How To Accurately Analyze qPCR Gene Expression Data in Minutes

How To Accurately Analyze qPCR Gene Expression Data in Minutes Introduction Real-time PCR, or quantitative PCR (qPCR), generates large volumes of data, but these raw measurements must be processed and interpreted before they can provide meaningful biological insights. Accurate gene expression analysis goes beyond comparing Cq values—it requires appropriate normalization, quality control, and statistical analysis to ensure that observed differences are biologically relevant rather than the result of technical variation. Bio-Rad’s CFX Maestro software is a dedicated platform for managing qPCR data collection and analysis. It streamlines the process by combining plate setup, data analysis, and visualization within a single workflow. Once the experiment has been correctly configured, the software can automatically perform key calculations, including normalization to references genes, fold-change analysis, and statistical comparisons, eliminating the need for manual calculations or external analysis tools. In this guide, Kevin Murnan, Field Application Scientist and qPCR Applications Lead at Bio-Rad, outlines the recommended workflow for analyzing gene expression data in CFX Maestro. While this example compares expression of Gene A in wild-type and mutant cell lines using GAPDH as the reference gene, the same principles apply to a wide range of gene expression experiments. Watch the software demonstration in the Teach Me in 10 here. 1. How to configure the plate layout Begin by creating a new plate map within a userdefined run and select Plate during the run setup. First, the appropriate Scan Mode must be selected. For experiments using SYBR Green chemistry, select ‘SYBR/FAM only’ (Figure 1A). If the assay uses fluorescent probes with multiple fluorophores, select ‘All Channels’ instead. Next, identify every well that will contain a sample, and set the Sample Type to ‘Unknown’ for the experiment wells at this stage (Figure 1B). Individual samples can be assigned later, once the plate layout has been established. Devoting time to configuring the plate correctly at the beginning of the workflow helps to prevent errors during downstream analysis and ensures the software can apply the correct calculations once the run is completed. However, if mistakes are identified during analysis, the plate configuration can be updated post-run from the data file by clicking ‘Plate Setup’. This gives the flexibility to refine analyses without repeating the experiment. Figure 1. A) Showing Scan Mode options. B) Showing Sample Type options. A B HOW TO GUIDE Visit bio-rad.com/bio-rad.com/cfxmaestro for more information. Bulletin 4055 Ver A2. How to assign samples and targets After defining the plate layout, assign each well to its corresponding biological groups and target gene. For instance, rows A and B may represent the wild-type samples, while rows C and D represent the mutant samples. After assigning biological groups, users may then apply target names. To quickly assign the same target to multiple biological groups, users may select non-adjacent wells or rows by holding ‘Ctrl’ (e.g., rows A and C for Gene A, rows B and D for GAPDH). This ensures that each biological group contains measurements for both the target gene and the reference genes. During analysis, CFX Maestro uses the reference gene(s) to normalize expression levels before calculating fold changes between experimental groups. 3. How to set up technical replicates Technical replicates improve confidence in qPCR data by reducing the impact of pipetting variation and other sources of technical error. To define replicates, select the appropriate wells, choose Technical Replicates, and specify the number of replicates included in the experiment (e.g., triplicate). The software also requires specification of the replicate orientation. Most plate layouts use horizontally arranged replicates, although vertical configurations can be accommodated where appropriate. Individual samples can also be labeled at this stage, by highlighting the relative wells and assigning a name under Sample Names. Although optional, giving descriptive sample names simplifies both interpretation of completed map plates and subsequent data analysis. 4. How to configure standards for a dilution series Many gene expression experiments include a standard curve to assess assay performance and calculate reaction efficiency. If required, assign the relevant wells under Sample Type as ‘Standard’ and select the appropriate target gene. Specify the number of technical replicates for each dilution point before defining the dilution series. This includes selecting the first and last replicate positions, entering the dilution factor, and indicating whether concentration decreases from left to right across the plate (Figure 3). Once the run has finished, CFX Maestro automatically generates the standard curve and calculates reaction efficiency. Figure 2. Plate editor showing the biological group as mutant, and the target name as ‘GAPDH’. For the wild-type samples, the target name might be Gene A, while the biological group would be WT. © 2026 Bio-Rad Laboratories, Inc. 2 Bulletin 4055 Ver A How To Accurately Analyze qPCR Gene Expression Data in MinutesThis is also an appropriate stage to define any positive, negative, or no-template controls (NTCs) included on the plate by changing Sample Type. Wells that have not been loaded should be cleared to exclude them from the analysis. Before starting the run, review the completed plate map under Show Technical Replicates to confirm that all samples, controls, and replicates have been assigned correctly. 5. How to configure gene expression analysis Before analyzing the results, configure the experiment settings to enable automated gene expression analysis in CFX Maestro. You can configure these settings before the run or later from the data file. Open Experiment Settings, designate your reference gene (i.e., GAPDH), and define appropriate samples as the control group (i.e., wild-type) (Figure 4). These settings determine how expression data will be normalized and compared. Once they have been saved, the software automatically calculates fold changes, performs the associated statistical analyses, and generates the relevant visualizations when the run is complete. Although this step requires minimal selections, it forms the basis of all downstream gene expression calculations. Confirming that the correct reference gene and control group have been assigned before analysis helps produce accurate and reproducible results. 