Implantation failure and insufficient placental development are important causes of female infertility, recurrent miscarriage, and other pregnancy-related problems. A better understanding of gene expression profiling based on high-throughput sequencing technology is essential for the study on the development of the placenta and the causes of pregnancy-related diseases. In this study, we collected 6 first-trimester chorionic villus and decidua tissues were collected from 6 healthy women and acquired obtained the transcriptome database on was acquired by RNA-seq high-throughput sequencing. We performed routine principal component analysis (PCA) on these 12 samples and identified differentially expressed genes between chorionic villus and decidua tissues and its related biological functions from between chorionic villus and decidua tissue with different sex background were identified. At the same time, we performed Principal Component Analysis (PCA) analysis on these 12 samples. And identified genes highly expressed in villus and decidua, respectively. Furthermore, we collected and analyzed 18 published transcriptomes (a total of 425 samples) datasets of human pregnancy-related tissues (including chorionic villus and decidua, term placenta, endometrium, 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. The result shows indicate that PCA which based on linear dimensionality reduction cannot correctly distinguish the various subtypes of placental tissue, and the use of t-Distributed Stochastic Neighbor Embedding (t-SNE) provides a 2D-map that accurately could identify distinguish the different various subtypes of placental tissue. Our comprehensive analysis includes human placental tissue at different developmental stages and under different culture conditions. Therefore, The 2D-map presented here provided a valuable reference for understanding the maintenance of early gestation as well as pathogenesis inof pregnancy-related disorders. The dataset and analysis we presented here provide a valuable source for understanding the establishment and maintenance of pregnancy as well as pathogenesis in pregnancy-related disorders.