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 Getting Started with RT-PCR Analysis
- 2 Downstream Analysis of pipeline data
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.
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.
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.
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.