Rooney comparison
Setup
library(ithi.utils)
load_base_libs()
library(methods)
library(bedr)
library(biomaRt)
library(BSgenome.Hsapiens.UCSC.hg19)
library(gdata)
library(ithi.meta)
library(ithi.figures)
library(ithi.utils)
library(ithi.expr)
library(ithi.seq)
library(ithi.bed)
library(ithi.clones)
library(ithi.supp)nanostring_data_path <- snakemake@input$nanostring_data
nanostring_annotations_path <- snakemake@input$nanostring_annotations
ihc_table_path <- snakemake@input$ihc_table
master_variant_file <- snakemake@input$snv_table
master_breakpoint_file <- snakemake@input$breakpoint_table
somatic_coding_result_dir <- snakemake@input$somatic_coding_result_dir
neoediting_outdir <- snakemake@input$neoediting_outdir
snv_cluster_files <- snakemake@input$snv_cluster_files
clone_tree_file <- snakemake@input$clone_tree_file
clone_branch_length_file <- snakemake@input$clone_branch_length_file
clone_prevalence_file <- snakemake@input$clone_prevalence_file
rooney_full_mutsigcv_list_file <- snakemake@params$rooney_mutsigcv_file
refseq_gene_file <- snakemake@params$refseq_gene_file
db_path <- snakemake@params$db
all_tiltypes <- snakemake@params$all_tiltypesannotation_colours <- ithi.figures::get_annotation_colours()
ihc_table <- fread(ihc_table_path)
mart <- useDataset("hsapiens_gene_ensembl", useMart(biomart = "ENSEMBL_MART_ENSEMBL",
host = "feb2014.archive.ensembl.org"))
titan_file <- file.path(somatic_coding_result_dir, "somatic_cnv_titan.tsv")
somatic_snv_file <- file.path(somatic_coding_result_dir, "somatic_snvs.tsv")
somatic_indel_file <- file.path(somatic_coding_result_dir, "somatic_indels.tsv")
titan_cnv <- fread(titan_file)
somatic_snvs <- fread(somatic_snv_file)
somatic_indels <- fread(somatic_indel_file)
master_variant_table <- read_variant_file(master_variant_file, db_path)
Read 0.0% of 682516 rows
Read 682516 rows and 9 (of 9) columns from 0.026 GB file in 00:00:04master_breakpoint_table <- read_variant_file(master_breakpoint_file, db_path)
exprs <- fread(nanostring_data_path)
nanostring_labels <- fread(nanostring_annotations_path)
tree_branch_data <- read_clone_tree_data(clone_tree_file, clone_branch_length_file,
clone_prevalence_file, db_path)Rooney comparison
In this document, we’ll compare to both the Rooney variant-immune association findings and the neoepitope elimination findings.
Analysis
First, we’ll see whether or not particular mutations (the same set as Rooney) are associated with immune activity (cytotoxicity gene signature or IHC variables) in our cohort.
somatic_snvs_filtered <- plyr::join(somatic_snvs, master_variant_table %>% subset(is_present ==
1), type = "inner")
somatic_indels_filtered <- subset(somatic_indels, mappability == 1)
titan_bed <- convert_to_bed(titan_cnv)
refseq_bed <- read_bed(refseq_gene_file)
gene_names <- getBM(attributes = c("refseq_mrna", "hgnc_symbol"), values = refseq_bed$refseq_id,
mart = mart)
refseq_bed_annotated <- merge(refseq_bed, gene_names %>% subset(refseq_mrna !=
"") %>% plyr::rename(c(refseq_mrna = "refseq_id")), by = c("refseq_id"))
refseq_bed_annotated <- subset(refseq_bed_annotated, select = c("chr", "start",
"end", "hgnc_symbol", "refseq_id"))
refseq_bed_annotated <- bedr.sort.region(refseq_bed_annotated)
titan_merged <- bedr(engine = "bedtools", input = list(a = titan_bed, b = refseq_bed_annotated),
method = "intersect", params = "-loj")
titan_merged <- data.frame(titan_merged)
titan_merged$median_logR <- as.numeric(titan_merged$median_logR)
titan_merged$total <- as.numeric(titan_merged$total)
titan_merged$major <- as.numeric(titan_merged$major)
titan_merged$minor <- as.numeric(titan_merged$minor)rooney_cytolytic_pancancer_association_genes <- c("CASP8", "B2M", "PIK3CA",
"SMC1A", "TET2", "ARID5B", "ALPK2", "LPAR2", "COL5A1", "TP53", "NCOR1",
"SSX5", "DNER", "MORC4", "IRF6", "MYOCD", "CIC", "SLC22A14", "CNKSR1", "NF1",
"SOS1", "CUL4B", "DDX3X", "FUBP1", "HLA-A", "HLA-B", "HLA-C", "ARID2", "TCP11L2",
"MET", "CSNK2A1", "ASXL1", "TMEM88", "DNMT3A", "EP300", "MUC17", "OVOL1")
rooney_mutsigcv_table <- read.xls(rooney_full_mutsigcv_list_file, sheet = 1,
header = FALSE, stringsAsFactors = FALSE)
rooney_mutsigcv_genes <- sapply(strsplit(rooney_mutsigcv_table$V1, ":"), function(x) x[1])
rooney_mutsigcv_genes <- rooney_mutsigcv_genes[rooney_mutsigcv_genes != "HLA-A,B,C"]
rooney_mutsigcv_genes <- c(rooney_mutsigcv_genes, c("HLA-A", "HLA-B", "HLA-C"))Linear modeling for mutations
Check whether this is done only for point mutations or indels too.
They only do it for point mutations; I think this makes sense because of the low indel count. Let’s do it similarly, then.
somatic_snvs_rooney <- subset(somatic_snvs_filtered, gene_name %in% rooney_mutsigcv_genes)
somatic_indels_rooney <- subset(somatic_indels_filtered, gene_name %in% rooney_mutsigcv_genes)
titan_merged_rooney <- subset(titan_merged, hgnc_symbol %in% rooney_mutsigcv_genes)perc.rank <- function(x) trunc(rank(x))/length(x)
compute_num_transition_events <- function(titan_merged) {
titan_merged_unique <- subset(titan_merged, select = c(chr, start, end,
patient_id, condensed_id, median_logR, major, minor, total)) %>% unique
titan_merged_unique_ordered <- titan_merged_unique[with(titan_merged_unique,
order(condensed_id, chr, start, end)), ]
dfs <- split(titan_merged_unique_ordered, f = titan_merged_unique_ordered$condensed_id)
event_counts <- lapply(dfs, function(x) {
counter <- 0
prev_chrom <- "chr0"
prev_cn_sign <- 1
for (i in 1:nrow(x)) {
row <- x[i, ]
if ((row$chr == prev_chrom) & (sign(row$total - 2) != prev_cn_sign)) {
counter <- counter + 1
}
prev_chrom <- row$chr
prev_logR_sgn <- sign(row$total)
}
return(counter)
})
event_df <- data.frame(event_counts, check.names = FALSE) %>% t %>% as.data.frame
colnames(event_df) <- "chr_events"
event_df <- event_df %>% rownames_to_column(var = "condensed_id")
return(event_df)
}
transition_events <- compute_num_transition_events(titan_merged)
cyt_expr <- subset(exprs, Name %in% c("GZMA", "PRF1"), select = -c(Code.Class,
Accession))
cyt_expr_combined <- cyt_expr %>% as.data.frame %>% column_to_rownames(var = "Name") %>%
t %>% as.data.frame
cyt_expr_combined$CYT <- with(cyt_expr_combined, ithi.utils::p.geomean(GZMA,
PRF1))
cyt_expr_combined <- subset(cyt_expr_combined, select = -c(GZMA, PRF1))
cyt_expr_df <- cyt_expr_combined %>% rownames_to_column(var = "condensed_id")
cyt_expr_df$condensed_id <- cyt_expr_df$condensed_id %>% ithi.meta::map_id(from = "voa",
to = "condensed_id", db_path)
linear_model_til_variant <- function(selected_variants, all_variants, ihc_table,
cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT",
variant_type = "snv", cn_type = "amp", transition_events = NULL) {
if (variant_type == "snv") {
background_variant_rate <- all_variants %>% group_by(condensed_id) %>%
summarise(nvariant = n())
} else if (variant_type == "cnv") {
all_variants_filtered <- subset(all_variants, total != 2)
all_variants_filtered_unique <- subset(all_variants_filtered, select = c(chr,
start, end, patient_id, condensed_id, median_logR, major, minor,
total)) %>% unique
all_variants_filtered_unique$event_type <- sign(all_variants_filtered_unique$total -
2)
background_variant_rate <- all_variants_filtered_unique %>% group_by(condensed_id,
event_type) %>% summarise(nvariant = n())
background_variant_rate_expanded <- dcast(background_variant_rate, formula = condensed_id ~
event_type, value.var = "nvariant") %>% plyr::rename(c(`-1` = "ndel",
`1` = "namp"))
background_variant_rate <- merge(background_variant_rate_expanded, transition_events,
all = TRUE)
## Transition events is the number of chromosomal transitions between +/-
## logR's, as per Rooney.
}
ihc_table_subset <- subset(ihc_table, condensed_id %in% background_variant_rate$condensed_id,
select = c("condensed_id", all_tiltypes))
other_tables <- Reduce(function(x, y) merge(x, y, all = TRUE), list(background_variant_rate,
ihc_table_subset, cyt_expr_df))
other_tables <- subset(other_tables, !str_detect(condensed_id, "^SP"))
other_tables$patient_id <- ithi.meta::map_id(other_tables$condensed_id,
from = "condensed_id", to = "patient_id", db_path)
selected_variants$condensed_id <- ithi.meta::factor_id(selected_variants$condensed_id,
type = "condensed_id", db_path)
if (variant_type == "cnv") {
if (cn_type == "amp") {
selected_variants <- subset(selected_variants, total > 2)
} else if (cn_type == "del") {
selected_variants <- subset(selected_variants, total < 2)
}
selected_variants$gene_name <- selected_variants$hgnc_symbol
selected_variants <- subset(selected_variants, select = c(chr, start,
end, patient_id, condensed_id, median_logR, major, minor, total,
gene_name)) %>% unique
selected_variants <- selected_variants %>% group_by(gene_name, condensed_id,
patient_id) %>% summarise(median_logR = max(abs(median_logR)) *
sign(median_logR[which.max(abs(median_logR))]))
foreground_table <- as.data.frame.matrix(xtabs(median_logR ~ condensed_id +
gene_name, data = selected_variants))
} else {
foreground_table <- as.data.frame.matrix(xtabs(~condensed_id + gene_name,
data = selected_variants))
}
gene_names <- colnames(foreground_table)
test_results <- rbind.fill(lapply(gene_names, function(gene) {
# print(gene)
x <- foreground_table[, gene, drop = FALSE] %>% tibble::rownames_to_column(var = "condensed_id")
x <- merge(other_tables, x, all = TRUE)
x[, gene][is.na(x[, gene]) & (x$condensed_id %in% background_variant_rate$condensed_id)] <- 0
if (variant_type == "cnv") {
if (cn_type == "amp") {
x[, gene][!is.na(x[, gene]) & (x[, gene] < 0)] <- 0
} else if (cn_type == "del") {
x[, gene][!is.na(x[, gene]) & (x[, gene] > 0)] <- 0
x[, gene] <- -1 * x[, gene]
}
}
if (rank_transform) {
if (variant_type == "snv") {
x[, "nvariant"] <- perc.rank(x[, "nvariant"])
} else if (variant_type == "cnv") {
x[, "chr_events"] <- perc.rank(x[, "chr_events"])
x[, "namp"] <- perc.rank(x[, "namp"])
x[, "ndel"] <- perc.rank(x[, "ndel"])
}
x[, immune_variable] <- perc.rank(x[, immune_variable])
}
if (str_detect(gene, "\\-")) {
gene_name_mod <- paste0("`", gene, "`")
} else {
gene_name_mod <- gene
}
if (variant_type == "snv") {
if (control_patient) {
mod <- lmer(as.formula(paste0(immune_variable, "~", gene_name_mod,
"+", "nvariant", "+", "(1|patient_id)")), data = x[!is.na(x[,
gene]), ])
} else {
mod <- lm(as.formula(paste0(immune_variable, "~", gene_name_mod,
"+", "nvariant")), data = x[!is.na(x[, gene]), ])
}
} else if (variant_type == "cnv") {
if (control_patient) {
mod <- lmer(as.formula(paste0(immune_variable, "~", gene_name_mod,
"+", "namp+ndel+chr_events", "+", "(1|patient_id)")), data = x[!is.na(x[,
gene]), ])
} else {
mod <- lm(as.formula(paste0(immune_variable, "~", gene_name_mod,
"+", "namp+ndel+chr_events")), data = x[!is.na(x[, gene]),
])
}
}
pval <- unname(summary(mod)$coefficients[, 5][gene_name_mod])
coeff <- unname(summary(mod)$coefficients[, 1][gene_name_mod])
return(data.frame(gene = gene, immune_variable = immune_variable, p.value = pval,
coef = coeff, n = nrow(x[!is.na(x[, gene]), ])))
}))
test_results$p.adj <- p.adjust(test_results$p.value, method = "BH")
return(test_results)
}snv_cyt_patient_rank <- linear_model_til_variant(somatic_snvs_rooney, somatic_snvs_filtered,
ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT",
variant_type = "snv")
snv_ecd8_patient_rank <- linear_model_til_variant(somatic_snvs_rooney, somatic_snvs_filtered,
ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "E_CD8_density",
variant_type = "snv")print(snv_cyt_patient_rank) gene immune_variable p.value coef n p.adj
1 ANK3 CYT 0.25854215 0.240153334 66 0.8338131
2 APC CYT 0.47917542 -0.068488526 66 0.9342232
3 ARHGAP35 CYT 0.02600806 -0.326131817 66 0.2860886
4 AXIN2 CYT 0.57064596 0.042833163 66 0.9342232
5 BRCA2 CYT 0.75599939 0.107637049 66 0.9342232
6 CCDC6 CYT 0.80265166 -0.037541699 66 0.9459823
7 CD300LG CYT 0.47390514 -0.245355174 66 0.9342232
8 COL5A1 CYT 0.57064596 0.042833163 66 0.9342232
9 CYP7B1 CYT 0.73290290 0.057842676 66 0.9342232
10 DNAH12 CYT 0.55431705 -0.127321847 66 0.9342232
11 ERCC2 CYT 0.02600806 -0.326131817 66 0.2860886
12 FAT1 CYT 0.75599939 0.107637049 66 0.9342232
13 FLG CYT 0.69660040 -0.034815283 66 0.9342232
14 GZMA CYT 0.75599939 0.107637049 66 0.9342232
15 IRF4 CYT 0.15324640 0.239887097 66 0.6321414
16 ITPKB CYT 0.99053177 -0.001940611 66 0.9905318
17 MPO CYT 0.13520502 -0.273831365 66 0.6321414
18 MYCN CYT 0.93320742 0.029185253 66 0.9905318
19 NUP210L CYT 0.53146580 0.115532561 66 0.9342232
20 PLCG2 CYT 0.58438080 -0.194001758 66 0.9342232
21 POLE CYT 0.27793771 -0.183311630 66 0.8338131
