Getting Started with RT-PCR Analysis
This tutorial series will provide a walkthrough of Array Studio’s RT-PCR analysis pipeline. After importing any of several common RT-PCR data formats, standard visualizations and statistical inference modules can be used to analyze your data. In addition, the RT-PCR data can easily be integrated with other RT-PCR, microarray or RNA-seq experiments.
Getting Started with RT-PCR Analysis
Array Studio allows you to quickly and easily create a new project to analyze your RT-PCR data. This video is an overview of what you need to get started.
- Purpose of the RT-PCR Wizard function [00:07]
- Example input data: "Tall-skinny" tables [00:36]
- Example input data: Matrix data [00:48]
- Overview of the RT-PCR video series [01:02]
- Additional help at [wiki.omicsoft.com] [01:24]
Common RT-PCR Input Formats
Array Studio's RT-PCR workflow can import Ct or abundance data from text files and Excel spreadsheets. This video walks through the basic organization of common formats.
- "Tall-skinny" format [00:07]
- Matrix data (e.g. one gene per row) [00:28]
- Annotation (gene) metadata [00:48]
- Design (sample) metadata [01:01]
Array Studio RT-PCR Wizard
Importing and normalizing RT-PCR can be a bit tricky. Array Studio's RT-PCR Wizard simplifies this process, by walking through each step of the process. Click here for the tutorial data sets.
- Create a new Array Studio project [00:13]
- For an introduction to Server-based analysis, see Getting Started With Server Analysis
- Run the RT-PCR Wizard [01:10]
- Choosing the correct input format [01:30]
- Select the annotation and data columns [02:27]
- Preview raw data for missing values [02:56]
- Attach Annotation and Design metadata [03:12]
- Combine or remove technical replicates [04:07]
- Specify default values for missing data [04:21]
- Transform Ct data to delta-Ct [04:35]
- Normalize data [04:54]
- Preview data [05:21]
- Inspect and edit labels for the new data [05:51]
Downstream Analysis of pipeline data
A large number of visualization and QC functions are available to analyze RT-PCR data. The following videos will demonstrate some ways to explore your data.
Adding Views to RT-PCR data
Array Studio has up to 40 different "Views" that display your RT-PCR data in different ways. You can filter the underlying data and customize the Views to your liking. Views are also interactive, so clicking on elements will display the underlying data in the Details Window.
- Table View [00:24]
- Adding a View to your data [00:50]
- BoxPlot View (Shows range of gene expression per-sample) [01:05]
- Customizing Views [01:17]
- DotPlot View (Shows range of sample expression per-gene) [02:58]
- Variable View (Shows one chart per-gene of sample/group expression) [04:35]
- Filtering Views by gene name [05:21]
- Change Variable View to Violin plot([05:51]
- 3-D Multi-variable View [06:09]
QC and Excluding/Subsetting Data
Principal Component Analysis (PCA) can be used to look for variance in your data, including outlier samples. If you discover that an assay or sample failed, and want to exclude it, or you want to focus your analysis on a subset of genes, you can easily subset your Array Studio data.
- Run the PCA module [00:19]
- Select and exclude outlier samples [01:56]
- Subset -Omic data by sample list [02:55]
- Hierarchical clustering of data [03:41]
- Add Color Bars to Dendrogram View [ 04:47]
- Select variables to subset data [05:34]
- Hierarchical clustering with variable subset list [06:19]
Two-Way ANOVA of RT-PCR Data
Array Studio contains multiple modules to identify statistical-significant differences in your data. This video demonstrates a Two-Way ANOVA (Tissue and Treatment), and summarization of the results by interactive reports and Venn diagrams.
- The Two-Way ANOVA module [00:18]
- Output from Statistical Inference modules [01:34]
- Interacting with the Volcano Plot [01:52]
- Summarize Inference Reports [02:46]
- Create a Venn Diagram [03:36]
Integration of RT-PCR and RNA-seq/Microarray Data
Feature-level (genes, transcripts, etc.) results from RNA-seq experiments can directly be compared to RT-PCR data from the same samples, using the Microarray-Microarray Integration module. Microarray functions can be run on any -Omic data (table data with associated annotation and design metadata).
- Importing RNA-seq FPKM matrix data into Array Studio [00:15]
- Ensure that design and annotation tables contain a matching column between data sets [01:20]
- Log2-transform RNA-seq data [01:55]
- Move Array Studio data objects betweeen projects [03:15]
- Run the MicroArray-MicroArray Integration function [03:48]
- Select metadata columns to ensure proper matching of data [04:58]
- Output of MicroArray-MicroArray Integration [05:26]