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Fig. 2 | Journal of Experimental & Clinical Cancer Research

Fig. 2

From: Spatially resolved transcriptomics provide a new method for cancer research

Fig. 2

a In the research of Wu et al. (2021), a Pearson correlation analysis of the spatial distribution of different cell types within breast cancer tissues was performed. MyCAFs and iCAFs are negatively correlated in spatial distribution, and iCAFs with appear to co-localize spatially with T cells. b In Casasent et al. (2018) research, breast tumor cells were divided into normal, invasive, and in situ cell on pathological images. c Casasent et al. (2018) used Timescape to plot clonal lineages of the major tumor subpopulation, with common ancestors indicated in grey and clonal frequencies labeled for the in situ and invasive regions. The results demonstrate that genome evolution occurs within the ducts before the tumor cells escape the basement membrane. d Berglund et al. (2018) selected several regions with different pathological annotations b. Berglund et al. (2018) sequenced the spatial transcriptomics of different tissues separately and selected 10 RNAs by factor analysis, with each factor corresponding to an activity map. e Massalha et al. (2020) found the expression of the ligand SLIT2 mRNAs (red dots) in pericytes (marked by red dashed lines) and ROBO4 mRNAs (green dots) in endothelial cells (marked by green dashed lines). f Massalha et al. (2020) used the NicheNet to detect the interaction between pericytes and endothelial cells. g Massalha et al. (2020) performed an enrichment analysis of the highly correlated pathways and replicated it in multiple samples from different patients. h In Levy-Jurgenson et al. (2020) study, Generate tensor molecular thermograms from pathological sections, subsequently generate inhomogeneous distribution maps of cells, and generate heterogeneity indices using the formula

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