Skip to main content
Fig. 3 | Journal of Translational Medicine

Fig. 3

From: Transcriptome of nasopharyngeal samples from COVID-19 patients and a comparative analysis with other SARS-CoV-2 infection models reveal disparate host responses against SARS-CoV-2

Fig. 3

Differential gene expression analysis of the studied nasophryngeal samples of COVID-19 patients. a Variance plot. This plots the standard deviation of the transformed data, across samples, against the mean, using the variance stabilizing transformation. The vertical axis in the plots is the square root of the variance over all samples. b Sample to sample distance plot. A heatmap of distance matrix providing an overview of similarities and dissimilarities between samples. Clustering is based on the distances between the rows/columns of the distance matrix. c Principal component analysis plot. Samples are in the 2D plane spanned by their first two principal components. d Clustered heatmap of the log2 converted normalized count matrix RNA-seq reads, top 50 genes, of nasopharyngeal samples. e Normalized Log2 read counts of the genes encoding SARS-CoV-2 receptor and entry associated proteins. Enrichment analysis and comparison between deregulated genes and the genes of some selected processes in SARS-CoV-2 infected nasopharyngeal samples and SARS-CoV-2 infected lung biopsy samples using f GOBP module, g KEGG pathway, h Bioplanet pathway module. Selected significant terms are represented in heatmaps. Significance of enrichment in terms of the adjusted p-value (< 0.05) is represented in color-coded P-value scale for all heatmaps; Color towards red indicates higher significance and color towards yellow indicates less significance, while grey means non-significant. Normalized Log2 converted read counts are considered as the expression values of the genes and represented in a color-coded scale; Color towards red indicating higher expression, while color towards green indicating little to no expression. Here, Up, down and DE denote Upregulated, Downregulated and Differentially expressed, respectively

Back to article page