PRICING & INQUIRIES

For pricing and inquiries, send an email to sales@omicsoft.com.

5001 Weston Parkway, Suite 201
Cary, NC 27513
US

888-259-6642

Overview

Omicsoft is the leading provider of Next Generation Sequencing, Cancer Genomics, Immunology, and Bioinformatics solutions for Next Generation Sequencing Data and Gene Expression Analysis.

[Array Studio Analysis] Getting Started with RT-PCR Analysis

Exciting Updates and Latest News

Keeping you up-to-date with the latest in NGS, Bioinformatics Analysis, and cancer genomics with blogs on Array Suite, OncoLand (TCGA and more), ImmunoLand, and more.

[Array Studio Analysis] Getting Started with RT-PCR Analysis

Vivian Zhang

Although RNA-seq has become the invaluable tool to study gene expression, RT-PCR (reverse transcription-polymerase chain reaction) is still the most sensitive method and widely-used for small-scale mRNA expression studies or RNA-seq analysis validation. In this article, we would like to introduce to you how to perform RT-PCR analysis using Array Studio. For more details, please check out our tutorial series: Getting Started with RT-PCR Analysis, which has step-by-step video tutorial clips to help you quickly become RT-PCR analysis expert.

In this tutorial, we provide tutorial video clips on:

 

1. Importing RT-PCR data

Array Studio allows user to import Ct or abundance data from text files or Excel spreadsheets. The Import RT-PCR Wizard function simplifies the data importing and normalizing processes.

Array Studio can process different data formats, no matter its "Tall-skinny" or Matrix data format. 

Array Studio can process different data formats, no matter its "Tall-skinny" or Matrix data format. 

With your data ready to import, Import RT-PCR Wizard offers step-by-step instructions on:

  • Choosing the correct input format
  • Selecting the annotation and data columns 
  • Previewing raw data for missing values 
  • Attaching Annotation and Design metadata 
  • Combining or remove technical replicates
  • Specifying default values for missing data 
  • Transforming Ct data to delta-Ct
  • Normalizing data 
  • Previewing data 

After importing through the RT-PCR Wizard, three data tables are generated: a data table, annotation table and design table. They are standard data formats in Array Studio.

 

2. Downstream Analysis

2.1 Visualizing RT-PCR Data- Adding Views to RT-PCR data

Once the data is in an Array Studio project, a variety of functions are available for downstream analysis. To start with, data visualization provides a good overview of the data. Array Studio has up to 40 different views available for your RT-PCR data. Here are a few commonly used views:

 

2.2 Data Processing-QC and Excluding/Subsetting Data

Sometimes single assay or sample experiment fails and should be removed from downstream analysis to allow for more accurate detection of real differences among groups. These failed experiments can be detected and easily removed from an Array Studio data object. For example, we can use Principal Component Analysis (PCA) to detect and remove outlier samples. To further subset data, we can use hierarchical clustering.

3D PCA plot and hierarchical clustering heatmap. In the upper PCA plot, each dot represents a sample. In the bottom heatmap, data is clearly separated by source tissue but not so much by group.

3D PCA plot and hierarchical clustering heatmap. In the upper PCA plot, each dot represents a sample. In the bottom heatmap, data is clearly separated by source tissue but not so much by group.

 

 

2.3 Statistical Inference-Two-Way ANOVA of RT-PCR Data

Array Studio has a few different statistical inference modules to identify statistical significant differences between groups, for example, ANOVA and general linear model. Here is an example of Two-Way ANOVA analysis.

Two-Way ANOVA analysis results using source tissue and group as factors. This analysis generates one volcano plot for each test, in addition to the report table. The volcano plot is interactive -- selecting a subset of samples in one plot automatically selects the corresponding samples in another plot. In this example, the three selected genes CDH1, PFN2 and NOTCH2 that are affected in Breast are not similarly affected in Lymphoid, with NOTCH2 affected in the opposite direction.

Two-Way ANOVA analysis results using source tissue and group as factors. This analysis generates one volcano plot for each test, in addition to the report table. The volcano plot is interactive -- selecting a subset of samples in one plot automatically selects the corresponding samples in another plot. In this example, the three selected genes CDH1, PFN2 and NOTCH2 that are affected in Breast are not similarly affected in Lymphoid, with NOTCH2 affected in the opposite direction.

 

2.4 Omic Data Analysis-Integration of RT-PCR and RNA-seq/Microarray Data

RT-PCR data can be compared to other gene or transcript level data, such as from RNA-Seq or microarray, using Microarray-Microarray Integration. Careful data matching is important to ensure proper matching of data. 

Variable view of RT-PCR and RNA-Seq data integration for gene TFPI as an example.

Variable view of RT-PCR and RNA-Seq data integration for gene TFPI as an example.

 

Please check out our tutorial series: Getting Started with RT-PCR Analysis to learn how to perform the above RT-PCA analyses.