22 SELP CYT 0.14233519 0.218947279 66 0.6321414
23 SERPINB13 CYT 0.02600806 -0.326131817 66 0.2860886
24 SLC17A8 CYT 0.98952671 -0.002216283 66 0.9905318
25 SMAP1 CYT 0.25854215 0.240153334 66 0.8338131
26 SOS1 CYT 0.71951600 -0.066630679 66 0.9342232
27 TET2 CYT 0.99053177 -0.001940611 66 0.9905318
28 TP53 CYT 0.03910878 0.481741461 66 0.3226475
29 TSC1 CYT 0.85245667 -0.033847667 66 0.9700369
30 USO1 CYT 0.76436440 0.054579164 66 0.9342232
31 XIRP2 CYT 0.31089339 -0.343493160 66 0.8549568
32 ZFHX3 CYT 0.08451637 0.250288735 66 0.5578080
33 ZNF180 CYT 0.68610433 0.033092946 66 0.9342232print(snv_ecd8_patient_rank) gene immune_variable p.value coef n p.adj
1 ANK3 E_CD8_density 0.266056723 0.196115914 66 0.89830494
2 APC E_CD8_density 0.744104867 -0.026429982 66 0.94812867
3 ARHGAP35 E_CD8_density 0.434679773 -0.095919150 66 0.89830494
4 AXIN2 E_CD8_density 0.789597469 -0.017051081 66 0.94812867
5 BRCA2 E_CD8_density 0.544427234 0.131510389 66 0.89830494
6 CCDC6 E_CD8_density 0.460431775 -0.092882173 66 0.89830494
7 CD300LG E_CD8_density 0.435632825 -0.168878952 66 0.89830494
8 COL5A1 E_CD8_density 0.789597469 -0.017051081 66 0.94812867
9 CYP7B1 E_CD8_density 0.627111238 -0.065550545 66 0.94066686
10 DNAH12 E_CD8_density 0.495899491 0.120206533 66 0.89830494
11 ERCC2 E_CD8_density 0.434679773 -0.095919150 66 0.89830494
12 FAT1 E_CD8_density 0.544427234 0.131510389 66 0.89830494
13 FLG E_CD8_density 0.678594174 -0.030547819 66 0.94812867
14 GZMA E_CD8_density 0.544427234 0.131510389 66 0.89830494
15 IRF4 E_CD8_density 0.889493148 -0.018812144 66 0.96445935
16 ITPKB E_CD8_density 0.306757003 0.137202574 66 0.89830494
17 MPO E_CD8_density 0.896988369 0.020365897 66 0.96445935
18 MYCN E_CD8_density 0.966752914 -0.009142713 66 0.96675291
19 NUP210L E_CD8_density 0.001222884 -0.481123354 66 0.04035516
20 PLCG2 E_CD8_density 0.504415205 -0.153067844 66 0.89830494
21 POLE E_CD8_density 0.698144426 -0.052599377 66 0.94812867
22 SELP E_CD8_density 0.372913527 -0.111658913 66 0.89830494
23 SERPINB13 E_CD8_density 0.434679773 -0.095919150 66 0.89830494
24 SLC17A8 E_CD8_density 0.247814402 -0.161164987 66 0.89830494
25 SMAP1 E_CD8_density 0.266056723 0.196115914 66 0.89830494
26 SOS1 E_CD8_density 0.338479026 0.150087372 66 0.89830494
27 TET2 E_CD8_density 0.306757003 0.137202574 66 0.89830494
28 TP53 E_CD8_density 0.582883694 0.087776273 66 0.91596009
29 TSC1 E_CD8_density 0.906007269 -0.018264872 66 0.96445935
30 USO1 E_CD8_density 0.936884538 -0.012250237 66 0.96616218
31 XIRP2 E_CD8_density 0.195208549 -0.272017121 66 0.89830494
32 ZFHX3 E_CD8_density 0.022448147 0.272732111 66 0.37039442
33 ZNF180 E_CD8_density 0.804472808 -0.016967522 66 0.94812867Using the same regression approach as Rooney, none of the SNVs are significantly correlated with the status of any of these point mutations. Note that TP53, which was reported to be significant by them, has a significant uncorrected p-value, but not after BH adjustment.
Linear modeling for copy number
cnvamp_cyt_patient_rank <- linear_model_til_variant(titan_merged_rooney, titan_merged,
ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT",
variant_type = "cnv", cn_type = "amp", transition_events)
cnvdel_cyt_patient_rank <- linear_model_til_variant(titan_merged_rooney, titan_merged,
ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT",
variant_type = "cnv", cn_type = "del", transition_events)print(cnvamp_cyt_patient_rank) gene immune_variable p.value coef n p.adj
1 ACO1 CYT 0.315367454 0.1723804964 66 0.7992756
2 ACVR1B CYT 0.833045016 0.0416486987 66 0.9845359
3 ACVR2B CYT 0.861388671 0.0422001361 66 0.9860212
4 ADNP CYT 0.132647977 -0.2069951704 66 0.7464542
5 AGBL5 CYT 0.435932526 -0.1138656481 66 0.8513337
6 AJUBA CYT 0.837671410 0.0248187346 66 0.9845359
7 AKT1 CYT 0.514234035 0.1651238030 66 0.8578466
8 ALB CYT 0.443381875 -0.2059351969 66 0.8513337
9 ALK CYT 0.333920187 -0.1425551069 66 0.8162483
10 ALKBH6 CYT 0.018612720 -0.1557109991 66 0.6593230
11 ALPK2 CYT 0.680953901 -0.1769513651 66 0.9564089
12 ANK3 CYT 0.479815169 0.1873756991 66 0.8513337
13 APC CYT 0.227994261 -0.7497631243 66 0.7533660
14 APOL2 CYT 0.754200268 0.0822014380 66 0.9619797
15 ARHGAP35 CYT 0.900015968 0.0446417027 66 0.9876376
16 ARID1A CYT 0.549521193 -0.5510204774 66 0.8754804
17 ARID2 CYT 0.842535991 0.0219635875 66 0.9860212
18 ARID5B CYT 0.508156007 0.1788050827 66 0.8578466
19 ARL15 CYT 0.737187842 0.0830858572 66 0.9619797
20 ARL2 CYT 0.522030277 -0.1479350941 66 0.8622150
21 ARL6IP1 CYT 0.279745416 -0.3446144002 66 0.7730370
22 ARMCX1 CYT 0.110442766 -0.4253497346 66 0.6908155
23 ASXL1 CYT 0.017224099 -0.2980920118 66 0.6593230
24 ASXL2 CYT 0.385616201 -0.1248442823 66 0.8374803
25 ATM CYT 0.461277193 -0.1062600302 66 0.8513337
26 ATP1B4 CYT 0.726840499 -0.0597037678 66 0.9619797
27 ATP5B CYT 0.644754543 -0.0937366426 66 0.9361273
28 ATRX CYT 0.987715180 -0.0070191765 66 0.9991821
29 AXIN2 CYT 0.513097333 0.1778664862 66 0.8578466
30 AZGP1 CYT 0.411635666 0.1498416484 66 0.8513337
31 B2M CYT 0.897337891 -0.0377209607 66 0.9876376
32 BAP1 CYT 0.945269943 -0.0329427076 66 0.9925265
33 BCLAF1 CYT 0.474650167 0.4806771755 66 0.8513337
34 BCOR CYT 0.622922794 0.3271541988 66 0.9318101
35 BHMT2 CYT 0.076953362 -0.7742051168 66 0.6593230
36 BLOC1S3 CYT 0.616798891 -0.0790562176 66 0.9298897
37 BRAF CYT 0.789809189 -0.0385218073 66 0.9779504
38 BRCA1 CYT 0.725293239 0.0995158193 66 0.9619797
39 BRCA2 CYT 0.110139988 -0.5228948835 66 0.6908155
40 BRE CYT 0.328105013 -0.1388715023 66 0.8162483
41 C12orf39 CYT 0.134031831 -0.2283200510 66 0.7464542
42 C12orf57 CYT 0.185308589 -0.1455091930 66 0.7533660
43 C19orf33 CYT 0.232155580 -0.1060974416 66 0.7533660
44 C1QL2 CYT 0.223588882 -0.3963219058 66 0.7533660
45 C3orf62 CYT 0.687074157 0.2136193353 66 0.9566186
46 C3orf70 CYT 0.141358333 -0.1218289813 66 0.7533660
47 CAP2 CYT 0.246129846 -0.1681102595 66 0.7533660
48 CARD11 CYT 0.474461585 0.2005207642 66 0.8513337
49 CASP8 CYT 0.068246835 -0.3891903904 66 0.6593230
50 CBFB CYT 0.734287546 0.1385907160 66 0.9619797
51 CC2D1B CYT 0.846809951 -0.0578846258 66 0.9860212
52 CCDC101 CYT 0.642691791 -0.1138744444 66 0.9361273
53 CCDC120 CYT 0.307759417 0.3602042522 66 0.7992756
54 CCDC36 CYT 0.496641474 -0.1662654774 66 0.8561153
55 CCDC6 CYT 0.466593632 0.2115260067 66 0.8513337
56 CCND1 CYT 0.684281494 -0.0521356392 66 0.9564089
57 CD1D CYT 0.173004675 -0.2220401850 66 0.7533660
58 CD300LG CYT 0.568856694 0.1606613345 66 0.8876126
59 CD38 CYT 0.073700690 -0.6434904843 66 0.6593230
60 CD5L CYT 0.173004675 -0.2220401850 66 0.7533660
61 CD70 CYT 0.239311380 0.4834226665 66 0.7533660
62 CD79B CYT 0.371800811 0.2343211794 66 0.8374803
63 CDC27 CYT 0.492610988 0.1549272320 66 0.8561153
64 CDH1 CYT 0.734287546 0.1385907160 66 0.9619797
65 CDK12 CYT 0.376122235 -0.2921784942 66 0.8374803
66 CDK4 CYT 0.809312665 0.0443568374 66 0.9784506
67 CDKN1A CYT 0.100886756 -0.2063373144 66 0.6908155
68 CDKN1B CYT 0.043449806 -0.3172314490 66 0.6593230
69 CDKN2A CYT 0.806908894 0.0559188504 66 0.9784506
70 CDX1 CYT 0.346942963 0.3039797041 66 0.8257971
71 CEBPA CYT 0.024853263 -0.1397488000 66 0.6593230
72 CEP76 CYT 0.922871064 0.0142384263 66 0.9876376
73 CHD4 CYT 0.110683153 -0.1850221152 66 0.6908155
74 CHD8 CYT 0.754702291 0.0383482559 66 0.9619797
75 CIC CYT 0.398831752 -0.1575221640 66 0.8489077
76 CLEC4E CYT 0.031851213 -0.2469836624 66 0.6593230
77 CNBD1 CYT 0.619070187 -0.0485600985 66 0.9298897
78 CNKSR1 CYT 0.665527404 -0.3683005391 66 0.9484739
79 COL5A1 CYT 0.791545455 0.0586726771 66 0.9779504
80 COL5A3 CYT 0.050536432 -0.1276926786 66 0.6593230
81 CPS1 CYT 0.058687371 -0.4110879170 66 0.6593230
82 CREBBP CYT 0.216789479 -0.2997710468 66 0.7533660
83 CSAG1 CYT 0.175870249 0.2001330420 66 0.7533660
84 CSNK2A1 CYT 0.712187276 -0.0400524844 66 0.9619797
85 CTCF CYT 0.734287546 0.1385907160 66 0.9619797
86 CTNNB1 CYT 0.749620465 0.1477302608 66 0.9619797
87 CUL4B CYT 0.562833313 -0.0958351414 66 0.8820158
88 CUX1 CYT 0.593622307 -0.0910542026 66 0.9128389
89 CXCL9 CYT 0.385215708 -0.2212720899 66 0.8374803
90 CYP7B1 CYT 0.224414154 -0.1199988233 66 0.7533660
91 DDX3X CYT 0.973732767 -0.0186938282 66 0.9991821
92 DDX5 CYT 0.468159448 0.1992093526 66 0.8513337
93 DHDH CYT 0.963068030 0.0267443861 66 0.9989416
94 DIAPH1 CYT 0.639376441 0.1249103226 66 0.9361273
95 DIS3 CYT 0.057057344 -0.4131613342 66 0.6593230
96 DNAH12 CYT 0.874276313 0.0778984389 66 0.9876376
97 DNER CYT 0.279438659 -0.2638682315 66 0.7730370
98 DNMT3A CYT 0.385616201 -0.1248442823 66 0.8374803
99 DPRX CYT 0.858388929 0.0962125328 66 0.9860212
100 DUSP19 CYT 0.134030025 -0.3653515521 66 0.7464542
101 EAF1 CYT 0.642009731 -0.1464042245 66 0.9361273
102 EEF1A1 CYT 0.233680747 0.3677223461 66 0.7533660
103 EGFR CYT 0.871414197 -0.0353306073 66 0.9876376
104 EIF1AX CYT 0.472920530 -0.2437729136 66 0.8513337
105 EIF2S2 CYT 0.046656444 -0.2274077888 66 0.6593230
106 ELF3 CYT 0.042272733 -0.4162445628 66 0.6593230
107 ELTD1 CYT 0.269636614 -0.4324889675 66 0.7625660
108 EP300 CYT 0.853498427 -0.0906221673 66 0.9860212
109 EPB41L3 CYT 0.940891992 -0.0086784747 66 0.9925265
110 EPHA2 CYT 0.058483524 -0.5775123093 66 0.6593230
111 EPS8 CYT 0.090299692 -0.2429152295 66 0.6637271
112 ERBB2 CYT 0.919036242 0.0054033647 66 0.9876376
113 ERBB3 CYT 0.648594170 -0.0922996636 66 0.9361273
114 ERCC2 CYT 0.958277914 0.0055461301 66 0.9968293
115 ETHE1 CYT 0.247653459 -0.0921611059 66 0.7533660
116 EZH1 CYT 0.648204837 -0.1840373701 66 0.9361273
117 EZH2 CYT 0.652971908 -0.0504517759 66 0.9361273
118 EZR CYT 0.892634925 0.0811862739 66 0.9876376
119 FAM166A CYT 0.481221944 0.1621547634 66 0.8513337
120 FAM46C CYT 0.229343637 -0.2039885046 66 0.7533660
121 FAM8A1 CYT 0.195230276 -0.2084311912 66 0.7533660
122 FAT1 CYT 0.773554346 -0.0580370733 66 0.9723148
123 FBXW7 CYT 0.998733556 -0.0004373475 66 0.9991821
124 FGFBP1 CYT 0.073700690 -0.6434904843 66 0.6593230
125 FGFR2 CYT 0.951904567 0.0104687502 66 0.9930532
126 FGFR3 CYT 0.707032928 0.0808309954 66 0.9619797
127 FKBP2 CYT 0.226680162 -0.2689862199 66 0.7533660
128 FLG CYT 0.183849342 -0.2328752102 66 0.7533660
129 FLT3 CYT 0.164322667 -0.4125817910 66 0.7533660
130 FOXA1 CYT 0.579502337 0.1400495124 66 0.8964951
131 FOXQ1 CYT 0.969284618 -0.0065043108 66 0.9991821
132 FRG1 CYT 0.999182075 -0.0002149795 66 0.9991821
133 FRMD7 CYT 0.349981557 0.2383909443 66 0.8257971
134 FUBP1 CYT 0.249824364 -0.4919920056 66 0.7536368
135 GATA3 CYT 0.604191711 0.0746758422 66 0.9151356
136 GFER CYT 0.056366267 -0.4215757897 66 0.6593230
137 GNA13 CYT 0.513097333 0.1778664862 66 0.8578466
138 GNB1 CYT 0.225295892 -0.3286785069 66 0.7533660
139 GNPTAB CYT 0.682221523 0.0857010694 66 0.9564089
140 GOT1 CYT 0.112708729 -0.4246878244 66 0.6915349
141 GPATCH4 CYT 0.073709488 -0.2781768669 66 0.6593230
142 GPS2 CYT 0.206009670 -1.0633139137 66 0.7533660
143 GUSB CYT 0.990338599 -0.0020316006 66 0.9991821
144 GZMA CYT 0.318151886 0.3551357167 66 0.7997985
145 HIF1A CYT 0.472640555 -0.1393402728 66 0.8513337
146 HIST1H1E CYT 0.083781381 -0.3052292666 66 0.6593230
147 HIST1H2BN CYT 0.522534818 -0.0922833859 66 0.8622150
148 HIST1H3B CYT 0.087819057 -0.2984969273 66 0.6623021
149 HIST1H3C CYT 0.087819057 -0.2984969273 66 0.6623021
150 HIST1H4E CYT 0.083781381 -0.3052292666 66 0.6593230
151 HLA-A CYT 0.264870332 -0.1422419492 66 0.7625660
152 HLA-B CYT 0.380567003 -0.1121878301 66 0.8374803
153 HLA-C CYT 0.315735952 -0.1146997963 66 0.7992756
154 HRAS CYT 0.553825993 -0.2535428624 66 0.8754804
155 HSP90AB1 CYT 0.194160353 -0.1668747817 66 0.7533660
156 HSPA6 CYT 0.140544018 -0.2661824246 66 0.7533660
157 IDH1 CYT 0.048214043 -0.4313548973 66 0.6593230
158 IDH2 CYT 0.877332034 -0.0244844212 66 0.9876376
159 IFITM1 CYT 0.553825993 -0.2535428624 66 0.8754804
160 IL32 CYT 0.203131110 -0.3069877789 66 0.7533660
161 IL7R CYT 0.198710061 -0.1829922391 66 0.7533660
162 ING1 CYT 0.081618445 -0.4996043210 66 0.6593230
163 INPPL1 CYT 0.723830257 -0.0483261159 66 0.9619797
164 INTS12 CYT 0.150452771 -0.4394991629 66 0.7533660
165 IPO7 CYT 0.750748227 -0.1419373442 66 0.9619797
166 IRF4 CYT 0.995036656 0.0010964654 66 0.9991821
167 IRF6 CYT 0.212771132 -0.2255456125 66 0.7533660
168 ITGB7 CYT 0.780041721 -0.0500358643 66 0.9770765
169 ITPKB CYT 0.175587672 -0.2905481789 66 0.7533660
170 JTB CYT 0.385168369 -0.1123527899 66 0.8374803
171 KDM5C CYT 0.301121276 0.3643076292 66 0.7992756
172 KDM6A CYT 0.923288307 0.0288622701 66 0.9876376
173 KEAP1 CYT 0.074273279 -0.1225619034 66 0.6593230
