Enrichment_escape-Visualization Notebook

1. load libraries

2. Load Seurat Object

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
SS_All_samples_Merged <- load("../SS_Escape_final-c2.Robj")

3. Escape

# # 1: Molecular Signture Database
# GS.hallmark <- getGeneSets(
#                             species = "Homo sapiens",
#                             library = c("H","C1", "C2", "C3", "C4" ,"C5", "C6","C7","C8"))
#                               
#                         
# 
# #2: Built-In gene sets
#  data("escape.gene.sets", package="escape")
#  gene.sets <- escape.gene.sets
# 
# #3: Define personal gene sets
# gene.sets <- list(Bcells = c("MS4A1","CD79B","CD79A","IGH1","IGH2"),
#                         Myeloid = c("SPI1","FCER1G","CSF1R"),
#                         Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))

# # Enrichment
# enrichment.scores <- escape.matrix(SS_escape, 
#                                    gene.sets = GS.hallmark, 
#                                    groups = 5000, 
#                                    min.size = 5)

# ggplot(data = as.data.frame(enrichment.scores), 
#       mapping = aes(x = `HALLMARK-DNA-REPAIR`, `HALLMARK-G2M-CHECKPOINT`)) + 
#       geom_point() + 
#       theme_classic() + 
#       theme()

# Alternatively, we can use runEscape() to calculate the enrichment score and directly attach the output to a single-cell object.
# SS_escape <- runEscape(SS_escape, 
#                         method = "ssGSEA",
#                         gene.sets = GS.hallmark, 
#                         groups = 5000, 
#                         min.size = 0,
#                         new.assay.name = "escape.ssGSEA")
# 
# # if working with a Seurat object
# #SS_escape  <- Seurat::AddMetaData(SS_escape , ES.seurat)
# 
# 
# #Define color palette 
# colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)
# 
# FeaturePlot(SS_escape, "HALLMARK-APOPTOSIS") + 
#   scale_color_gradientn(colors = colorblind_vector) + 
#   theme(plot.title = element_blank())
# 
# # perform Normalization
# SS_escape <- performNormalization(SS_escape, 
#                                   assay = "escape.ssGSEA", 
#                                   gene.sets = GS.hallmark)
# 
# #scaling 
# SS_escape <- performNormalization(SS_escape, 
#                                   assay = "escape.ssGSEA", 
#                                   gene.sets = GS.hallmark, 
#                                   scale.factor = SS_escape$nFeature_RNA)

4. Escape Visualization

# Visualizations
#gene.set.use set to first 12 gene sets. Alternatively, we can plot all gene sets using gene.set.use = “all”.

pathway_set <- c(
    "HALLMARK_TNFA_SIGNALING_VIA_NFKB",
    "HALLMARK_E2F_TARGETS",
    "HALLMARK_G2M_CHECKPOINT",
    "HALLMARK_ALLOGRAFT_REJECTION",
    "HALLMARK_IL2_STAT5_SIGNALING",
    "HALLMARK_INFLAMMATORY_RESPONSE",
    "HALLMARK_MYC_TARGETS_V2",
    "HALLMARK_CHOLESTEROL_HOMEOSTASIS",
    "HALLMARK_APOPTOSIS",
    "HALLMARK_IL6_JAK_STAT3_SIGNALING",
    "HALLMARK_MTORC1_SIGNALING",
    "HALLMARK_INTERFERON_GAMMA_RESPONSE"
)

pathway_set <- gsub("_", "-", pathway_set)



heatmapEnrichment(
  SS_escape,
  group.by = "seurat_clusters",
  gene.set.use = pathway_set,
  assay = "escape.ssGSEA",
  palette = "Spectral",
  scale = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "seurat_clusters",
  gene.set.use = pathway_set,
  assay = "escape.ssGSEA",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "orig.ident",
  gene.set.use = pathway_set,
  assay = "escape.ssGSEA",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "Patient_origin",
  gene.set.use = pathway_set,
  assay = "escape.ssGSEA",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

KEGG_list <- c(
    "KEGG-GRAFT-VERSUS-HOST-DISEASE",
    "KEGG-TYPE-I-DIABETES-MELLITUS",
    "KEGG-ALLOGRAFT-REJECTION",
    "KEGG-PRIMARY-IMMUNODEFICIENCY",
    "KEGG-DNA-REPLICATION",
    "KEGG-CELL-CYCLE",
    "KEGG-CELL-ADHESION-MOLECULES-CAMS",
    "KEGG-HEMATOPOIETIC-CELL-LINEAGE",
    "KEGG-CYTOKINE-CYTOKINE-RECEPTOR-INTERACTION",
    "KEGG-P53-SIGNALING-PATHWAY",
    "KEGG-NATURAL-KILLER-CELL-MEDIATED-CYTOTOXICITY",
    "KEGG-ANTIGEN-PROCESSING-AND-PRESENTATION",
    "KEGG-T-CELL-RECEPTOR-SIGNALING-PATHWAY",
    "KEGG-SYSTEMIC-LUPUS-ERYTHEMATOSUS"
)



heatmapEnrichment(
  SS_escape,
  group.by = "Patient_origin",
  gene.set.use = KEGG_list,
  assay = "escape.ssGSEA_C2",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "cell_line",
  gene.set.use = KEGG_list,
  assay = "escape.ssGSEA_C2",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)
gc()

             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   16298357   870.5   27909668  1490.6   27909668  1490.6
Vcells 1579266846 12048.9 2727668158 20810.5 2212444142 16879.7

heatmapEnrichment(
  SS_escape,
  group.by = "seurat_clusters",
  gene.set.use = KEGG_list,
  assay = "escape.ssGSEA_C2",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "Patient_origin",
  gene.set.use = "all",
  assay = "escape2.ssGSEA",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "Patient_origin",
  gene.set.use = "all",
  assay = "escape2.ssGSEA",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "seurat_clusters",
  gene.set.use = "all",
  assay = "escape2.ssGSEA",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

heatmapEnrichment(
  SS_escape,
  group.by = "cell_line",
  gene.set.use = "all",
  assay = "escape2.ssGSEA",
  palette = "Spectral",
  scale = TRUE,
  cluster.rows = TRUE,
  cluster.columns = TRUE
)

NA
NA

## geyserEnrichment

geyserEnrichment(SS_escape,
                 assay = "escape.ssGSEA",
                 gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE")

geyserEnrichment(SS_escape,
                 assay = "escape.ssGSEA",
                 gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
                 order.by = "mean")

NA
NA
NA

# ridgeEnrichment

ridgeEnrichment(
  SS_escape,
  assay = "escape.ssGSEA",
  gene.set = "HALLMARK-IL2-STAT5-SIGNALING"
)

ridgeEnrichment(
  SS_escape,
  assay = "escape.ssGSEA",
  gene.set = "HALLMARK-IL2-STAT5-SIGNALING",
  add.rug = TRUE,
  scale = TRUE
)

# scatterEnrichment

scatterEnrichment(
  SS_escape,
  assay = "escape.ssGSEA",
  x.axis = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
  y.axis = "HALLMARK-IL6-JAK-STAT3-SIGNALING"
  )

scatterEnrichment(
  SS_escape,
  assay = "escape.ssGSEA",
  x.axis = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
  y.axis = "HALLMARK-IL6-JAK-STAT3-SIGNALING",
  style = "hex"
)