Setup

Load objects

Filter for markers only present in adult

From Seurat Positive values of logFC indicate that the gene is more highly expressed in the first group.

Therefore, as ident 1 is embryo and ident 2 is adult, we want negative values, more expressed in second group.

There are 1720 up regulated genes in adult compared with the foetal ones. And 2750, 0 up regulated in foetal.

Test different filtering methods

We can filter by pct.1, pct.2, avg_log2FC.

As a reference, in the past we have used:

Pct group of interest < 0.25

Pct other group > 0.6

avg_log2 > |1.2|

Broader filtering and top 10 genes from log2FC sorting

fil_pct_1_dev < 0.25

fil_pct_2_adult > 0.5

Click to expand

Very top genes for the three parametres

Here I take the top genes of each category, and keep the genes that satisfy the three conditions.

avg_log2FC < -1.3345376

pct.1_dev < 0.072

pct.2_adult >0.641

10 top genes

Click to expand

Longuer list of top genes for the three parametres

Increase even more the number of top genes from each category.

avg_log2FC < -0.9844015

pct.1_dev < 0.138

pct.2_adult >0.53

28 top genes

Click to expand

Filter for markers only present in embryos

From Seurat Positive values of logFC indicate that the gene is more highly expressed in the first group

Therefore, as ident 1 is embryo and ident 2 is adult, we want positive values, more expressed in first group.

There are 2750 up regulated genes in foetal compared with the adult ones. And 1720, 5 up regulated in adults.

Test different filtering methods

Take from each of the three categories we can filter by (pct.1, pct.2, avg_log2FC) the top genes, and keep the genes that satisfy the three conditions.

Broader filtering and top 10 genes from log2FC sorting

fil_pct_2_adult < 0.25

fil_pct_1_dev > 0.6

Click to expand

Very top genes in the three parametres

avg_log2FC > 1.132685

pct.1_dev >0.893

pct.2_adult < 0.01

8 top genes

Click to expand 

## Warning in FeaturePlot(srt_combined, features = top_genes, split.by =
## "AdultVsEmbryo"): All cells have the same value (0) of ST8SIA2.

Long list of top genes for the three parametres

Increase the number of top genes from each category.

avg_log2FC > 0.9051493

pct.1_dev >0.829

pct.2_adult < 0.021

30 genes
Click to expand 

## Warning in FeaturePlot(srt_combined, features = top_genes, split.by =
## "AdultVsEmbryo"): All cells have the same value (0) of ST8SIA2.

Session info

Click to expand 
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] dplyr_1.0.5        here_1.0.1         SeuratObject_4.0.0 Seurat_4.0.0      
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15           colorspace_2.0-0     deldir_0.2-10       
##   [4] ellipsis_0.3.1       ggridges_0.5.3       rprojroot_2.0.2     
##   [7] spatstat.data_2.0-0  leiden_0.3.7         listenv_0.8.0       
##  [10] farver_2.1.0         ggrepel_0.9.1        fansi_0.4.2         
##  [13] codetools_0.2-18     splines_4.0.4        knitr_1.31          
##  [16] polyclip_1.10-0      jsonlite_1.7.2       ica_1.0-2           
##  [19] cluster_2.1.1        png_0.1-7            uwot_0.1.10         
##  [22] shiny_1.6.0          sctransform_0.3.2    compiler_4.0.4      
##  [25] httr_1.4.2           assertthat_0.2.1     Matrix_1.3-2        
##  [28] fastmap_1.1.0        lazyeval_0.2.2       later_1.1.0.1       
##  [31] htmltools_0.5.1.1    tools_4.0.4          igraph_1.2.6        
##  [34] gtable_0.3.0         glue_1.4.2           RANN_2.6.1          
##  [37] reshape2_1.4.4       Rcpp_1.0.6           spatstat_1.64-1     
##  [40] scattermore_0.7      jquerylib_0.1.3      vctrs_0.3.6         
##  [43] nlme_3.1-152         lmtest_0.9-38        xfun_0.21           
##  [46] stringr_1.4.0        globals_0.14.0       mime_0.10           
##  [49] miniUI_0.1.1.1       lifecycle_1.0.0      irlba_2.3.3         
##  [52] goftest_1.2-2        future_1.21.0        MASS_7.3-53.1       
##  [55] zoo_1.8-9            scales_1.1.1         promises_1.2.0.1    
##  [58] spatstat.utils_2.1-0 parallel_4.0.4       RColorBrewer_1.1-2  
##  [61] yaml_2.2.1           reticulate_1.18      pbapply_1.4-3       
##  [64] gridExtra_2.3        ggplot2_3.3.3        sass_0.3.1          
##  [67] rpart_4.1-15         stringi_1.5.3        highr_0.8           
##  [70] rlang_0.4.10         pkgconfig_2.0.3      matrixStats_0.58.0  
##  [73] evaluate_0.14        lattice_0.20-41      ROCR_1.0-11         
##  [76] purrr_0.3.4          tensor_1.5           patchwork_1.1.1     
##  [79] htmlwidgets_1.5.3    labeling_0.4.2       cowplot_1.1.1       
##  [82] tidyselect_1.1.0     parallelly_1.24.0    RcppAnnoy_0.0.18    
##  [85] plyr_1.8.6           magrittr_2.0.1       R6_2.5.0            
##  [88] generics_0.1.0       DBI_1.1.1            pillar_1.5.1        
##  [91] withr_2.4.1          mgcv_1.8-34          fitdistrplus_1.1-3  
##  [94] survival_3.2-10      abind_1.4-5          tibble_3.1.0        
##  [97] future.apply_1.7.0   crayon_1.4.1         KernSmooth_2.23-18  
## [100] utf8_1.2.1           plotly_4.9.3         rmarkdown_2.7       
## [103] grid_4.0.4           data.table_1.14.0    digest_0.6.27       
## [106] xtable_1.8-4         tidyr_1.1.3          httpuv_1.5.5        
## [109] munsell_0.5.0        viridisLite_0.3.0    bslib_0.2.4