Trajectory analysis (Monocle3)-Checkpoint2_onwards

load libraries

1. Load monocle Object

cds <- readRDS("../cds_checkpoint_2/cds_object.rds")

2. Visualization of Trajectory

p1 <- plot_cells(cds,
                color_cells_by = "pseudotime",
                label_cell_groups = FALSE,
                label_leaves = FALSE,
                label_branch_points = FALSE,
                graph_label_size = 1.5)

p1

p2 <- plot_cells(cds,
                color_cells_by = "Patient_origin",
                label_cell_groups = FALSE,
                label_leaves = FALSE,
                label_branch_points = FALSE,
                graph_label_size = 1.5)

p2

p3 <- plot_cells(cds,
                color_cells_by = "orig.ident",
                label_cell_groups = FALSE,
                label_leaves = FALSE,
                label_branch_points = FALSE,
                graph_label_size = 1.5)

p3

3. Visualize Gene Expression Along Pseudotime

plot_genes_in_pseudotime(cds["CCR7", ])

plot_genes_in_pseudotime(cds["SELL", ])

plot_genes_in_pseudotime(cds["LEF1", ])

plot_genes_in_pseudotime(cds["TCF7", ])

#Output tells you: how canonical naïve markers decline/shift across pseudotime.

Visualize Gene Expression Along Pseudotime

plot_genes_in_pseudotime(cds["CLIC1", ])

plot_genes_in_pseudotime(cds["YWHAH", ])

plot_genes_in_pseudotime(cds["TOX", ])

plot_genes_in_pseudotime(cds["TOX2", ])

plot_genes_in_pseudotime(cds["LAG3", ])

plot_genes_in_pseudotime(cds["FOXP3", ])

plot_genes_in_pseudotime(cds["IL2RA", ])

plot_genes_in_pseudotime(cds["GATA3", ])

plot_genes_in_pseudotime(cds["GATA2", ])

plot_genes_in_pseudotime(cds["IKZF2", ])

#Output tells you: how canonical naïve markers decline/shift across pseudotime.

Visualize Gene Expression Along Pseudotime

plot_genes_in_pseudotime(cds["STAT1", ])

plot_genes_in_pseudotime(cds["CXCR1", ])

plot_genes_in_pseudotime(cds["PIM2", ])

plot_genes_in_pseudotime(cds["KLF2", ])

plot_genes_in_pseudotime(cds["LGALS3", ])

plot_genes_in_pseudotime(cds["CCND2", ])

plot_genes_in_pseudotime(cds["PIM2", ])

plot_genes_in_pseudotime(cds["CD74", ])

plot_genes_in_pseudotime(cds["CD40", ])

plot_genes_in_pseudotime(cds["CD44", ])

plot_genes_in_pseudotime(cds["CD52", ])

plot_genes_in_pseudotime(cds["CD69", ])

plot_genes_in_pseudotime(cds["PRF1", ])

plot_genes_in_pseudotime(cds["GZMB", ])

plot_genes_in_pseudotime(cds["CD7", ])

plot_genes_in_pseudotime(cds["DPP4", ])

plot_genes_in_pseudotime(cds["CD28", ])

plot_genes_in_pseudotime(cds["CD27", ])

plot_genes_in_pseudotime(cds["CD8A", ])

#Output tells you: how canonical naïve markers decline/shift across pseudotime.

Visualize Gene Expression Along Pseudotime

marker_groups <- list(
  "CD4 Tex (Exhausted)" = c("TOX", "LAG3", "CTLA4", "TIGIT"),
  "CD4 Treg" = c("FOXP3", "IL2RA", "CTLA4", "IKZF2", "TIGIT"),
  "CD4 Tcm (Central Memory)" = c("CCR7", "SELL", "IL7R", "TCF7"),
  "CD4 Tn (Naive)" = c("CCR7", "SELL", "IL7R", "TCF7", "LEF1"),
  "CD4 Tem (Effector Memory)" = c("GZMB", "PRF1", "IFNG", "KLRG1"),
  "CD4 Trm (Tissue Resident)" = c("CD69", "CXCR6"),
  "CD4 Tc (Cytotoxic)" = c("PRF1", "GZMB", "NKG7", "GNLY"),
  "CD4 Tisg (IFN Signature)" = c("ISG15", "IFI6", "IFIT3", "MX1"),
  "CD4 Proliferation" = c("MKI67", "TOP2A"),
  "CD4 Th17" = c("STAT3", "AHR", "CCR6", "BATF"),
  "CD4 Temra (Effector Memory RA+)" = c("PTPRC", "GZMB"),
  "CD4 Tfh (Follicular Helper)" = c("BCL6", "ICOS"),
  "CD4 Tstr (Stress)" = c("HSPA1A", "ATF3"),
  "CD4 Activated" = c("IL2RA", "CD69", "HLA-DRA")
)

library(monocle3)
library(ggplot2)

# Flatten your marker_groups list to a single vector of unique genes
all_genes <- unique(unlist(marker_groups))

# Optional: check which genes are actually present in your cds
all_genes_present <- all_genes[all_genes %in% rownames(cds)]

# Loop over genes and plot
for (gene in all_genes_present) {
  print(plot_genes_in_pseudotime(cds[gene, ]) + ggtitle(gene))
}

Summarize Lineages and Cell Fate Branches

Visualize Gene Modules Along Pseudotime

Multi-Gene Pseudotime Plot with Facets

Multi-Gene Pseudotime Plot with Facets

Optional: Overlay CITE-seq Protein Expression

Pathway Enrichment of Top DEGs or Gene Modules

Branch Probability / Fate Bias

Explore Branch-Specific Differential Expression

Explore Branch-Specific Differential Expression

Explore Branch-Specific Differential Expression

Visualize Gene Modules Along Pseudotime