6. How to review amplification data Once data acquisition has finished, begin by reviewing the Quantification tab. This displays amplification curves on a logarithmic scale by default, although a linear view can be selected if preferred. One advantage of CFX Maestro is that the different visualization panels are linked. For example, selecting an amplification curve highlights the corresponding point on the reaction efficiency plot, making it easier to investigate individual reactions. Standard samples are displayed separately from unknown samples, while a summary of Cq values provides a rapid overview of assay performance (Figure 5). If required, graphs and data can be copied or exported directly for reports, presentations, or further analysis by rightclicking anywhere on the chart. 7. How to interpret gene expression results The Quantification Data tab contains numerical spreadsheets Figure 3. Technical replicate controls, with replicate positions and the dilution factor. © 2026 Bio-Rad Laboratories, Inc. 3 Bulletin 4055 Ver A How To Accurately Analyze qPCR Gene Expression Data in Minutesand allows data to be exported in Excel or CSV format. However, when the experiment has been configured for gene expression analysis, the Gene Expression tab provides the most informative overview of the results. This tab is where the automatically generated fold-change plots are displayed, based on the selected reference gene(s) and control group. Statistically significant differences are identified automatically, while error bars can be displayed as either confidence intervals, standard error of the mean, or standard deviation, depending on the reporting requirements (Figure 6). The underlying fold-change calculations are based on the mathematical model described by Pfaffl (2001), which corrects for gene-specific amplification efficiency when comparing expression of the target gene relative to the reference gene. The Gene Expression tab also includes several additional visualization tools, including clustergrams, scatter plots, volcano plots, and heat maps. The appearance of the charts can be customized based on color within the data analysis table. For experiments involving multiple groups or time points, built-in ANOVA with Tukey-adjusted p-values allows statistical comparisons to be performed without exporting the data to separate statistical software (Figure 6). 8. How to assess assay quality Accurate interpretation of gene expression data depends on confirming that the assay has performed as expected. If SYBR chemistry has been used, begin by reviewing the Melt Curve tab to assess amplification specificity. A single, well-defined melt peak indicates specific amplification. Next, examine the Quality Control tab. CFX Maestro automatically identifies reactions that fall outside user-defined performance thresholds, such as reduced amplification efficiency or other quality metrics. Selecting a flagged result links directly back to the corresponding quantification data, making it easy to investigate potential issues. Quality control thresholds can be adjusted to suit individual Figure 4. The reference and control samples can be determined under the Experiment Settings tab. Figure 5. The Quantification tab displays amplification curves, reaction efficiency plots with different symbols (X, O) for unknown and standard samples, and data such as Cq values. © 2026 Bio-Rad Laboratories, Inc. 4 Bulletin 4055 Ver A How To Accurately Analyze qPCR Gene Expression Data in MinutesWebsite bio-rad.com USA 1 800 424 6723 Australia 61 2 9914 2800 Austria 00 800 00 24 67 23 Belgium 00 800 00 24 67 23 Brazil 55 11 3065 7550 Canada 1 800 361 1808 China 86 21 6169 8500 Czech Republic 00 800 00 24 67 23 Denmark 00 800 00 24 67 23 Finland 00 800 00 24 67 23 France 00 800 00 24 67 23 Germany 00 800 00 24 67 23 Greece 30 210 7774396 Hong Kong 852 2789 3300 Hungary 00 800 00 24 67 23 India 91 124 4029300 Israel 000 800 00 24 67 23 Italy 00 800 00 24 67 23 Japan 81 3 6361 7000 Korea 82 080 007 7373 Luxembourg 00 800 00 24 67 23 Mexico 52 55 5488 7670 The Netherlands 00 800 00 24 67 23 New Zealand 64 9 415 2280 Norway 00 800 00 24 67 23 Poland 00 800 00 24 67 23 Portugal 00 800 00 24 67 23 Russian Federation 7 495 721 14 04 Singapore 65 6415-3170 South Africa 27 21 531 7504 Spain 00 800 00 24 67 23 Sweden 00 800 00 24 67 23 Switzerland 00 800 00 24 67 23 Taiwan 886 2 2578 7189 Thailand 662 651 8311 United Arab Emirates 971 4 818 7300 United Kingdom 00 800 00 24 67 23 Life Science Group Bio-Rad Laboratories, Inc. Bulletin 4055 Ver A 08/2026 assay requirements, and wells that fail predefined acceptance criteria can be excluded from downstream analyses where appropriate. Reviewing these quality metrics before interpreting fold-change data helps ensure that conclusions are based on reliable experimental results. Conclusion Successful gene expression analysis begins long before the first amplification curve is generated. Careful plate setup, accurate assignment of samples and controls, and appropriate experiment configuration provide the foundation for reliable downstream analysis. Once these elements are in place, CFX Maestro automates many of the calculations required for gene expression studies, enabling researchers to focus on interpreting the biological significance of their results rather than performing manual data processing. Learn more about CFX Maestro software Dr. Kevin Murnan obtained his BS in Molecular Genetics from The Ohio State University and PhD in the departments of neurology and medicine at Northwestern University. Presently, Kevin is a Field Application Scientist and qPCR Applications Lead at Bio-Rad. Figure 6. The Gene Expression tab allows you to visualize charts, such as bar charts, clustergrams, scatter plots, volcano plots, and heat maps, edit their appearance, and add error bars. How To Accurately Analyze qPCR Gene Expression Data in Minutes