Skip to main content
. 2020 Nov 24;12(24):25916–25938. doi: 10.18632/aging.202163

Figure 2.

Figure 2

The mRNA expression landscape of 20 m6A regulators in the training dataset in EMs. (A) Most m6A regulators were dysregulated among NM, EU, and EC samples except for WATP ('Kruskal.test'). Several m6A regulators were differentially expressed in the EU vs. NM matrix and the EC vs. EU matrix, respectively ('LIMMA' R package). (B) Intersection analysis of differentially expressed m6A regulators between the EU vs. NM matrix and the EC vs. EU matrix. (C) The heatmap of 20 m6A regulators' expression among NM, EU, and EC samples. The heatmap was based on 'Euclidean' distance, and hierarchical clustering (clustering method = "complete" in R package 'pheatmap'); the clustering was performed on rows (genes) while not on columns (samples). (D) Spearman correlation analysis of 20 m6A regulators expression in EMs. EMs, endometriosis; NM, normal endometrium; EU, eutopic endometrium; EC, ectopic endometrium. NS - not significant; * p < 0.05; ** p < 0.01; *** p < 0.001.