Reading:

After reducing table in such a way that when a gene is marker for more than one cluster, the cluster with higher log2FC is selected i.e. markers are unique for a cluster:

       blood  decidual_stroma      endothelial fetal_mesenchyme 
         570              277              461              823 
          GC             JZP1             JZP2             LaTP 
         203               89               44              392 
       LaTP2            S_TGC   S_TGCPrecursor              SpT 
          60              341               21              330 
SpTPrecursor            SynTI           SynTII  SynTIIPrecursor 
          33              451              241              122

SynTIPrecursor 84

The list of unique markers will be used for all plots.

1 GSEA using placental markers

2 Pseudotime using placental markers expression

Expression of markers from cell types of the labyrinth is more predictive of the genotype than junctional zone’s ones. One reason could be the more variable presence of JZ tissue in the single dissected placentae.

3 Pseudotime using DEGs expression

sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=it_IT.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=it_IT.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=it_IT.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] enrichplot_1.14.2           org.Mm.eg.db_3.14.0        
##  [3] AnnotationDbi_1.56.2        clusterProfiler_4.2.2      
##  [5] data.table_1.14.6           gridExtra_2.3              
##  [7] gplots_3.1.3                ggpubr_0.5.0               
##  [9] pheatmap_1.0.12             reshape2_1.4.4             
## [11] ggrepel_0.9.2               ggplot2_3.4.0              
## [13] DESeq2_1.34.0               SummarizedExperiment_1.24.0
## [15] Biobase_2.54.0              MatrixGenerics_1.6.0       
## [17] matrixStats_0.62.0          GenomicRanges_1.46.1       
## [19] GenomeInfoDb_1.30.1         IRanges_2.28.0             
## [21] S4Vectors_0.32.4            BiocGenerics_0.40.0        
## 
## loaded via a namespace (and not attached):
##   [1] shadowtext_0.1.2       backports_1.4.1        fastmatch_1.1-3       
##   [4] plyr_1.8.8             igraph_1.3.5           lazyeval_0.2.2        
##   [7] splines_4.1.3          BiocParallel_1.28.3    digest_0.6.30         
##  [10] yulab.utils_0.0.5      htmltools_0.5.3        GOSemSim_2.20.0       
##  [13] viridis_0.6.2          GO.db_3.14.0           fansi_1.0.3           
##  [16] magrittr_2.0.3         memoise_2.0.1          Biostrings_2.62.0     
##  [19] annotate_1.72.0        graphlayouts_0.8.3     colorspace_2.0-3      
##  [22] blob_1.2.3             xfun_0.35              dplyr_1.0.10          
##  [25] crayon_1.5.2           RCurl_1.98-1.9         jsonlite_1.8.3        
##  [28] scatterpie_0.1.8       genefilter_1.76.0      survival_3.2-13       
##  [31] ape_5.6-2              glue_1.6.2             polyclip_1.10-4       
##  [34] gtable_0.3.1           zlibbioc_1.40.0        XVector_0.34.0        
##  [37] DelayedArray_0.20.0    car_3.1-1              abind_1.4-5           
##  [40] scales_1.2.1           DOSE_3.20.1            DBI_1.1.3             
##  [43] rstatix_0.7.1          Rcpp_1.0.9             viridisLite_0.4.1     
##  [46] xtable_1.8-4           gridGraphics_0.5-1     tidytree_0.4.1        
##  [49] bit_4.0.5              httr_1.4.4             fgsea_1.20.0          
##  [52] RColorBrewer_1.1-3     pkgconfig_2.0.3        XML_3.99-0.12         
##  [55] farver_2.1.1           sass_0.4.2             locfit_1.5-9.6        
##  [58] utf8_1.2.2             labeling_0.4.2         ggplotify_0.1.0       
##  [61] tidyselect_1.2.0       rlang_1.0.6            munsell_0.5.0         
##  [64] tools_4.1.3            cachem_1.0.6           downloader_0.4        
##  [67] cli_3.4.1              generics_0.1.3         RSQLite_2.2.18        
##  [70] broom_1.0.1            evaluate_0.18          stringr_1.4.1         
##  [73] fastmap_1.1.0          yaml_2.3.6             ggtree_3.2.1          
##  [76] knitr_1.40             bit64_4.0.5            tidygraph_1.2.2       
##  [79] caTools_1.18.2         purrr_0.3.5            KEGGREST_1.34.0       
##  [82] ggraph_2.1.0           nlme_3.1-155           aplot_0.1.8           
##  [85] DO.db_2.9              compiler_4.1.3         rstudioapi_0.14       
##  [88] png_0.1-7              ggsignif_0.6.4         treeio_1.18.1         
##  [91] tibble_3.1.8           tweenr_2.0.2           geneplotter_1.72.0    
##  [94] bslib_0.4.1            stringi_1.7.8          highr_0.9             
##  [97] lattice_0.20-45        Matrix_1.5-3           vctrs_0.5.1           
## [100] pillar_1.8.1           lifecycle_1.0.3        jquerylib_0.1.4       
## [103] bitops_1.0-7           patchwork_1.1.2        qvalue_2.26.0         
## [106] R6_2.5.1               KernSmooth_2.23-20     codetools_0.2-18      
## [109] MASS_7.3-55            gtools_3.9.3           assertthat_0.2.1      
## [112] withr_2.5.0            GenomeInfoDbData_1.2.7 parallel_4.1.3        
## [115] grid_4.1.3             prettydoc_0.4.1        ggfun_0.0.8           
## [118] tidyr_1.2.1            rmarkdown_2.18         carData_3.0-5         
## [121] ggforce_0.4.1