174 KEL CYT 0.925414961 0.0103733369 66 0.9876376
175 KIT CYT 0.430760232 -0.1517548753 66 0.8513337
176 KLHL8 CYT 0.026124008 -0.7034259102 66 0.6593230
177 KRAS CYT 0.082382719 -0.2306246454 66 0.6593230
178 LARP4B CYT 0.469624607 0.0897795971 66 0.8513337
179 LASP1 CYT 0.388664874 -0.3535084941 66 0.8374803
180 LCE4A CYT 0.351305958 -0.1505813635 66 0.8257971
181 LCTL CYT 0.862335269 0.0495881126 66 0.9860212
182 LMAN1 CYT 0.257522500 -0.2697165736 66 0.7625660
183 LPAR2 CYT 0.534110846 0.1055656546 66 0.8631613
184 LZTR1 CYT 0.820069198 -0.0599526080 66 0.9797526
185 MAP2K1 CYT 0.862335269 0.0495881126 66 0.9860212
186 MAP2K4 CYT 0.292799645 -0.8792766924 66 0.7969434
187 MAP3K1 CYT 0.494930984 0.3526408005 66 0.8561153
188 MAP4K3 CYT 0.171084913 -0.2084046494 66 0.7533660
189 MBD1 CYT 0.414747288 -0.1260875219 66 0.8513337
190 MBD6 CYT 0.903658403 -0.0215662690 66 0.9876376
191 MED12 CYT 0.979176434 0.0088592233 66 0.9991821
192 MED23 CYT 0.950135535 -0.0292663381 66 0.9930532
193 MET CYT 0.811762221 0.0317553107 66 0.9784506
194 MGA CYT 0.809174128 0.0738892070 66 0.9784506
195 MICALCL CYT 0.668123897 0.0952915788 66 0.9484739
196 MLF2 CYT 0.110683153 -0.1850221152 66 0.6908155
197 MMD CYT 0.331782147 0.2065144681 66 0.8162483
198 MORC4 CYT 0.106496049 -0.3959057504 66 0.6908155
199 MPO CYT 0.473242324 0.1941813564 66 0.8513337
200 MRPS25 CYT 0.706115969 -0.0946591079 66 0.9619797
201 MTOR CYT 0.401003361 -0.2241767841 66 0.8489077
202 MUC17 CYT 0.495222497 0.1264128718 66 0.8561153
203 MUC4 CYT 0.188906296 -0.1055207969 66 0.7533660
204 MUC7 CYT 0.411321777 -0.2249297811 66 0.8513337
205 MXRA5 CYT 0.489925262 -0.1805940940 66 0.8561153
206 MYB CYT 0.642645096 0.3604702113 66 0.9361273
207 MYCN CYT 0.245575722 -0.1624608236 66 0.7533660
208 MYD88 CYT 0.807822570 0.0590115254 66 0.9784506
209 MYOCD CYT 0.292799645 -0.8792766924 66 0.7969434
210 NBPF1 CYT 0.054834818 -0.3507883275 66 0.6593230
211 NCOR1 CYT 0.863449479 -0.0116941731 66 0.9860212
212 NF1 CYT 0.527011167 -0.7704612637 66 0.8622150
213 NFE2L2 CYT 0.595110454 -0.1196570946 66 0.9128389
214 NKIRAS2 CYT 0.384297250 -0.3643869929 66 0.8374803
215 NOTCH1 CYT 0.893889348 -0.0386783847 66 0.9876376
216 NPM1 CYT 0.235057872 -0.2417853804 66 0.7533660
217 NRAS CYT 0.177475826 -0.2372414828 66 0.7533660
218 NSD1 CYT 0.298183122 -0.1755221062 66 0.7992756
219 NTN4 CYT 0.764565839 0.0563018401 66 0.9677372
220 NUP210L CYT 0.385168369 -0.1123527899 66 0.8374803
221 ODAM CYT 0.444047790 -0.2130731667 66 0.8513337
222 OMA1 CYT 0.817641027 -0.0643732455 66 0.9797526
223 OR4A16 CYT 0.128663191 -0.4112023780 66 0.7464542
224 OR52N1 CYT 0.265920413 -0.7285233120 66 0.7625660
225 OTUD7A CYT 0.101574961 -0.3388254422 66 0.6908155
226 OVOL1 CYT 0.202866892 -0.2805071065 66 0.7533660
227 PAPD5 CYT 0.539943858 0.1687550291 66 0.8674929
228 PBRM1 CYT 0.921554464 -0.0471732030 66 0.9876376
229 PCBP1 CYT 0.158285893 -0.2071749896 66 0.7533660
230 PDAP1 CYT 0.477853001 0.1298889051 66 0.8513337
231 PDCD2L CYT 0.058026646 -0.1138646436 66 0.6593230
232 PDSS2 CYT 0.024685841 -0.1015804124 66 0.6593230
233 PHF6 CYT 0.417295630 0.1933891739 66 0.8513337
234 PIGB CYT 0.767952833 -0.1282173380 66 0.9686374
235 PIGZ CYT 0.315181178 -0.0851596608 66 0.7992756
236 PIK3CA CYT 0.047508708 -0.2154473159 66 0.6593230
237 PIK3R1 CYT 0.054314783 -1.7048118641 66 0.6593230
238 PLCG2 CYT 0.452656615 0.2453796504 66 0.8513337
239 POLE CYT 0.160211662 -0.2865203078 66 0.7533660
240 POU2AF1 CYT 0.630248029 -0.0732557621 66 0.9361273
241 POU2F2 CYT 0.145523244 -0.1761379472 66 0.7533660
242 PPM1D CYT 0.703771868 0.0789689921 66 0.9619797
243 PPM1F CYT 0.463713445 -0.1876684185 66 0.8513337
244 PPP2R1A CYT 0.787968953 0.1245312314 66 0.9779504
245 PPP2R4 CYT 0.908802152 0.0330144002 66 0.9876376
246 PPP6C CYT 0.736578917 0.0642582210 66 0.9619797
247 PRDM1 CYT 0.023416847 -0.1593541918 66 0.6593230
248 PRKRIR CYT 0.809739705 0.0268707025 66 0.9784506
249 PTEN CYT 0.671718437 0.1159856635 66 0.9498519
250 PTH2 CYT 0.899688880 0.0724328853 66 0.9876376
251 PTPN11 CYT 0.382443575 0.1484076116 66 0.8374803
252 PTPN12 CYT 0.007468472 0.5904820357 66 0.6593230
253 QKI CYT 0.921628308 0.0640657300 66 0.9876376
254 RAB40A CYT 0.075885537 -0.4302628848 66 0.6593230
255 RAC1 CYT 0.373192457 0.2496248922 66 0.8374803
256 RAD21 CYT 0.246809953 -0.0988655623 66 0.7533660
257 RAD51C CYT 0.743651798 0.0821303365 66 0.9619797
258 RASA1 CYT 0.068139100 -0.7872919474 66 0.6593230
259 RB1 CYT 0.148431461 -0.2971574018 66 0.7533660
260 RBM10 CYT 0.344419948 0.3313778207 66 0.8257971
261 RGS2 CYT 0.466915660 -0.1392261530 66 0.8513337
262 RHEB CYT 0.723182280 -0.0411681734 66 0.9619797
263 RHOA CYT 0.933305082 0.0411186309 66 0.9907813
264 RIMS2 CYT 0.455855222 -0.0675550362 66 0.8513337
265 RIT1 CYT 0.101015946 -0.2445901454 66 0.6908155
266 RNF43 CYT 0.473242324 0.1941813564 66 0.8513337
267 RPL19 CYT 0.388664874 -0.3535084941 66 0.8374803
268 RPL22 CYT 0.178118180 -0.3227276354 66 0.7533660
269 RPL5 CYT 0.003837472 -0.4876529656 66 0.6593230
270 RPS15 CYT NA NA 66 NA
271 RPS2 CYT 0.056366267 -0.4215757897 66 0.6593230
272 RPSA CYT 0.942261090 -0.0171224243 66 0.9925265
273 RPTN CYT 0.188501992 -0.2311848019 66 0.7533660
274 RSBN1L CYT 0.007468472 0.5904820357 66 0.6593230
275 RUNX1 CYT 0.993928752 0.0025354820 66 0.9991821
276 RXRA CYT 0.923984352 0.0212185744 66 0.9876376
277 RYBP CYT 0.927615459 -0.0295496156 66 0.9876376
278 SACS CYT 0.020632071 -0.7511121748 66 0.6593230
279 SELP CYT 0.347429490 -0.1903315928 66 0.8257971
280 SEPT12 CYT 0.245741149 -0.2932769048 66 0.7533660
281 SERPINB13 CYT 0.482108891 -0.0776268720 66 0.8513337
282 SETD2 CYT 0.690117509 -0.1426081335 66 0.9571745
283 SETDB1 CYT 0.335969597 -0.1511392876 66 0.8162483
284 SF3B1 CYT 0.465245720 -0.1836093240 66 0.8513337
285 SGK1 CYT 0.945916152 -0.0314912740 66 0.9925265
286 SIRT4 CYT 0.178421188 -0.3083077872 66 0.7533660
287 SLC17A8 CYT 0.813573601 0.0454171429 66 0.9784506
288 SLC1A3 CYT 0.254439990 -0.1460932300 66 0.7612172
289 SLC22A14 CYT 0.861388671 0.0422001361 66 0.9860212
290 SLC25A15 CYT 0.031560284 -0.4936147175 66 0.6593230
291 SLC25A5 CYT 0.428953714 -0.1162819009 66 0.8513337
292 SLC26A3 CYT 0.560270914 -0.0719577569 66 0.8818177
293 SLC44A3 CYT 0.003003064 -0.4932150254 66 0.6593230
294 SLC4A5 CYT 0.541583977 -0.1151750104 66 0.8674929
295 SMAD2 CYT 0.832805799 -0.0370574951 66 0.9845359
296 SMAD4 CYT 0.411682153 -0.2607811942 66 0.8513337
297 SMAP1 CYT 0.306148632 0.3097690456 66 0.7992756
298 SMARCA4 CYT 0.710174987 -0.0473075630 66 0.9619797
299 SMARCB1 CYT 0.718284007 -0.0903725956 66 0.9619797
300 SMC1A CYT 0.301121276 0.3643076292 66 0.7992756
301 SMC3 CYT 0.243339239 -0.3864000022 66 0.7533660
302 SOS1 CYT 0.268405698 -0.1660798422 66 0.7625660
303 SOX17 CYT 0.527257518 0.0728758493 66 0.8622150
304 SOX9 CYT 0.189276290 0.2974765645 66 0.7533660
305 SP8 CYT 0.788126329 0.0657980478 66 0.9779504
306 SPEN CYT 0.068577887 -0.6551697648 66 0.6593230
307 SPOP CYT 0.730093548 -0.0581370221 66 0.9619797
308 SSX5 CYT 0.307759417 0.3602042522 66 0.7992756
309 STAG2 CYT 0.741027903 -0.0558846483 66 0.9619797
310 STK11 CYT NA NA 66 NA
311 STK19 CYT 0.155763260 -0.2136903314 66 0.7533660
312 STX2 CYT 0.133026108 -0.3309870273 66 0.7464542
313 TAP1 CYT 0.189059934 -0.1865114470 66 0.7533660
314 TBC1D12 CYT 0.836801173 0.0468159806 66 0.9845359
315 TBL1XR1 CYT 0.020112038 -0.2358050560 66 0.6593230
316 TBX3 CYT 0.455020360 0.1345317725 66 0.8513337
317 TCAP CYT 0.905114877 0.0063268246 66 0.9876376
318 TCEB1 CYT 0.823164915 -0.0228021112 66 0.9802161
319 TCF7L2 CYT 0.893795483 -0.0237669717 66 0.9876376
320 TCP11L2 CYT 0.984620021 -0.0036642957 66 0.9991821
321 TDRD10 CYT 0.396614003 -0.1104446763 66 0.8489077
322 TET2 CYT 0.236477626 -0.3586615158 66 0.7533660
323 TGFBR2 CYT 0.760219440 0.0651950797 66 0.9656121
324 TIMM17A CYT 0.042272733 -0.4162445628 66 0.6593230
325 TM4SF1 CYT 0.654254744 -0.0621239474 66 0.9361273
326 TMCO2 CYT 0.499748621 0.2097417924 66 0.8573886
327 TMED10 CYT 0.333752336 -0.1878924512 66 0.8162483
328 TMEM30B CYT 0.079597749 -0.3205144467 66 0.6593230
329 TMEM88 CYT 0.188268716 -1.0176324491 66 0.7533660
330 TMEM92 CYT 0.600781791 -0.0975089589 66 0.9137942
331 TNF CYT 0.116889607 -0.2400320402 66 0.7052340
332 TNFRSF14 CYT 0.236866146 -0.3259742436 66 0.7533660
333 TP53 CYT 0.268564277 -0.8738113548 66 0.7625660
334 TP53BP1 CYT 0.507181187 -0.1210987740 66 0.8578466
335 TPX2 CYT 0.015795091 -0.3021709270 66 0.6593230
336 TRAF3 CYT 0.990612095 0.0028786151 66 0.9991821
337 TRAT1 CYT 0.798896140 -0.0484212285 66 0.9784506
338 TRIM23 CYT 0.054314783 -1.7048118641 66 0.6593230
339 TRIM6-TRIM34 CYT 0.265920413 -0.7285233120 66 0.7625660
340 TSC1 CYT 0.531143509 -0.1868079964 66 0.8622150
341 TTLL9 CYT 0.019860653 -0.2898873651 66 0.6593230
342 TXLNA CYT 0.898704932 0.0432618544 66 0.9876376
343 TXNDC8 CYT 0.530399957 -0.1522143625 66 0.8622150
344 U2AF1 CYT 0.238863276 -0.5271291600 66 0.7533660
345 UBC CYT 0.475134620 -0.1613879862 66 0.8513337
346 UBXN4 CYT 0.431782473 -0.2159273037 66 0.8513337
347 UPP1 CYT 0.200620932 0.2961809154 66 0.7533660
348 USO1 CYT 0.385215708 -0.2212720899 66 0.8374803
349 VHL CYT 0.654095168 0.1063201395 66 0.9361273
350 WASF3 CYT 0.091675011 -0.5756945777 66 0.6637271
351 WT1 CYT 0.878553116 0.0465933380 66 0.9876376
352 XCL1 CYT 0.314744488 -0.2177889764 66 0.7992756
353 XIRP2 CYT 0.966657568 -0.0106970417 66 0.9991821
354 XPO1 CYT 0.578505609 -0.0843602360 66 0.8964951
355 ZFHX3 CYT 0.887350892 0.0584048522 66 0.9876376
356 ZFP36L1 CYT 0.146089217 -0.2882439553 66 0.7533660
357 ZNF180 CYT 0.277080360 -0.1696273753 66 0.7730370
358 ZNF471 CYT 0.457366397 -0.1343817931 66 0.8513337
359 ZNF483 CYT 0.504966174 -0.1617692707 66 0.8578466
360 ZNF492 CYT 0.193591121 -0.1943602580 66 0.7533660
361 ZNF620 CYT 0.752446387 -0.0548929110 66 0.9619797
362 ZNF706 CYT 0.599108308 -0.0467750420 66 0.9137942
363 ZNF750 CYT 0.989875975 0.0021615254 66 0.9991821
364 ZRANB3 CYT 0.405906557 -0.2296248736 66 0.8513337print(cnvdel_cyt_patient_rank) gene immune_variable p.value coef n p.adj
1 ACO1 CYT 0.6959188811 0.0596576244 66 0.9155831
2 ACVR1B CYT NA NA 66 NA
3 ACVR2B CYT 0.1454485935 -0.1972880225 66 0.4623534
4 ADNP CYT 0.2440439799 0.6379883534 66 0.5727406
5 AGBL5 CYT 0.2522623072 -0.2484739685 66 0.5780215
6 AJUBA CYT 0.8256796810 0.0514137241 66 0.9380171
7 AKT1 CYT 0.4914213479 -0.1609610019 66 0.7927810
8 ALB CYT 0.6445945027 -0.0853058049 66 0.9008141
9 ALK CYT 0.1745052388 -0.3362698931 66 0.4843293
10 ALKBH6 CYT 0.2788384258 -0.1581697742 66 0.6035564
11 ALPK2 CYT 0.1342348582 -0.1195302153 66 0.4623534
12 ANK3 CYT 0.0134833687 -0.5988061852 66 0.1645371
13 APC CYT 0.7590745422 -0.0400395508 66 0.9155831
14 APOL2 CYT 0.4609132250 -0.1068632094 66 0.7695583
15 ARHGAP35 CYT 0.1526156409 -0.1287981629 66 0.4623534
16 ARID1A CYT 0.4886448819 0.0783276621 66 0.7927810
17 ARID2 CYT 0.0234501921 -1.4574698236 66 0.1645371
18 ARID5B CYT 0.0045214258 -0.5531770835 66 0.1645371
19 ARL15 CYT 0.1121408840 -0.2067422061 66 0.4406477
20 ARL2 CYT 0.1277321656 -0.7831140130 66 0.4623534
21 ARL6IP1 CYT 0.5933898888 -0.1314255614 66 0.8730935
22 ARMCX1 CYT 0.0300224530 -0.1807624218 66 0.1928365
23 ASXL1 CYT NA NA 66 NA
24 ASXL2 CYT 0.2435552637 -0.2537565941 66 0.5727406
25 ATM CYT 0.1886866909 -0.4809682127 66 0.4962311
26 ATP1B4 CYT 0.7370857272 -0.0360029087 66 0.9155831
27 ATP5B CYT 0.0234501921 -1.4574698236 66 0.1645371
28 ATRX CYT 0.0262219261 -0.1808058802 66 0.1763110
29 AXIN2 CYT 0.6641113866 -0.1291162985 66 0.9016797
30 AZGP1 CYT 0.1177083442 -1.0959387601 66 0.4478793
31 B2M CYT 0.0019768208 -0.4904547726 66 0.1173852
32 BAP1 CYT 0.0538343033 -0.2684849013 66 0.2644214
33 BCLAF1 CYT 0.0263938630 -0.2436620876 66 0.1763110
34 BCOR CYT 0.6164564652 -0.0443806777 66 0.8864464
35 BHMT2 CYT 0.1433553227 -0.1817361762 66 0.4623534
36 BLOC1S3 CYT 0.5726983387 -0.0738069190 66 0.8501389
37 BRAF CYT 0.6699026530 0.0900398230 66 0.9022076
38 BRCA1 CYT 0.3739022742 -0.1254247557 66 0.7197151
39 BRCA2 CYT 0.0185900672 -0.3268695622 66 0.1645371
40 BRE CYT 0.1745052388 -0.3362698931 66 0.4843293
41 C12orf39 CYT 0.4465475597 0.1556534283 66 0.7648558
42 C12orf57 CYT 0.4017694638 0.1774479032 66 0.7197151
43 C19orf33 CYT 0.1073849342 -0.2971827473 66 0.4373972
44 C1QL2 CYT 0.7087385265 0.3004301093 66 0.9155831
45 C3orf62 CYT 0.0153504357 -0.1212401301 66 0.1645371
46 CAP2 CYT 0.5450880718 0.1793164864 66 0.8368765
47 CARD11 CYT 0.4427752217 -0.1081763661 66 0.7648558
