Transcriptome profiling and differential gene expression constitute a ubiquitous tool in biomedical research and clinical application. Linear dimensionality reduction methods especially principal component analysis (PCA) are widely used in detecting sample-to-sample heterogeneity in bulk transcriptomic datasets so that appropriate analytic methods can be used to correct batch effects, remove outliers and distinguish subgroups. In response to the challenge in analysing transcriptomic datasets with large sample size such as single-cell RNA-sequencing (scRNA-seq), non-linear dimensionality reduction methods were developed. t-distributed stochastic neighbour embedding (t-SNE) and uniform manifold approximation and projection (UMAP) show the advantage of preserving local information among samples and enable effective identification of heterogeneity and efficient organisation of clusters in scRNA-seq analysis. However, the utility of t-SNE and UMAP in bulk transcriptomic analysis has not been carefully examined. In this study, we compared the capabilities of PCA, multidimensional scaling (MDS), t-SNE, and UMAP in heterogeneity exploration of 71 sizeable transcriptome datasets. We first quantitatively analysed the performance of four dimensionality reduction methods in terms of clustering accuracy, neighbourhood preserving and computational efficiency, followed by qualitative analysis on identifying batch effects, validating biological groups and associating clustering structures with sample features and clinical meaning. By visualising and interpreting 71 sizeable datasets of bulk transcriptome profiling, we found that UMAP was superior in preserving sample level neighbourhood information and maintaining clustering accuracy, thus conspicuously differentiating batch effects, identifying pre-defined biological groups and identifying new clustering structures associated with biological features and clinical meaning. UMAP was found superior in preserving sample level neighbourhood information and maintaining clustering accuracy, thus conspicuously differentiating batch effects, identifying pre-defined biological groups and revealing in-depth clustering structures. We further verified that new clustering structures visualised by UMAP were associated with biological and clinical meaning. Therefore, we recommend using UMAP in visualising and analysing of sizable bulk transcriptomic datasets.