyses:

* **Single Cell RNA-Seq Interactive Analysis**. This is an interactive analysis for performing Clustering and Cluster Marker Identification analysis on scRNA-Seq data.
* **methylKit Differential Methylation Analysis**. This interactive analysis is based on **methylKit**, an R package used for analysis and annotation of DNA methylation information obtained through high-throughput bisulfite sequencing.
* **NanoStringNorm Gene Expression Analysis**. This analysis is written as an Rmarkdown document and can be used for the normalization, visualization and differential expression of NanoString miRNA and RNA expression level.
* **Ballgown Interactive Analysis**. This analysis tests for differential expression at the gene and transcript level using FPKM expression measurement data.
* **VCF Visualization Interactive Analysis**. The analysis offers several quality control analyses of variant call format (VCF) data. It accepts both raw and annotated VCF files without the need for compression or indexing.
* **Structural Variation (SV) Interactive Analysis**. This is a Jupyter Notebook designed for a quick overview of a VCF file containing structural variant calls. The analysis parses the SV caller’s VCF output for simple data filtering and visualization.
* **ChIP-seq Interactive Analysis**. The Chromatin Immunoprecipitation Sequencing (ChIP-Seq) Interactive Analysis provides a visualization of the likely locations of transcription factor binding sites or histone modifications.
* **Microbiome Differential Abundance Analysis**. The goal of the Microbiome Differential Abundance Interactive Analysis is to detect differential abundance of microbes between two predetermined classes of samples. This experimental design is applicable to case-control clinical studies and other settings for which there is prior assumption about the existing microbiological conditions within the different groups of samples.

## Access and run an analysis

1. On the main menu bar click **Public projects**.
   ![](https://files.readme.io/8505141-data-studio-interactive-analysis.png)
2. Locate **Data Studio Interactive Analyses** in the public projects gallery.
3. Click **Copy project** in the lower right corner.
4. If needed, change the project name, url, billing group and choose whether the project will contain controlled data.
5. Click **Copy**. You are now taken to your own copy of the **Data Studio Interactive Analyses** public project.
6. Click the **Data Studio** tab. You see a list of all available interactive analyses.
7. Click the name of the desired analysis.
8. Click **Start** in the top-right corner. Your analysis will start initializing.
9. Once it is ready, click **Open in editor** in the top-right corner. This opens the JupyterLab editor.
10. In the files list on the left, double click the **.ipynb** file containing the analysis. Your analysis is now loaded in the editor and ready to be executed.

![](https://files.readme.io/0b0a590-sbpla-data-cruncher-interactive-analyses-1.png "sbpla-data-cruncher-interactive-analyses-1.png")