48 CASP8 CYT 0.7532185791 0.2505735829 66 0.9155831
49 CBFB CYT 0.3914267750 -0.0955423706 66 0.7197151
50 CC2D1B CYT 0.8956012321 0.0332069817 66 0.9615861
51 CCDC101 CYT 0.1725235953 -0.3865133369 66 0.4843293
52 CCDC120 CYT 0.0176896125 -0.2782387347 66 0.1645371
53 CCDC36 CYT 0.0153504357 -0.1212401301 66 0.1645371
54 CCDC6 CYT 0.0177969092 -0.7147708718 66 0.1645371
55 CCND1 CYT NA NA 66 NA
56 CD1D CYT NA NA 66 NA
57 CD300LG CYT 0.9225733917 0.0141593594 66 0.9659546
58 CD38 CYT 0.9133032009 0.0168912609 66 0.9653268
59 CD5L CYT NA NA 66 NA
60 CD70 CYT 0.1053138442 -0.1382358398 66 0.4342571
61 CD79B CYT 0.6641113866 -0.1291162985 66 0.9016797
62 CDC27 CYT 0.5027662345 -0.1147173030 66 0.7996377
63 CDH1 CYT 0.3914267750 -0.0955423706 66 0.7197151
64 CDK12 CYT 0.3645908959 -0.1233407222 66 0.7079846
65 CDK4 CYT 0.7863266757 0.1838175031 66 0.9247645
66 CDKN1A CYT 0.3975640272 -0.3399699092 66 0.7197151
67 CDKN1B CYT 0.4465475597 0.1556534283 66 0.7648558
68 CDKN2A CYT 0.7816643798 0.0138492489 66 0.9247645
69 CDX1 CYT 0.0054010197 -0.6449545399 66 0.1645371
70 CEBPA CYT 0.6115357158 -0.0909788521 66 0.8864464
71 CEP76 CYT 0.9764995590 -0.0113769647 66 0.9883359
72 CHD4 CYT 0.4017694638 0.1774479032 66 0.7197151
73 CHD8 CYT 0.8614651788 -0.0375097942 66 0.9471238
74 CIC CYT 0.5611224266 -0.0857694335 66 0.8404255
75 CLEC4E CYT 0.9873575507 -0.0048920742 66 0.9933055
76 CNBD1 CYT 0.0063098236 0.4346036520 66 0.1645371
77 CNKSR1 CYT 0.5932706842 -0.0799667528 66 0.8730935
78 COL5A1 CYT 0.3626283977 -0.0990493142 66 0.7079846
79 COL5A3 CYT 0.7530358545 0.0528559382 66 0.9155831
80 CPS1 CYT 0.0829751644 -0.4318077927 66 0.3745095
81 CREBBP CYT 0.8310863844 -0.0389231700 66 0.9409588
82 CSAG1 CYT 0.4575499261 -0.0676145756 66 0.7679481
83 CSNK2A1 CYT 0.5530633320 -0.1664015053 66 0.8368765
84 CTCF CYT 0.3914267750 -0.0955423706 66 0.7197151
85 CTNNB1 CYT 0.1446213643 -0.1980497415 66 0.4623534
86 CUL4B CYT 0.7370857272 -0.0360029087 66 0.9155831
87 CUX1 CYT 0.9970242772 0.0011678622 66 0.9970243
88 CXCL9 CYT 0.6857671167 -0.0727370874 66 0.9155831
89 CYP7B1 CYT 0.5537416389 0.3796111409 66 0.8368765
90 DDX3X CYT 0.8102005544 -0.0206924171 66 0.9306562
91 DDX5 CYT 0.6641113866 -0.1291162985 66 0.9016797
92 DHDH CYT 0.1499771211 -0.1291591934 66 0.4623534
93 DIAPH1 CYT 0.1612348081 -0.2502170777 66 0.4768173
94 DIS3 CYT 0.0603692023 -0.2593224386 66 0.2880473
95 DNAH12 CYT 0.1645910582 -0.2099418841 66 0.4822229
96 DNER CYT 0.8069069732 -0.0150261493 66 0.9306562
97 DNMT3A CYT 0.2660025877 -0.2402592787 66 0.5922991
98 DPRX CYT 0.4695179390 -0.0757210795 66 0.7695583
99 DUSP19 CYT 0.4659765102 0.1892658660 66 0.7695583
100 EAF1 CYT 0.0024601680 -0.6324156925 66 0.1173852
101 EEF1A1 CYT 0.6511268913 -0.1026013010 66 0.9016797
102 EGFR CYT 0.6670832010 -0.1154809136 66 0.9020477
103 EIF1AX CYT 0.4029542371 -0.0643190591 66 0.7197151
104 EIF2S2 CYT 0.7863266758 5.6983425958 66 0.9247645
105 ELF3 CYT 0.1480621334 -0.5680835180 66 0.4623534
106 ELTD1 CYT 0.0222513215 -0.5070475910 66 0.1645371
107 EP300 CYT 0.7757842160 -0.0339705488 66 0.9234906
108 EPB41L3 CYT 0.7769487027 -0.1440266432 66 0.9234906
109 EPHA2 CYT 0.2137844209 -0.1987981697 66 0.5328656
110 EPS8 CYT 0.4465475597 0.1556534283 66 0.7648558
111 ERBB2 CYT 0.7690752856 -0.0382707544 66 0.9206851
112 ERBB3 CYT 0.0234501921 -1.4574698236 66 0.1645371
113 ERCC2 CYT 0.4228586301 -0.1036554897 66 0.7433410
114 ETHE1 CYT 0.3036415358 -0.1336957114 66 0.6378382
115 EZH1 CYT 0.7300351296 0.0406419519 66 0.9155831
116 EZH2 CYT 0.8982481281 -0.0411443191 66 0.9615861
117 EZR CYT 0.0292228636 -0.2324319155 66 0.1913811
118 FAM166A CYT 0.3505128027 -0.1004054749 66 0.7010256
119 FAM46C CYT NA NA 66 NA
120 FAM8A1 CYT 0.5450880718 0.1793164864 66 0.8368765
121 FAT1 CYT 0.0057515380 -0.2674531953 66 0.1645371
122 FBXW7 CYT 0.0152421422 -0.3082982958 66 0.1645371
123 FGFBP1 CYT 0.9133032009 0.0168912609 66 0.9653268
124 FGFR2 CYT 0.0139286574 -0.5571592972 66 0.1645371
125 FGFR3 CYT 0.6400090377 0.0568105249 66 0.8994256
126 FKBP2 CYT 0.1277321656 -0.7831140130 66 0.4623534
127 FLG CYT NA NA 66 NA
128 FLT3 CYT 0.1023852290 -0.1179211851 66 0.4274583
129 FOXA1 CYT 0.8060897498 0.0561399745 66 0.9306562
130 FOXQ1 CYT 0.7289617579 0.1727462280 66 0.9155831
131 FRG1 CYT 0.0572374973 -0.2651081868 66 0.2770627
132 FRMD7 CYT 0.6944121618 -0.0414351890 66 0.9155831
133 FUBP1 CYT 0.0229119869 -0.5018165446 66 0.1645371
134 GATA3 CYT 0.0451944092 -0.3740597229 66 0.2396021
135 GFER CYT 0.6231261480 0.0912817210 66 0.8864464
136 GNA13 CYT 0.6641113866 -0.1291162985 66 0.9016797
137 GNB1 CYT 0.1827631233 -0.2121225960 66 0.4883431
138 GNPTAB CYT 0.0234501921 -1.4574698236 66 0.1645371
139 GOT1 CYT 0.0016079048 -0.5695084591 66 0.1173852
140 GPATCH4 CYT NA NA 66 NA
141 GPS2 CYT 0.1020526494 -0.2279628834 66 0.4274583
142 GUSB CYT 0.2064524541 -0.3996749696 66 0.5304240
143 GZMA CYT 0.1121408840 -0.2067422061 66 0.4406477
144 HIF1A CYT 0.9333244960 -0.0150375852 66 0.9741574
145 HIST1H1E CYT 0.7593309201 -0.0750955473 66 0.9155831
146 HIST1H2BN CYT 0.7593309201 -0.0750955473 66 0.9155831
147 HIST1H3B CYT 0.8837560237 -0.0337698770 66 0.9552573
148 HIST1H3C CYT 0.7593309201 -0.0750955473 66 0.9155831
149 HIST1H4E CYT 0.7593309201 -0.0750955473 66 0.9155831
150 HLA-A CYT 0.9730771345 0.0088113456 66 0.9883359
151 HLA-B CYT 0.7402282475 -0.0590710099 66 0.9155831
152 HLA-C CYT 0.7447668756 0.0585249297 66 0.9155831
153 HRAS CYT 0.4023571728 -0.1059277535 66 0.7197151
154 HSP90AB1 CYT 0.7171684568 0.1570738415 66 0.9155831
155 HSPA6 CYT NA NA 66 NA
156 IDH1 CYT 0.7532185791 0.2505735829 66 0.9155831
157 IDH2 CYT 0.2628578663 -0.2105840702 66 0.5892250
158 IFITM1 CYT 0.4023571728 -0.1059277535 66 0.7197151
159 IL32 CYT 0.8381209868 -0.0372219888 66 0.9457176
160 IL7R CYT 0.2595405181 -0.4356758237 66 0.5857198
161 ING1 CYT 0.0434034302 -0.2629600211 66 0.2338185
162 INPPL1 CYT 0.8731433361 -0.0568125194 66 0.9471238
163 INTS12 CYT 0.8700391371 0.0251787359 66 0.9471238
164 IPO7 CYT 0.1270796348 -0.2451955036 66 0.4623534
165 IRF4 CYT 0.9803003575 -0.0090924343 66 0.9891853
166 IRF6 CYT 0.1613184197 -0.7239824299 66 0.4768173
167 ITGB7 CYT NA NA 66 NA
168 ITPKB CYT 0.1521615029 -0.7272323626 66 0.4623534
169 JTB CYT NA NA 66 NA
170 KDM5C CYT 0.0378109452 -0.2455181865 66 0.2177389
171 KDM6A CYT 0.4188972404 -0.1073434685 66 0.7402734
172 KEAP1 CYT 0.7530358545 0.0528559382 66 0.9155831
173 KEL CYT 0.8147413771 -0.0818196404 66 0.9319302
174 KIT CYT 0.2526680781 -0.3521386928 66 0.5780215
175 KLHL8 CYT 0.8025295736 0.0322672647 66 0.9306562
176 KRAS CYT 0.3203004928 0.4945679523 66 0.6603726
177 LARP4B CYT 0.0474512758 -0.3702877447 66 0.2476363
178 LASP1 CYT 0.3982403094 -0.1163850739 66 0.7197151
179 LCE4A CYT NA NA 66 NA
180 LCTL CYT 0.0503846773 -0.3513223178 66 0.2549770
181 LMAN1 CYT 0.0119756963 -0.2240004271 66 0.1645371
182 LPAR2 CYT 0.9550507013 -0.0151688448 66 0.9883359
183 LZTR1 CYT 0.2452152673 -0.1734356212 66 0.5727406
184 MAP2K1 CYT 0.0503846773 -0.3513223178 66 0.2549770
185 MAP2K4 CYT 0.0154938938 -0.3081352391 66 0.1645371
186 MAP3K1 CYT 0.2261919989 -0.1437116527 66 0.5555009
187 MAP4K3 CYT 0.1853476126 -0.3444392709 66 0.4913183
188 MBD1 CYT 0.0114108841 -0.2293717101 66 0.1645371
189 MBD6 CYT NA NA 66 NA
190 MED12 CYT 0.0236460455 -0.1777921166 66 0.1645371
191 MED23 CYT 0.0407211946 -0.2222590402 66 0.2229652
192 MET CYT 0.8656788183 0.0492360642 66 0.9471238
193 MGA CYT 0.0737161838 -0.2986174929 66 0.3372768
194 MICALCL CYT 0.9965403636 -0.0007249317 66 0.9970243
195 MLF2 CYT 0.4017694638 0.1774479032 66 0.7197151
196 MMD CYT 0.2686289043 -0.1839831727 66 0.5941858
197 MORC4 CYT 0.7581204368 -0.0316598194 66 0.9155831
198 MPO CYT 0.9755611951 0.0067984529 66 0.9883359
199 MRPS25 CYT 0.0024601680 -0.6324156925 66 0.1173852
200 MTOR CYT 0.4020618089 -0.1344400038 66 0.7197151
201 MUC17 CYT 0.7418557903 -0.1658068294 66 0.9155831
202 MUC4 CYT 0.0145252709 -0.5641653968 66 0.1645371
203 MUC7 CYT 0.8596616859 -0.0366810501 66 0.9471238
204 MXRA5 CYT 0.2863237754 0.0931386720 66 0.6130265
205 MYB CYT 0.0213530192 -0.2504981711 66 0.1645371
206 MYCN CYT 0.2567938648 -0.2450321689 66 0.5834636
207 MYD88 CYT 0.1454485935 -0.1972880225 66 0.4623534
208 MYOCD CYT 0.0154328399 -0.3064214837 66 0.1645371
209 NBPF1 CYT 0.1356961700 -0.2466847471 66 0.4623534
210 NCOR1 CYT 0.2011181425 -0.1504855798 66 0.5225317
211 NF1 CYT 0.2237493547 -0.0847316298 66 0.5535725
212 NFE2L2 CYT 0.4643249005 0.1893158476 66 0.7695583
213 NKIRAS2 CYT 0.8549832717 -0.0228297780 66 0.9471238
214 NOTCH1 CYT 0.5269192849 -0.0680209826 66 0.8262490
215 NPM1 CYT 0.2924551831 0.1266797425 66 0.6187137
216 NRAS CYT 0.0184108387 -1.5653474002 66 0.1645371
217 NSD1 CYT 0.7923405484 0.0468964460 66 0.9285232
218 NTN4 CYT 0.0234501921 -1.4574698236 66 0.1645371
219 NUP210L CYT NA NA 66 NA
220 ODAM CYT 0.6409080508 0.0956258367 66 0.8994256
221 OMA1 CYT 0.9671958317 0.0127634729 66 0.9883359
222 OR4A16 CYT 0.1783607912 -0.6317851210 66 0.4843293
223 OR52N1 CYT 0.3495761886 -0.1412690677 66 0.7010256
224 OTUD7A CYT 0.0006714359 -0.5878706221 66 0.1173852
225 OVOL1 CYT 0.1277321656 -0.7831140130 66 0.4623534
226 PAPD5 CYT 0.8917165549 0.0145342308 66 0.9607527
227 PBRM1 CYT 0.0538343033 -0.2684849013 66 0.2644214
228 PCBP1 CYT NA NA 66 NA
229 PDAP1 CYT 0.1177083442 -1.0959387601 66 0.4478793
230 PDCD2L CYT 0.4572014531 -0.1373221732 66 0.7679481
231 PDSS2 CYT 0.6534238378 0.0473575320 66 0.9016797
232 PHF6 CYT 0.4532393789 -0.0728446604 66 0.7679481
233 PIGB CYT 0.1091762023 -0.2495161136 66 0.4393356
234 PIGZ CYT 0.0145252709 -0.5641653968 66 0.1645371
235 PIK3R1 CYT 0.1427527111 -0.1817633024 66 0.4623534
236 PLCG2 CYT 0.8733956204 0.0179988691 66 0.9471238
237 POLE CYT 0.5598356412 0.1269367667 66 0.8404255
238 POU2AF1 CYT 0.1802558848 -0.4912397502 66 0.4855279
239 POU2F2 CYT 0.5700826428 -0.0821178801 66 0.8500339
240 PPM1D CYT 0.5112196539 -0.1815044847 66 0.8092292
241 PPM1F CYT 0.2452152673 -0.1734356212 66 0.5727406
242 PPP2R1A CYT 0.2115945438 -0.1384371340 66 0.5328656
243 PPP2R4 CYT 0.6350780181 -0.0935157590 66 0.8987969
244 PPP6C CYT 0.5215831723 -0.1767879731 66 0.8217395
245 PRDM1 CYT 0.7123950331 -0.0561071391 66 0.9155831
246 PRKRIR CYT 0.9097034954 -0.1291723871 66 0.9653268
247 PTEN CYT 0.0014025486 -0.5751086925 66 0.1173852
248 PTH2 CYT 0.1714867962 -0.1654913462 66 0.4843293
249 PTPN11 CYT 0.0234501924 -1.4119238916 66 0.1645371
250 PTPN12 CYT 0.6236973504 -0.1706110847 66 0.8864464
251 QKI CYT 0.0351915732 -0.2181926783 66 0.2098926
252 RAB40A CYT 0.6139788585 -0.0427455540 66 0.8864464
253 RAC1 CYT 0.8530313084 0.0284350107 66 0.9471238
254 RAD51C CYT 0.3872434064 -0.1891007555 66 0.7197151
255 RASA1 CYT 0.2319029671 -0.1507565177 66 0.5653693
256 RB1 CYT 0.0339721482 -0.2187596400 66 0.2063036
257 RBM10 CYT 0.0180314647 -0.2780764438 66 0.1645371
258 RGS2 CYT 0.2401138586 -0.5325414955 66 0.5727406
259 RHEB CYT 0.9727251763 -0.0118782161 66 0.9883359
260 RHOA CYT 0.3810562091 -0.1340246574 66 0.7197151
261 RIMS2 CYT 0.5474010318 1.2613520190 66 0.8368765
262 RIT1 CYT NA NA 66 NA
263 RNF43 CYT 0.9755611951 0.0067984529 66 0.9883359
264 RPL19 CYT 0.3645908959 -0.1233407222 66 0.7079846
265 RPL22 CYT 0.2137844209 -0.1987981697 66 0.5328656
266 RPL5 CYT 0.1367965756 0.2088042453 66 0.4623534
267 RPS15 CYT 0.2800935211 -0.0953693531 66 0.6035564
268 RPS2 CYT 0.6231261480 0.0912817210 66 0.8864464
269 RPSA CYT 0.1536563721 -0.1899454511 66 0.4623534
270 RPTN CYT NA NA 66 NA
271 RSBN1L CYT 0.6236973504 -0.1706110847 66 0.8864464
272 RUNX1 CYT 0.4985322000 -0.0965986634 66 0.7966974
273 RXRA CYT 0.3636113103 -0.0983793039 66 0.7079846
274 RYBP CYT 0.2018161317 -0.1971802950 66 0.5225317
275 SACS CYT 0.0666687799 -0.2285883635 66 0.3136250
276 SELP CYT NA NA 66 NA
277 SEPT12 CYT 0.8547357187 -0.0333660753 66 0.9471238
278 SERPINB13 CYT 0.3552482892 -0.1470112788 66 0.7062674
279 SETD2 CYT 0.3322893429 -0.1437690531 66 0.6808874
280 SETDB1 CYT 0.4700295979 -1.1181771517 66 0.7695583
281 SF3B1 CYT 0.7532185791 0.2505735829 66 0.9155831
282 SGK1 CYT 0.0407211946 -0.2222590402 66 0.2229652
283 SIRT4 CYT 0.4339226613 -0.3196291637 66 0.7587967
284 SLC17A8 CYT 0.0234501921 -1.4574698236 66 0.1645371
285 SLC1A3 CYT 0.2926849401 -0.3646459702 66 0.6187137
286 SLC22A14 CYT 0.1454485935 -0.1972880225 66 0.4623534
287 SLC25A15 CYT 0.0386418770 -0.2275599837 66 0.2187523
288 SLC25A5 CYT 0.7370857272 -0.0360029087 66 0.9155831
