In Yutong441/TBdev: Infer a Trophoblast Developmental Trajectory

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE
)

library (TBdev)

root <- '.'
data_dir <- paste (root, 'data', sep='/')
save_dir <- paste (root, 'result', sep='/')
all_data <- get (load (paste (data_dir, 'final_merged_tb.Robj', sep='/') ))

# For simplicity we will only use a subset of the dataset, i.e., the in vivo
# set
all_data <- all_data [, all_data$date != 'in_vitro']

Pathway analysis

Obtain the module scores for each pathway for each cell. Again due to the computational intensive nature of this calculation, we recommend saving the object. You may input a datarame in all_path containing the columns 'pathway' and kegg_id.

path_seurat <- get_module_score (all_data, 
                                 save_path=paste (save_dir, 'Data_module_scores.csv', sep='/'),
                                 append_meta=T #return a Seurat object with metadata
)

Visualise the pathway scores in heatmap.

AP <- list (pointsize=3, legend_point_size=3, fontsize=11, point_fontsize=4,
            font_fam= 'sans')
setwd ('../TBdev')
devtools::load_all ()
setwd ('../vignette_test')
seurat_heat (path_seurat, 
             group.by=c('broad_type','date'),
             row_scale=T, grid_height=4, 
             main_width=20, main_height=15,
             center_scale=T, AP=AP, automatic=F
)

Alternatively, visualise selected pathways with violin plot.

seurat_violin (path_seurat, 
               features=c('MAPK', 'WNT', 'PI3K-AKT', 'steroid'), 
               group.by='broad_type', 
               slot_data='counts', #the data is stored in this slot
               num_col=2, 
               free_xy='free_y', AP=AP
)

Cell cycle analysis

Calculate the phase score according to Macosco 2015

exp_mat_cc <- as.matrix (Seurat::GetAssayData (all_data, assay='RNA', slot='data'))
ans <- get_phase_score (exp_mat_cc)

Visualise using heatmap

make_cycle_heat (ans, all_data, group.by='broad_type',
                 main_height=23, main_width=7, grid_height=4, 
                 AP=AP, automatic=F
)

Correlate cell cycle with PC

phase_PC_plot (ans, all_data, corr_feature='broad_type', 
               num_dim=1, # correlation with the PC1
               AP=AP
)

Ridgeplot

phase_density_plot (ans, all_data, corr_feature='broad_type', AP=AP)

Yutong441/TBdev documentation built on Dec. 18, 2021, 8:22 p.m.