Fig 1.
EndoGeneAnalyzer tool workflow.
Users import data and select target genes for analysis, and the tool allows outlier removal. It also calculates stability metrics are calculated, the best reference gene is identified, and differential expression analysis between groups or conditions is enabled.
Fig 2.
Analysis of specific reference genes.
This figure provides a comprehensive side-by-side analysis of specific reference genes. Each gene is visually represented by a distinct square, allowing for a clear and concise depiction of its individual attributes. Within each square, box plots showcase the mean Cq values for each condition examined in the study. The implementation of vivid colors throughout the figure serves to effectively differentiate the diverse groups or conditions under investigation, enriching our comprehension of their unique characteristics and trends.
Fig 3.
Graphical verification of potential outliers separated by analyzed groups.
This figure shows four rectangles, each representing the sample distribution for a single reference gene. The icons (dot, triangle, square, and cross) symbolize the conditions, with red indicating non-outlier data and blue icons indicating outlier data. At the top, the standard deviation can be adjusted, and the user can remove outliers that affect the mean or all outliers.
Fig 4.
Gene Reference Descriptive Statistics by EndoGeneAnalyzer.
(1) Analysis of reference genes separated by study groups. (2) Descriptive statistics of the analyzed reference genes. (3) Results generated from the NormFinder tool. The first table displays the statistical results obtained using the Kruskal-Wallis test for each reference gene and the mean of all genes. Statistically significant values are highlighted in red. The second table presents descriptive data for each examined group, indicating lower values for superior gene expression or a more favorable set of genes. The third table shows the stability data generated by the NormFinder software, providing insights into the reliability of the identified reference genes for accurate gene expression analysis.
Fig 5.
Graphical visualization of differential expression analysis separated by target and study groups.
The figure summarizes the differential gene expression among the examined groups using ΔCq values in box plots. The color-coded conditions and tables showing the fold change values and Shapiro-Wilk normality test results provided comprehensive information. The user interface allows the selection of target genes and groups for statistical comparisons, including Pearson’s t-test, Wilcoxon-Mann-Whitney rank sum test (for two groups), ANOVA/Tukey’s test, or the Kruskal‒Wallis/Dunn test (for three groups). This illustration facilitates the exploration of the molecular differences underlying gene expression variations between conditions.