289 SLC26A3 CYT 0.0336632318 -2.9042687421 66 0.2063036
290 SLC44A3 CYT 0.1304335242 0.2134593351 66 0.4623534
291 SLC4A5 CYT NA NA 66 NA
292 SMAD2 CYT 0.0112164693 -0.2292000909 66 0.1645371
293 SMAD4 CYT 0.1433857889 -0.1328422338 66 0.4623534
294 SMAP1 CYT 0.1338077897 -0.3294801167 66 0.4623534
295 SMARCA4 CYT 0.7950827176 0.0433894555 66 0.9285232
296 SMARCB1 CYT 0.8603959917 0.0302431783 66 0.9471238
297 SMC1A CYT 0.0378109452 -0.2455181865 66 0.2177389
298 SMC3 CYT 0.0106476354 -0.4794027756 66 0.1645371
299 SOS1 CYT 0.0735742008 -0.5106359678 66 0.3372768
300 SOX17 CYT NA NA 66 NA
301 SOX9 CYT 0.2720990408 -0.2880975660 66 0.5979018
302 SP8 CYT 0.6056335422 -0.0795371917 66 0.8864464
303 SPEN CYT 0.4137381383 -0.1307578841 66 0.7350454
304 SPOP CYT 0.4937079040 -0.1163255943 66 0.7927810
305 SSX5 CYT 0.0176896125 -0.2782387347 66 0.1645371
306 STAG2 CYT 0.7358351496 -0.0361668776 66 0.9155831
307 STK11 CYT 0.2756778070 -0.0962675375 66 0.6018065
308 STK19 CYT 0.9195002559 -0.0339570139 66 0.9657644
309 STX2 CYT 0.8614639322 0.0596590221 66 0.9471238
310 TAP1 CYT 0.8217923994 -0.0541674515 66 0.9367872
311 TBC1D12 CYT 0.0030629097 -0.6510756786 66 0.1278765
312 TBX3 CYT 0.0211902359 -1.4307695771 66 0.1645371
313 TCAP CYT 0.7690752856 -0.0382707544 66 0.9206851
314 TCEB1 CYT 0.5537416381 0.6922320814 66 0.8368765
315 TCF7L2 CYT 0.0139286574 -0.5571592972 66 0.1645371
316 TCP11L2 CYT 0.0234501924 -1.4119238916 66 0.1645371
317 TDRD10 CYT NA NA 66 NA
318 TET2 CYT 0.8700391371 0.0251787359 66 0.9471238
319 TGFBR2 CYT 0.2410159651 -0.1709532693 66 0.5727406
320 TIMM17A CYT 0.0886635488 -1.0126795815 66 0.3948483
321 TM4SF1 CYT NA NA 66 NA
322 TMCO2 CYT 0.0226321903 -0.4299189438 66 0.1645371
323 TMED10 CYT 0.9754496157 -0.0064733788 66 0.9883359
324 TMEM30B CYT 0.8108411658 0.0456299567 66 0.9306562
325 TMEM88 CYT 0.0306728186 -0.2893176470 66 0.1932966
326 TMEM92 CYT 0.4937079040 -0.1163255943 66 0.7927810
327 TNF CYT 0.9195002559 -0.0339570139 66 0.9657644
328 TNFRSF14 CYT 0.2137844209 -0.1987981697 66 0.5328656
329 TP53 CYT 0.1020526494 -0.2279628834 66 0.4274583
330 TP53BP1 CYT 0.0019768208 -0.4904547726 66 0.1173852
331 TPX2 CYT NA NA 66 NA
332 TRAF3 CYT 0.7145524048 -0.0785929550 66 0.9155831
333 TRAT1 CYT 0.9097034969 -0.0064229363 66 0.9653268
334 TRIM23 CYT 0.1518926906 -0.1737585260 66 0.4623534
335 TRIM6-TRIM34 CYT 0.3495761886 -0.1412690677 66 0.7010256
336 TSC1 CYT 0.4559656564 -0.0779377154 66 0.7679481
337 TTLL9 CYT NA NA 66 NA
338 TXLNA CYT 0.1772431861 -0.2342395151 66 0.4843293
339 TXNDC8 CYT 0.1749327333 -0.1604550403 66 0.4843293
340 U2AF1 CYT 0.3430449908 -0.1260023839 66 0.6986404
341 UBC CYT 0.7315886825 0.1163753977 66 0.9155831
342 UBXN4 CYT 0.3088094380 -0.1240598018 66 0.6406357
343 UPP1 CYT 0.0993374304 -0.3126746418 66 0.4274583
344 USO1 CYT 0.9648816765 0.0075206341 66 0.9883359
345 VHL CYT 0.0195090338 -0.5278277048 66 0.1645371
346 WASF3 CYT 0.0189776363 -0.3252356569 66 0.1645371
347 WT1 CYT 0.6494505788 0.1684013295 66 0.9016797
348 XCL1 CYT NA NA 66 NA
349 XIRP2 CYT 0.1763654104 -0.3196851763 66 0.4843293
350 XPO1 CYT NA NA 66 NA
351 ZFHX3 CYT 0.2504392309 -0.1235388227 66 0.5780215
352 ZFP36L1 CYT 0.9719159506 0.0053788423 66 0.9883359
353 ZNF180 CYT 0.5350707548 -0.0811770146 66 0.8351104
354 ZNF471 CYT 0.1180041343 -0.1343807268 66 0.4478793
355 ZNF483 CYT 0.1749327333 -0.1604550403 66 0.4843293
356 ZNF492 CYT 0.7081611335 0.0598735317 66 0.9155831
357 ZNF620 CYT 0.1446213643 -0.1980497415 66 0.4623534
358 ZNF706 CYT 0.5537416381 0.6922320814 66 0.8368765
359 ZNF750 CYT 0.1003821088 -0.4756575155 66 0.4274583
360 ZRANB3 CYT 0.3088094380 -0.1240598018 66 0.6406357I don’t believe there’s anything here, but I haven’t run the most recent version. So run this.
Total nonsynonymous variant burden
Next, we’ll look at the overall distribution of neoepitope burden and neoepitope elimination scores in our cohort.
Control for tumour content.
Neoepitope burden
neoediting_res <- supp_neoediting(neoediting_outdir, ihc_table, db_path, tree_branch_data,
wtfilter = TRUE, full_epitopes = FALSE, snv_cluster_files = snv_cluster_files)
snv_load <- neoediting_res$nonsynonymous %>% group_by(sample_id, patient_id) %>%
summarise(nnonsyn = n(), nepitopes = length(which(`NetMHCpan MT Percentile` <=
2))) %>% plyr::rename(c(sample_id = "condensed_id"))
snv_load$epitope_pct <- with(snv_load, nepitopes/nnonsyn)
median(snv_load$epitope_pct)[1] 0.6875ihc_table_subset <- subset(ihc_table, condensed_id %in% unique(somatic_snvs_filtered$condensed_id),
select = c("condensed_id", all_tiltypes))
ihc_cyt <- plyr::join(ihc_table_subset, cyt_expr_df)
ihc_cyt_snv <- plyr::join(snv_load %>% as.data.frame, ihc_cyt %>% as.data.frame)cor1 <- with(ihc_cyt_snv, cor.test(E_CD8_density, nepitopes, method = "spearman"))
cor2 <- with(ihc_cyt_snv, cor.test(CYT, nepitopes, method = "spearman"))
pvals <- p.adjust(c(cor1$p.value, cor2$p.value), method = "BH")
pvals[1] 0.07771216 0.21730229None of these are significant after BH correction. Therefore, consistent with the findings in the Brown et al. paper, there is no significant correlation between neoepitope burden and immune covariates in HGSC. And note that these are technically done incorrectly, more correctly we should be either considering patient as a random effect or taking the average within each patient, i.e. as:
Random effect patient
mod1 <- lmer(E_CD8_density ~ nepitopes + (1 | patient_id), data = ihc_cyt_snv %>%
subset(!is.na(E_CD8_density)))
mod2 <- lmer(CYT ~ nepitopes + (1 | patient_id), data = ihc_cyt_snv %>% subset(!is.na(CYT)))
mod3 <- lmer(E_CD8_density ~ nnonsyn + (1 | patient_id), data = ihc_cyt_snv %>%
subset(!is.na(E_CD8_density)))
mod4 <- lmer(CYT ~ nnonsyn + (1 | patient_id), data = ihc_cyt_snv %>% subset(!is.na(CYT)))
mod1 %>% summaryLinear mixed model fit by REML t-tests use Satterthwaite approximations
to degrees of freedom [lmerMod]
Formula: E_CD8_density ~ nepitopes + (1 | patient_id)
Data: ihc_cyt_snv %>% subset(!is.na(E_CD8_density))
REML criterion at convergence: 547.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.0589 -0.3780 -0.1331 0.0828 3.4086
Random effects:
Groups Name Variance Std.Dev.
patient_id (Intercept) 1841.0 42.91
Residual 845.8 29.08
Number of obs: 55, groups: patient_id, 13
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 38.18691 16.99028 23.97000 2.248 0.0341 *
nepitopes -0.09494 0.38669 50.71000 -0.246 0.8070
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
nepitopes -0.669mod2 %>% summaryLinear mixed model fit by REML t-tests use Satterthwaite approximations
to degrees of freedom [lmerMod]
Formula: CYT ~ nepitopes + (1 | patient_id)
Data: ihc_cyt_snv %>% subset(!is.na(CYT))
REML criterion at convergence: 163.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.08774 -0.53765 0.07283 0.47628 2.41896
Random effects:
Groups Name Variance Std.Dev.
patient_id (Intercept) 1.2556 1.1205
Residual 0.5704 0.7552
Number of obs: 56, groups: patient_id, 13
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.170960 0.435442 22.980000 16.468 3.24e-14 ***
nepitopes -0.004100 0.009981 51.420000 -0.411 0.683
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
nepitopes -0.660mod3 %>% summaryLinear mixed model fit by REML t-tests use Satterthwaite approximations
to degrees of freedom [lmerMod]
Formula: E_CD8_density ~ nnonsyn + (1 | patient_id)
Data: ihc_cyt_snv %>% subset(!is.na(E_CD8_density))
REML criterion at convergence: 548
Scaled residuals:
Min 1Q Median 3Q Max
-2.0510 -0.3571 -0.1283 0.0561 3.3920
Random effects:
Groups Name Variance Std.Dev.
patient_id (Intercept) 1890.9 43.48
Residual 841.2 29.00
Number of obs: 55, groups: patient_id, 13
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 33.9913 18.3691 26.1300 1.850 0.0756 .
nnonsyn 0.0338 0.3206 50.3600 0.105 0.9165
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
nnonsyn -0.719mod4 %>% summaryLinear mixed model fit by REML t-tests use Satterthwaite approximations
to degrees of freedom [lmerMod]
Formula: CYT ~ nnonsyn + (1 | patient_id)
Data: ihc_cyt_snv %>% subset(!is.na(CYT))
REML criterion at convergence: 162.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.00118 -0.53648 0.04014 0.52850 2.44067
Random effects:
Groups Name Variance Std.Dev.
patient_id (Intercept) 1.2521 1.1190
Residual 0.5518 0.7428
Number of obs: 56, groups: patient_id, 13
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.486128 0.468666 26.310000 15.973 4.66e-15 ***
nnonsyn -0.010580 0.008215 51.370000 -1.288 0.204
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr)
nnonsyn -0.718Number of neoepitopes is still not significantly correlated with TIL or CYT within patients.
Patient average
ihc_cyt_snv_mean <- ihc_cyt_snv %>% subset(select = -c(condensed_id)) %>% group_by(patient_id) %>%
summarise_all(.funs = function(x) mean(x, na.rm = TRUE))mod1 <- lm(E_CD8_density ~ nepitopes, data = ihc_cyt_snv_mean %>% subset(!is.na(E_CD8_density)))
mod2 <- lm(CYT ~ nepitopes, data = ihc_cyt_snv_mean %>% subset(!is.na(CYT)))
mod3 <- lm(E_CD8_density ~ nnonsyn, data = ihc_cyt_snv_mean %>% subset(!is.na(E_CD8_density)))
mod4 <- lm(CYT ~ nnonsyn, data = ihc_cyt_snv_mean %>% subset(!is.na(CYT)))
mod1 %>% summary
Call:
lm(formula = E_CD8_density ~ nepitopes, data = ihc_cyt_snv_mean %>%
subset(!is.na(E_CD8_density)))
Residuals:
Min 1Q Median 3Q Max
-38.13 -27.02 -19.55 18.52 120.44
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.7207 23.7871 1.922 0.0809 .
nepitopes -0.3807 0.7019 -0.542 0.5983
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 45.92 on 11 degrees of freedom
Multiple R-squared: 0.02605, Adjusted R-squared: -0.06249
F-statistic: 0.2942 on 1 and 11 DF, p-value: 0.5983mod2 %>% summary
Call:
lm(formula = CYT ~ nepitopes, data = ihc_cyt_snv_mean %>% subset(!is.na(CYT)))
Residuals:
Min 1Q Median 3Q Max
-2.04869 -0.77185 0.05659 0.42544 2.07090
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.36250 0.62851 11.71 1.49e-07 ***
nepitopes -0.01094 0.01855 -0.59 0.567
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.213 on 11 degrees of freedom
Multiple R-squared: 0.03066, Adjusted R-squared: -0.05747
F-statistic: 0.3479 on 1 and 11 DF, p-value: 0.5672mod3 %>% summary
Call:
lm(formula = E_CD8_density ~ nnonsyn, data = ihc_cyt_snv_mean %>%
subset(!is.na(E_CD8_density)))
Residuals:
Min 1Q Median 3Q Max
-39.23 -27.20 -17.18 20.69 116.09
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.2690 26.2040 2.109 0.0587 .
nnonsyn -0.5028 0.5668 -0.887 0.3940
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 44.95 on 11 degrees of freedom
Multiple R-squared: 0.06676, Adjusted R-squared: -0.01808
F-statistic: 0.7869 on 1 and 11 DF, p-value: 0.394mod4 %>% summary
Call:
lm(formula = CYT ~ nnonsyn, data = ihc_cyt_snv_mean %>% subset(!is.na(CYT)))
Residuals:
Min 1Q Median 3Q Max
-1.99441 -0.82854 0.04783 0.41722 2.18126
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.373379 0.709770 10.388 5.05e-07 ***
nnonsyn -0.007967 0.015353 -0.519 0.614
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.218 on 11 degrees of freedom
Multiple R-squared: 0.0239, Adjusted R-squared: -0.06484
F-statistic: 0.2693 on 1 and 11 DF, p-value: 0.6141Still no significant correlation for either nonsynonymous SNV or neoantigen load. In fact, effect sizes are negative, not positive as typically seen (i.e. usually more neoepitopes = higher TIL densities/CYT activity).
Total neoepitope depletion signal
subclonal_rates <- data.frame(neoediting_res$subclonal_rates, muttype = "subclonal")
clonal_rates <- data.frame(neoediting_res$subclonal_rates, muttype = "clonal")
bound_rates <- rbind.fill(list(subclonal_rates, clonal_rates))
bound_rates$neoepitope_depletion <- with(bound_rates, obsratio/expratio)p_neodepletion <- ggplot(bound_rates, aes(x = muttype, y = neoepitope_depletion)) +
stat_boxplot(geom = "errorbar", width = 0.3) + geom_boxplot(width = 0.5) +
theme_bw() + theme_Publication() + ithi.utils::theme_nature()
p_neodepletion
In other words, neoepitope depletion values do not significantly differ from 0 – consistent with the Hacohen results reported for ovarian (LINK FIGURE).
The difference between our analysis and Rooney’s, though, is that we look at INTRA-patient trends, whereas they look at INTER-patient trends. As with the Rooney paper, we do not find anything for ovarian when looking inter-patient (CONFIRM WHETHER THEY SPECIFICALLY REPORTED THIS).
---
title: "Rooney comparison"
---
                        ```{r, echo=FALSE, message=FALSE, warning=FALSE}

