How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

Abstract

Identifying differentially expressed genes is critical in microarray data analysis. Many methods have been developed by combining p-value, fold-change, and various statistical models to determine these genes. When using these methods, it is necessary to set up various pre-determined cutoff values. However, many of these cutoff values are somewhat arbitrary and may not have clear connections to biology. In this study, a genetic distance method based on gene expression level was developed to analyze eight sets of microarray data extracted from the GEO database. Since the genes used in distance calculation have been ranked by fold-change, the genetic distance becomes more stable when adding more genes in the calculation, indicating there is an optimal set of genes which are sufficient to characterize the stable difference between samples. This set of genes is differentially expressed genes representing both the genotypic and phenotypic differences between samples.

Systematic Reconstruction of the Complete Two-Component Sensorial Network in Staphylococcus aureus

“…Despite the low number of genes whose expression changes significantly, complementation with native TCS restored the phenotypes as efficiently as complementation with the phosphomimetic RR. These results highlight the well-recognized problem about the adequacy of the fold change cutoff values that are currently used to associate genes to biological phenotypes (44,45). Identification of the individual TCS regulon is intrinsically dependent on the availability of its cognate signal when assessing transcriptional changes and further emphasizes the need to use culture conditions that activate TCS signaling when determining their target genes.…”

NusG is an intrinsic transcription termination factor that stimulates motility and coordinates gene expression with NusA

“…To examine the effect of NusG on gene expression, a differential expression analysis was conducted comparing expression data from each mutant strain to expression data from the WT strain (Supplementary file 5). Volcano plots were constructed based on the results of this analysis, and affected genes were determined using false discovery rate (FDR) cutoffs of 0.005 and fold-change cutoffs of 4 (Zhao et al 2018, Dalman et al 2012. This approach revealed that NusG is involved in regulating gene expression on a global scale, with 106 transcripts increasing in expression and 37 transcripts decreasing in expression in the ΔnusG strain (Figure 6A).…”

Use of scREAD to explore and analyze single-cell and single-nucleus RNA-seq data for Alzheimer’s disease

“…Adjusting any parameters in the DE section will immediately affect the displayed DEGs table. A less-stringent p value is recommended to be used to select a large preliminary list of genes, then all the genes in the list are ranked by FC, and finally, an FC cutoff is applied to determine the final set of DEGs ( Zhao et al., 2018 ). If you would like to use more stringent criteria for the enrichment analysis result, please increase the log fold-change values or decrease the p values and vice versa.…”