Aims: Principal component analysis (PCA) is a widely used dimensionality reduction technique in life sciences, which is usually used to create two-dimensional visualization of geometric morphological measurement data. However, because PCA cannot summarize nonlinear dependencies between variables, interesting biological information may be distorted or lost in these graphs. Nonlinear alternatives exist, but their effectiveness has never been tested on placental transcriptomic data. Methods and Results: In this study, first-trimester chorionic villus and decidua tissues were collected from 6 healthy women. Transcriptome data was acquired by RNA-seq and the expression levels of trophoblast specific transcription factors were identified by immunofluorescence. Differentially expressed genes between chorionic villus and decidua tissues and its related biological functions were identified. After that, we performed Principal Component Analysis (PCA) on these 12 samples. Furthermore, 18 published transcriptomes (a total of 425 samples) datasets of human pregnancy-related tissues (including chorionic villus and decidua, term placenta, endometrium, in vitro cell lines etc.) from public databases were collected and analyzed. At the same time, we compared two of the most widely used dimensionality reduction (DR) methods to generate 2D-map for visualization of these data. We compared the effects of different parameter settings and commonly used manifold learning methods on the results. Conclusions: The result indicates that, the nonlinear method can better preserve the small differences between different subtypes of placental tissue than PCA. Although there are public RNA-seq data available for chorionic villus and decidua tissue, this is the first time that the RNA-seq data were obtained from the chorionic villus and decidua which derived from the same patient. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.