######## Snakemake header ########
library(methods)
Snakemake <- setClass(
    "Snakemake",
    slots = c(
        input = "list",
        output = "list",
        params = "list",
        wildcards = "list",
        threads = "numeric",
        log = "list",
        resources = "list",
        config = "list",
        rule = "character"
    )
)
snakemake <- Snakemake(
    input = list('/shahlab/amcpherson/projects/ith3/ith3/notebooks/bespoke/ith_snvs.tsv', '/shahlab/alzhang/projects/ITH_Immune/results/nanostring_results/ith_full/qc/limma_quantile/normalized_expression_voa_labels_filtered.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/clone_data.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/ihc_table.tsv', 'notebooks/rooney_comparison.Rmd', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/branch_data.tsv', '/shahlab/alzhang/projects/ITH_Immune/data/expression/nanostring/pancancer_annotations.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/somatic_coding_variants', '/shahlab/alzhang/pipeline_outputs/ith_immune/neoediting/run6', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_1.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_2.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_3.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_4.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_7.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_9.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_10.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_11.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_12.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_13.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_14.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_15.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_16.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_17.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/tree_data.tsv', '/shahlab/amcpherson/projects/ith3/ith3/notebooks/bespoke/ith_breakpoints.tsv', "nanostring_data" = '/shahlab/alzhang/projects/ITH_Immune/results/nanostring_results/ith_full/qc/limma_quantile/normalized_expression_voa_labels_filtered.tsv', "clone_prevalence_file" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/clone_data.tsv', "ihc_table" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/ihc_table.tsv', "snv_table" = '/shahlab/amcpherson/projects/ith3/ith3/notebooks/bespoke/ith_snvs.tsv', "clone_branch_length_file" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/branch_data.tsv', "nanostring_annotations" = '/shahlab/alzhang/projects/ITH_Immune/data/expression/nanostring/pancancer_annotations.tsv', "somatic_coding_result_dir" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/somatic_coding_variants', "neoediting_outdir" = '/shahlab/alzhang/pipeline_outputs/ith_immune/neoediting/run6', "snv_cluster_files" = c('/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_1.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_2.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_3.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_4.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_7.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_9.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_10.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_11.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_12.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_13.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_14.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_15.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_16.tsv', '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster/patient_17.tsv'), "notebook" = 'notebooks/rooney_comparison.Rmd', "clone_tree_file" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/tree_data.tsv', "breakpoint_table" = '/shahlab/amcpherson/projects/ith3/ith3/notebooks/bespoke/ith_breakpoints.tsv'),
    output = list('/shahlab/alzhang/projects/ITH_Immune/paper/results/review/notebooks/run2/rooney_comparison.nb.html'),
    params = list(c('T_CD8_density', 'T_CD4_density', 'T_CD20_density', 'T_Plasma_density', 'E_CD8_density', 'E_CD4_density', 'E_CD20_density', 'E_Plasma_density', 'S_CD8_density', 'S_CD4_density', 'S_CD20_density', 'S_Plasma_density'), '/shahlab/alzhang/projects/ITH_Immune/external/other_papers/mmc6.xlsx', 'rooney_comparison_analysis', '/shahlab/alzhang/projects/ITH_Immune/metadata/db/immune_project.sqlite3', '/shahlab/alzhang/data/genome/hg19/refseq_genes.bed', "all_tiltypes" = c('T_CD8_density', 'T_CD4_density', 'T_CD20_density', 'T_Plasma_density', 'E_CD8_density', 'E_CD4_density', 'E_CD20_density', 'E_Plasma_density', 'S_CD8_density', 'S_CD4_density', 'S_CD20_density', 'S_Plasma_density'), "refseq_gene_file" = '/shahlab/alzhang/data/genome/hg19/refseq_genes.bed', "name" = 'rooney_comparison_analysis', "db" = '/shahlab/alzhang/projects/ITH_Immune/metadata/db/immune_project.sqlite3', "rooney_mutsigcv_file" = '/shahlab/alzhang/projects/ITH_Immune/external/other_papers/mmc6.xlsx'),
    wildcards = list(),
    threads = 1,
    log = list('/shahlab/alzhang/clusttmp/paperreview2/notebooks/rooney_comparison_analysis.log'),
    resources = list(),
    config = list("nanostring_data" = '/shahlab/alzhang/projects/ITH_Immune/results/nanostring_results/ith_full/qc/limma_quantile/normalized_expression_voa_labels_filtered.tsv', "tils_for_variability" = c('T_CD8_density', 'T_CD4_density', 'T_CD20_density', 'T_Plasma_density'), "neoediting_outdir" = '/shahlab/alzhang/pipeline_outputs/ith_immune/neoediting/run6', "snv_table" = '/shahlab/amcpherson/projects/ith3/ith3/notebooks/bespoke/ith_snvs.tsv', "total_tiltypes" = c('T_CD8_density', 'T_CD4_density', 'T_CD20_density', 'T_Plasma_density'), "ith_stat_types" = c('entropy', 'postprocessed_divergence', 'combined_ith_normalized', 'proportion_subclonal'), "clola_result_file" = '/shahlab/alzhang/pipeline_outputs/ith_immune/clola/run4/clola_condensed_results/beta/clola_results.tsv', "logdir" = '/shahlab/alzhang/clusttmp/paperreview2', "tilcluster_supervised_ipynb" = '/shahlab/alzhang/projects/ITH_Immune/paper/review/ipy/tilcluster_supervisedmulticlass.ipynb', "icgc_subtypes" = '/shahlab/alzhang/data/ICGC/icgc_primary_tumour_subtypes.tsv', "epitopes_unique_filtered" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/epitopes_unique_filtered.tsv', "benchmarkdir" = '/shahlab/alzhang/benchmarks/paperreview2', "ith_stats" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/ith_statistics.tsv', "igpartition_outdir" = '/shahlab/alzhang/pipeline_outputs/ith_immune/igpartition/run22', "icgc_specimen" = '/shahlab/alzhang/data/ICGC/specimen.tsv', "prevalence_threshold" = 0.01, "patients_for_clonal" = c(1, 2, 3, 4, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17), "ihc_features_output" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/intermediates/run2/ihc_features_output.txt', "til_clusters_output" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/intermediates/run2/til_clusters_output.txt', "rooney_mutsigcv_file" = '/shahlab/alzhang/projects/ITH_Immune/external/other_papers/mmc6.xlsx', "xcr_table" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/xcr_table.tsv', "image_summary" = '/shahlab/alzhang/data/ithi/yuan_hecr_image_results.csv', "snv_cluster_dir" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/snv_cluster', "tils_for_cluster" = c('E_CD8_density', 'E_CD4_density', 'E_CD20_density', 'E_Plasma_density', 'S_CD8_density', 'S_CD4_density', 'S_CD20_density', 'S_Plasma_density'), "ith_icgc_bc" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/ith_icgc_merged_bc.tsv', "distance_method" = 'horn', "remixt_cellularity_ploidy" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/remixt_cellularity_ploidy.tsv', "refseq_gene_file" = '/shahlab/alzhang/data/genome/hg19/refseq_genes.bed', "clone_trees" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/tree_data.tsv', "molsubtypes" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/molsubtypes.tsv', "nanostring_annotations" = '/shahlab/alzhang/projects/ITH_Immune/data/expression/nanostring/pancancer_annotations.tsv', "all_tiltypes" = c('T_CD8_density', 'T_CD4_density', 'T_CD20_density', 'T_Plasma_density', 'E_CD8_density', 'E_CD4_density', 'E_CD20_density', 'E_Plasma_density', 'S_CD8_density', 'S_CD4_density', 'S_CD20_density', 'S_Plasma_density'), "mmctm_final_patient_dir" = '/shahlab/alzhang/projects/ITH_Immune/results/mmctm_results/ith_by-patient_with-ov', "table_dir" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/review/tables/run2', "image_summary2" = '/shahlab/alzhang/data/ithi/yuan_hecr_image_results_2.csv', "tumour_purity" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/tumour_purity.tsv', "bcr_diversity" = '/shahlab/alzhang/pipeline_outputs/ith_immune/mixcr/mixcr_runs/ith_1_2_3/mixcr5/postprocess/IGH/postfilter_diversity_stats/diversity.strict.resampled.txt', "ihc_table" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/ihc_table.tsv', "clone_branch_lengths" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/branch_data.tsv', "clone_prevalences" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/clones/clone_data.tsv', "copynumber_table" = '/shahlab/alzhang/data/ithi/master_copynumber_file.tsv', "he_results_dir" = '/shahlab/alzhang/data/ithi/finn_results/he_output_Nov29', "db" = '/shahlab/alzhang/projects/ITH_Immune/metadata/db/immune_project.sqlite3', "somatic_coding_result_dir" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/tables/run2/somatic_coding_variants', "array_expression_file" = '/shahlab/alzhang/projects/ITH_Immune/data/expression/array/gene_exprs_rma_batch_corrected.txt', "notebook_dir" = '/shahlab/alzhang/projects/ITH_Immune/paper/results/review/notebooks/run2', "known_subtypes_array" = '/shahlab/alzhang/projects/ITH_Immune/data/expression/array/subtypes/known_subtypes.tsv', "variability_type" = 'stabilize', "tcr_diversity" = '/shahlab/alzhang/pipeline_outputs/ith_immune/mixcr/mixcr_runs/ith_1_2_3/mixcr5/postprocess/TRB/postfilter_diversity_stats/diversity.strict.resampled.txt', "breakpoint_table" = '/shahlab/amcpherson/projects/ith3/ith3/notebooks/bespoke/ith_breakpoints.tsv', "finnhe_pipeline_results_dir" = '/shahlab/alzhang/pipeline_outputs/ith_immune/finnhe/run1'),
    rule = 'rooney_comparison_analysis'
)
######## Original script #########

