Figure gallery

library(ithi.utils)
load_base_libs()

## grid graphics
library(grid)
library(gridExtra)
library(gridGraphics)
library(extrafont)
library(gridBase)
library(ape)
library(phylobase)
library(phytools)
library(OutbreakTools)
library(colorspace)
library(RColorBrewer)
library(igraph)

library(ithi.meta)
library(ithi.xcr)
library(ithi.clones)
library(ithi.expr)
library(ithi.figures)
library(ithi.seq)

Colour palettes

pal_patient <- select_palette("patient")

Parameters

db_path <- snakemake@params$db

ihc_table_path <- snakemake@input$ihc_table
xcr_table_path <- snakemake@input$xcr_table
nanostring_data_path <- snakemake@input$nanostring_data
nanostring_annotations_path <- snakemake@input$nanostring_annotations
master_variant_file <- snakemake@input$master_variant_file
trb_annotated_path <- snakemake@input$trb_annotated

# igpartition_outdir <- snakemake@input$igpartition_outdir
bcrphylo_figure <- snakemake@input$bcrphylo_png
mutsig_figure <- snakemake@input$mutsig_png
immune_cancer_figure <- snakemake@input$immune_cancer_png
immune_overview_figure <- snakemake@input$immune_overview_png
merged_figures_path <- snakemake@input$merged_figures

Metadata

db <- src_sqlite(db_path, create = FALSE)

samples <- collect(tbl(db, "samples"))

Data

ihc_table <- fread(ihc_table_path)
xcr_table <- read_clonotypes(xcr_table_path, duplicates = FALSE, db_path)

Read 19.7% of 304822 rows
Read 45.9% of 304822 rows
Read 88.6% of 304822 rows
Read 304822 rows and 18 (of 18) columns from 0.070 GB file in 00:00:06

exprs <- fread(nanostring_data_path)
labels <- fread(nanostring_annotations_path)
trb_annotated <- fread(trb_annotated_path)
master_variant_table <- read_variant_file(master_variant_file, db_path)

Figures

Will be updated as I work on more of these. Note: Fonts will always be ugly on here because genesis doesn’t have ANY truetype fonts :(

Merged

Link to merged figures

Figure 1

Landscape figure showing sample statistics. Base R graphics are very fast!

condensed_ids <- unique(subset(samples, project_code == "ITH" & !source %in% 
    c("blood", "normal"))$condensed_id)
condensed_ids <- as.character(sort(factor_id(condensed_ids, type = "condensed_id", 
    db_path)))

plot_landscape_figure(db_path, condensed_ids = condensed_ids, ihc_table = ihc_table, 
    xcr_table = xcr_table, variant_table = master_variant_table, separate_opt = "zebra")

WGS 

IHC 

TCR/BCR-seq 

plot_landscape_figure(db_path, condensed_ids = condensed_ids, ihc_table = ihc_table, 
    xcr_table = xcr_table, variant_table = master_variant_table, separate_opt = "line")

WGS 

IHC 

TCR/BCR-seq 

Plotted w/ zebra and line separators in case you like one better. Also added patient labels.

Figure 2

Immune overview figure. If you have any suggestions on what to include/remove please let me know!

Top to bottom:

• Patient colour label legend
• TCR (left) and BCR (right) clonotype sharing illustrated by Circos plots
• Mean expression of certain pathways as indicated by Nanostring annotations
• Intrapatient similarity in TCR repertoire
• High correlation between TCR repertoire similarity and CD8+ TIL density (left), lower correlation with CD4+ TIL density (middle) and CD8+’s are more broadly distributed across tumour sites than CD4+’s (right)

Figure 3

B-cell phylogenetics.

Subfigures:

• Evidence of either B-cell exchange or parallel affinity maturation across tumour sites
• Evidence of lineage divergence by tumour site
• Evidence of phylogenetic signal in readcounts (sequence-dependent expansion)
• Correlations between GSI/phylosignal and CD20+ TIL densities

# plot_bcrphylo_figure(resultdir = igpartition_outdir, ihc_table, parts =
# list(A=list(patient=2,clust=7), B=list(patient=18, clust=10),
# C=list(patient=9, clust=14)))

Figure 4

Mutational signatures and immune activity.

Subfigures:

• SNV signatures
• SV signatures
• Clustering by mutation signature proportions
• Expression-derived immune covariates overlaid on mutation signature-based clusters
• TCGA survival analysis

Figure 5

Immune-cancer interaction.

Figure 6