                        ```


## Setup

```{r global_chunk_options, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy=TRUE, warning=FALSE, message=FALSE, cache=TRUE) #cache=TRUE
```

```{r}
library(ithi.utils)
load_base_libs()

library(methods)
library(bedr)
library(biomaRt)
library(BSgenome.Hsapiens.UCSC.hg19)
library(gdata)

library(ithi.meta)
library(ithi.figures)
library(ithi.utils)
library(ithi.expr)
library(ithi.seq)
library(ithi.bed)
library(ithi.clones)
library(ithi.supp)
```

```{r}
nanostring_data_path <- snakemake@input$nanostring_data
nanostring_annotations_path <- snakemake@input$nanostring_annotations
ihc_table_path <- snakemake@input$ihc_table
master_variant_file <- snakemake@input$snv_table
master_breakpoint_file <- snakemake@input$breakpoint_table
somatic_coding_result_dir <- snakemake@input$somatic_coding_result_dir
neoediting_outdir <- snakemake@input$neoediting_outdir
snv_cluster_files <- snakemake@input$snv_cluster_files
clone_tree_file <- snakemake@input$clone_tree_file
clone_branch_length_file <- snakemake@input$clone_branch_length_file
clone_prevalence_file <- snakemake@input$clone_prevalence_file

rooney_full_mutsigcv_list_file <- snakemake@params$rooney_mutsigcv_file
refseq_gene_file <- snakemake@params$refseq_gene_file
db_path <- snakemake@params$db

all_tiltypes <- snakemake@params$all_tiltypes
```

```{r}
annotation_colours <- ithi.figures::get_annotation_colours()
ihc_table <- fread(ihc_table_path)

mart <- useDataset("hsapiens_gene_ensembl", useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "feb2014.archive.ensembl.org"))

titan_file <- file.path(somatic_coding_result_dir, "somatic_cnv_titan.tsv")
somatic_snv_file <- file.path(somatic_coding_result_dir, "somatic_snvs.tsv")
somatic_indel_file <- file.path(somatic_coding_result_dir, "somatic_indels.tsv")
titan_cnv <- fread(titan_file)
somatic_snvs <- fread(somatic_snv_file)
somatic_indels <- fread(somatic_indel_file)

master_variant_table <- read_variant_file(master_variant_file, db_path)
master_breakpoint_table <- read_variant_file(master_breakpoint_file, db_path)

exprs <- fread(nanostring_data_path)
nanostring_labels <- fread(nanostring_annotations_path)

tree_branch_data <- read_clone_tree_data(clone_tree_file, clone_branch_length_file, clone_prevalence_file, db_path)
```

## Rooney comparison

In this document, we'll compare to both the Rooney variant-immune association findings and the neoepitope elimination findings. 

## Analysis

First, we'll see whether or not particular mutations (the same set as Rooney) are associated with immune activity (cytotoxicity gene signature or IHC variables) in our cohort. 

```{r, cache=TRUE, results='hide'}
somatic_snvs_filtered <- plyr::join(somatic_snvs, master_variant_table %>% subset(is_present == 1), type = 'inner')
somatic_indels_filtered <- subset(somatic_indels, mappability == 1)

titan_bed <- convert_to_bed(titan_cnv)
refseq_bed <- read_bed(refseq_gene_file)

gene_names <- getBM(attributes = c("refseq_mrna", "hgnc_symbol"), values = refseq_bed$refseq_id, mart = mart)

refseq_bed_annotated <- merge(refseq_bed, gene_names %>% subset(refseq_mrna != "") %>% plyr::rename(c('refseq_mrna'='refseq_id')), by = c('refseq_id'))

refseq_bed_annotated <- subset(refseq_bed_annotated, select=c("chr", "start", "end", "hgnc_symbol", "refseq_id"))
refseq_bed_annotated <- bedr.sort.region(refseq_bed_annotated)

titan_merged <- bedr(engine = "bedtools", input = list(a=titan_bed, b=refseq_bed_annotated), method = "intersect", params = "-loj")
titan_merged <- data.frame(titan_merged)
titan_merged$median_logR <- as.numeric(titan_merged$median_logR)
titan_merged$total <- as.numeric(titan_merged$total)
titan_merged$major <- as.numeric(titan_merged$major)
titan_merged$minor <- as.numeric(titan_merged$minor)
```

```{r}
rooney_cytolytic_pancancer_association_genes <- c("CASP8", "B2M", "PIK3CA", "SMC1A", "TET2", "ARID5B", "ALPK2", "LPAR2", "COL5A1",
                                                  "TP53", "NCOR1", "SSX5", "DNER", "MORC4", "IRF6", "MYOCD", "CIC", "SLC22A14", "CNKSR1",
                                                  "NF1", "SOS1", "CUL4B", "DDX3X", "FUBP1", "HLA-A", "HLA-B", "HLA-C", "ARID2", "TCP11L2", "MET",
                                                  "CSNK2A1", "ASXL1", "TMEM88", "DNMT3A", "EP300", "MUC17", "OVOL1")

rooney_mutsigcv_table <- read.xls(rooney_full_mutsigcv_list_file, sheet = 1, header = FALSE, stringsAsFactors = FALSE)
rooney_mutsigcv_genes <- sapply(strsplit(rooney_mutsigcv_table$V1, ":"), function(x) x[1])
rooney_mutsigcv_genes <- rooney_mutsigcv_genes[rooney_mutsigcv_genes != "HLA-A,B,C"]
rooney_mutsigcv_genes <- c(rooney_mutsigcv_genes, c("HLA-A", "HLA-B", "HLA-C"))
```

### Linear modeling for mutations

Check whether this is done only for point mutations or indels too. 

They only do it for point mutations; I think this makes sense because of the low indel count. Let's do it similarly, then. 

```{r}
somatic_snvs_rooney <- subset(somatic_snvs_filtered, gene_name %in% rooney_mutsigcv_genes)
somatic_indels_rooney <- subset(somatic_indels_filtered, gene_name %in% rooney_mutsigcv_genes)
titan_merged_rooney <- subset(titan_merged, hgnc_symbol %in% rooney_mutsigcv_genes)
```

```{r}
perc.rank <- function(x) trunc(rank(x))/length(x)

compute_num_transition_events <- function(titan_merged) {
  titan_merged_unique <- subset(titan_merged, select=c(chr, start, end, patient_id, condensed_id, median_logR, major, minor, total)) %>% unique
  titan_merged_unique_ordered <- titan_merged_unique[with(titan_merged_unique, order(condensed_id, chr, start, end)),]
  
  dfs <- split(titan_merged_unique_ordered, f = titan_merged_unique_ordered$condensed_id)
  event_counts <- lapply(dfs, function(x) {
    counter <- 0
    prev_chrom <- "chr0"
    prev_cn_sign <- 1
    for (i in 1:nrow(x)) {
      row <- x[i,]
      if ((row$chr == prev_chrom) & (sign(row$total - 2) != prev_cn_sign)) {
        counter <- counter + 1
      }
      prev_chrom <- row$chr
      prev_logR_sgn <- sign(row$total)
    }
    return(counter)
  })
  
  event_df <- data.frame(event_counts, check.names = FALSE) %>% t %>% as.data.frame
  colnames(event_df) <- "chr_events"
  event_df <- event_df %>% rownames_to_column(var = "condensed_id")
  return(event_df)
}


transition_events <- compute_num_transition_events(titan_merged)

cyt_expr <- subset(exprs, Name %in% c("GZMA", "PRF1"), select =-c(Code.Class, Accession))
cyt_expr_combined <- cyt_expr %>% as.data.frame %>% column_to_rownames(var = "Name") %>% t %>% as.data.frame
cyt_expr_combined$CYT <- with(cyt_expr_combined, ithi.utils::p.geomean(GZMA, PRF1))
cyt_expr_combined <- subset(cyt_expr_combined, select=-c(GZMA, PRF1))
cyt_expr_df <- cyt_expr_combined %>% rownames_to_column(var = "condensed_id")
cyt_expr_df$condensed_id <- cyt_expr_df$condensed_id %>% ithi.meta::map_id(from = "voa", to = "condensed_id", db_path)

linear_model_til_variant <- function(selected_variants, all_variants, ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, 
                                 immune_variable = "CYT", variant_type = "snv", cn_type = "amp", transition_events = NULL) {
  if (variant_type == "snv") {
    background_variant_rate <- all_variants %>% group_by(condensed_id) %>% summarise(nvariant=n())
  } else if (variant_type == "cnv") {
    all_variants_filtered <- subset(all_variants, total != 2)
    all_variants_filtered_unique <- subset(all_variants_filtered, select=c(chr, start, end, patient_id, condensed_id, median_logR, major, minor, total)) %>% unique
    all_variants_filtered_unique$event_type <- sign(all_variants_filtered_unique$total - 2)
    background_variant_rate <- all_variants_filtered_unique %>% group_by(condensed_id, event_type) %>% summarise(nvariant=n())
    
    background_variant_rate_expanded <- dcast(background_variant_rate, formula = condensed_id ~ event_type, value.var = "nvariant") %>% 
      plyr::rename(c('-1'='ndel', '1'='namp'))
    
    background_variant_rate <- merge(background_variant_rate_expanded, transition_events, all=TRUE)
    ## Transition events is the number of chromosomal transitions between +/- logR's, as per Rooney. 
  }
  
  ihc_table_subset <- subset(ihc_table, condensed_id %in% background_variant_rate$condensed_id,
                             select = c("condensed_id", all_tiltypes))
  
  other_tables <- Reduce(function(x,y) merge(x,y,all=TRUE), list(background_variant_rate, ihc_table_subset, cyt_expr_df))
  other_tables <- subset(other_tables, !str_detect(condensed_id, "^SP"))
  other_tables$patient_id <- ithi.meta::map_id(other_tables$condensed_id, from = "condensed_id", to = "patient_id", db_path)
  
  selected_variants$condensed_id <- ithi.meta::factor_id(selected_variants$condensed_id, type = "condensed_id", db_path)
  if (variant_type == "cnv") {
    if (cn_type == "amp") {
      selected_variants <- subset(selected_variants, total > 2)
    } else if (cn_type == "del") {
      selected_variants <- subset(selected_variants, total < 2)
    }
    selected_variants$gene_name <- selected_variants$hgnc_symbol
    
    selected_variants <- subset(selected_variants, select=c(chr, start, end, patient_id, condensed_id, median_logR, major, minor, total, gene_name)) %>% unique
    
    selected_variants <- selected_variants %>% group_by(gene_name, condensed_id, patient_id) %>% summarise(median_logR=max(abs(median_logR))*sign(median_logR[which.max(abs(median_logR))]))
    
    foreground_table <- as.data.frame.matrix(xtabs(median_logR ~ condensed_id + gene_name, data = selected_variants))
  } else {
    foreground_table <- as.data.frame.matrix(xtabs( ~ condensed_id + gene_name, data = selected_variants))
  }
  
  gene_names <- colnames(foreground_table)
  
  test_results <- rbind.fill(lapply(gene_names, function(gene) {
    #print(gene)
    x <- foreground_table[,gene,drop=FALSE] %>% tibble::rownames_to_column(var = "condensed_id")
    x <- merge(other_tables, x, all=TRUE)
    x[,gene][is.na(x[,gene]) & (x$condensed_id %in% background_variant_rate$condensed_id)]<- 0
    
    if (variant_type == "cnv") {
      if (cn_type == "amp") {
        x[,gene][!is.na(x[,gene]) & (x[,gene] < 0)] <- 0
      } else if (cn_type == "del") {
        x[,gene][!is.na(x[,gene]) & (x[,gene] > 0)] <- 0
        x[,gene] <- -1*x[,gene]
      }
    }

    
    if (rank_transform) {
      if (variant_type == "snv") {
        x[,"nvariant"] <- perc.rank(x[,"nvariant"])
      } else if (variant_type == "cnv") {
        x[,"chr_events"] <- perc.rank(x[,"chr_events"])
        x[,"namp"] <- perc.rank(x[,"namp"])
        x[,"ndel"] <- perc.rank(x[,"ndel"])
      }
      x[,immune_variable] <- perc.rank(x[,immune_variable])
    }
    
    if (str_detect(gene, "\\-")) {
      gene_name_mod <- paste0("`", gene, "`")
    } else {
      gene_name_mod <- gene
    }
    
    if (variant_type == "snv") {
      if (control_patient) {
        mod <- lmer(as.formula(paste0(immune_variable, "~", gene_name_mod, "+", "nvariant", "+", "(1|patient_id)")), data = x[!is.na(x[,gene]),])
      } else {
        mod <- lm(as.formula(paste0(immune_variable, "~", gene_name_mod, "+", "nvariant")), data = x[!is.na(x[,gene]),])
      }
    } else if (variant_type == "cnv") {
       if (control_patient) {
        mod <- lmer(as.formula(paste0(immune_variable, "~", gene_name_mod, "+", "namp+ndel+chr_events", "+", "(1|patient_id)")), data = x[!is.na(x[,gene]),])
      } else {
        mod <- lm(as.formula(paste0(immune_variable, "~", gene_name_mod, "+", "namp+ndel+chr_events")), data = x[!is.na(x[,gene]),])
      }
    }
    pval <- unname(summary(mod)$coefficients[,5][gene_name_mod])
    coeff <- unname(summary(mod)$coefficients[,1][gene_name_mod])
    
    return(data.frame(gene=gene, immune_variable=immune_variable, p.value=pval, coef=coeff, n=nrow(x[!is.na(x[,gene]),])))
  }))
  
  test_results$p.adj <- p.adjust(test_results$p.value, method = "BH")
  return(test_results)
}
```

```{r, results='hide', cache=TRUE}
snv_cyt_patient_rank <- linear_model_til_variant(somatic_snvs_rooney, somatic_snvs_filtered, ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT", variant_type = "snv")
snv_ecd8_patient_rank <- linear_model_til_variant(somatic_snvs_rooney, somatic_snvs_filtered, ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "E_CD8_density", variant_type = "snv")
```

```{r}
print(snv_cyt_patient_rank)
print(snv_ecd8_patient_rank)
```

Using the same regression approach as Rooney, none of the SNVs are significantly correlated with the status of any of these point mutations. Note that TP53, which was reported to be significant by them, has a significant uncorrected p-value, but not after BH adjustment. 

### Linear modeling for copy number

```{r, results='hide', cache=TRUE}
cnvamp_cyt_patient_rank <- linear_model_til_variant(titan_merged_rooney, titan_merged, ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT", variant_type = "cnv", cn_type = "amp", transition_events)
cnvdel_cyt_patient_rank <- linear_model_til_variant(titan_merged_rooney, titan_merged, ihc_table, cyt_expr_df, rank_transform = TRUE, control_patient = TRUE, immune_variable = "CYT", variant_type = "cnv", cn_type = "del", transition_events)
```

```{r}
print(cnvamp_cyt_patient_rank)
print(cnvdel_cyt_patient_rank)
```

I don't believe there's anything here, but I haven't run the most recent version. So run this. 

### Total nonsynonymous variant burden

Next, we'll look at the overall distribution of neoepitope burden and neoepitope elimination scores in our cohort. 

Control for tumour content.  

### Neoepitope burden

```{r, cache = TRUE}
neoediting_res <- supp_neoediting(neoediting_outdir, ihc_table, db_path, tree_branch_data, wtfilter = TRUE, full_epitopes = FALSE, 
                                  snv_cluster_files = snv_cluster_files)

snv_load <- neoediting_res$nonsynonymous %>% group_by(sample_id, patient_id) %>% summarise(nnonsyn=n(), nepitopes=length(which(`NetMHCpan MT Percentile` <= 2))) %>% plyr::rename(c('sample_id'='condensed_id'))

snv_load$epitope_pct <- with(snv_load, nepitopes/nnonsyn)
median(snv_load$epitope_pct)
```

```{r}
ihc_table_subset <- subset(ihc_table, condensed_id %in% unique(somatic_snvs_filtered$condensed_id),
                             select = c("condensed_id", all_tiltypes))
ihc_cyt <- plyr::join(ihc_table_subset, cyt_expr_df)

ihc_cyt_snv <- plyr::join(snv_load %>% as.data.frame, ihc_cyt %>% as.data.frame)
```

```{r}
cor1 <- with(ihc_cyt_snv, cor.test(E_CD8_density, nepitopes, method = "spearman"))

cor2 <- with(ihc_cyt_snv, cor.test(CYT, nepitopes, method = "spearman"))

pvals <- p.adjust(c(cor1$p.value, cor2$p.value), method = "BH")

pvals
```

None of these are significant after BH correction. Therefore, consistent with the findings in the Brown et al. paper, there is no significant correlation between neoepitope burden and immune covariates in HGSC. And note that these are technically done incorrectly, more correctly we should be either considering patient as a random effect or taking the average within each patient, i.e. as:

#### Random effect patient

```{r}
mod1 <- lmer(E_CD8_density ~ nepitopes + (1|patient_id), data = ihc_cyt_snv %>% subset(!is.na(E_CD8_density)))
mod2 <- lmer(CYT ~ nepitopes+ (1|patient_id), data = ihc_cyt_snv %>% subset(!is.na(CYT)))
mod3 <- lmer(E_CD8_density ~ nnonsyn + (1|patient_id), data = ihc_cyt_snv %>% subset(!is.na(E_CD8_density)))
mod4 <- lmer(CYT ~ nnonsyn+ (1|patient_id), data = ihc_cyt_snv %>% subset(!is.na(CYT)))

mod1 %>% summary
mod2 %>% summary
mod3 %>% summary
mod4 %>% summary
```

Number of neoepitopes is still not significantly correlated with TIL or CYT within patients. 

#### Patient average

```{r}
ihc_cyt_snv_mean <- ihc_cyt_snv %>% subset(select=-c(condensed_id)) %>% group_by(patient_id) %>% summarise_all(.funs = function(x) mean(x,na.rm=TRUE))
```

```{r}
mod1 <- lm(E_CD8_density ~ nepitopes, data = ihc_cyt_snv_mean %>% subset(!is.na(E_CD8_density)))
mod2 <- lm(CYT ~ nepitopes, data = ihc_cyt_snv_mean %>% subset(!is.na(CYT)))
mod3 <- lm(E_CD8_density ~ nnonsyn, data = ihc_cyt_snv_mean %>% subset(!is.na(E_CD8_density)))
mod4 <- lm(CYT ~ nnonsyn, data = ihc_cyt_snv_mean %>% subset(!is.na(CYT)))

mod1 %>% summary
mod2 %>% summary
mod3 %>% summary
mod4 %>% summary
```

Still no significant correlation for either nonsynonymous SNV or neoantigen load. In fact, effect sizes are negative, not positive as typically seen (i.e. usually more neoepitopes = higher TIL densities/CYT activity).

### Total neoepitope depletion signal

```{r}
subclonal_rates <- data.frame(neoediting_res$subclonal_rates, muttype="subclonal")
clonal_rates <- data.frame(neoediting_res$subclonal_rates, muttype="clonal")

bound_rates <- rbind.fill(list(subclonal_rates, clonal_rates))

bound_rates$neoepitope_depletion <- with(bound_rates, obsratio/expratio)
```

```{r}
p_neodepletion <- ggplot(bound_rates, aes(x=muttype, y = neoepitope_depletion)) + stat_boxplot(geom = 'errorbar', width = 0.3) + geom_boxplot(width = 0.5) + theme_bw() + theme_Publication() + ithi.utils::theme_nature() 

p_neodepletion
```

In other words, neoepitope depletion values do not significantly differ from 0 -- consistent with the Hacohen results reported for ovarian (LINK FIGURE). 

The difference between our analysis and Rooney's, though, is that we look at INTRA-patient trends, whereas they look at INTER-patient trends. As with the Rooney paper, we do not find anything for ovarian when looking inter-patient (CONFIRM WHETHER THEY SPECIFICALLY REPORTED THIS). 

