diff --git a/mid_res/nat_adj/1.calc.lcpm.baselineH1_as03_modelgenes.r b/mid_res/nat_adj/1.calc.lcpm.baselineH1_as03_modelgenes.r
deleted file mode 100644
index 7fcf577..0000000
--- a/mid_res/nat_adj/1.calc.lcpm.baselineH1_as03_modelgenes.r
+++ /dev/null
@@ -1,54 +0,0 @@
-# R version 4.0.5 
-suppressMessages(library(here))
-suppressMessages(library(tidyverse))
-suppressMessages(library(scglmmr))
-source(here('functions/MattPMutils.r'))
-
-# set save paths 
-figpath = here("mid_res/nat_adj/figures/V4/"); dir.create(figpath)
-datapath = here("mid_res/nat_adj/generated_data//V4/"); dir.create(datapath)
-
-# define high responders 
-high.responders = c("205","207","209","212","215","234","237","245","250","256")
-
-# read pb data, subset to day 0 non adj, subset out day 0 metadata. 
-pb = readRDS(file = here('mid_res/variance_partition/generated_data/pb_vp.rds'))
-pb = lapply(pb, function(x) x = x[ ,1:40])
-cnames = gsub("~.*","",colnames(pb[[1]]))
-pb = lapply(pb, function(x){
-  x %>% as.data.frame() %>% setNames(nm = cnames) %>% as.matrix() 
-})
-d0 = readRDS(file = here('mid_res/1_H1N1_pseudobulk_DE/dataV3/d0.rds'))
-d0d = lapply(pb, function(x){ x = x[ , rownames(d0)]})
-
-# make a list of genes indxed by celltype for genes to fit from H5 model 
-av_tidy = readRDS(file = here('mid_res/3_H1N1_H5N1_joint_pseudobulk_DE/dataV4/git_ignore/av_tidy.rds'))
-genes.test = lapply(av_tidy , function(x) unique(x$gene))
-
-# check names are the same of indexed genes and average data
-stopifnot(isTRUE(all.equal(names(d0d), names(genes.test))))
-
-# calcualte log CPM of the baseline pseudobulk data
-av = list()
-for (i in 1:length(d0d)) {
-  dge =  edgeR::DGEList( d0d[[i]] ) 
-  dge = dge[genes.test[[i]], ]
-  av[[i]]  = edgeR::cpm(dge, log = TRUE)
-}
-names(av) = names(d0d)
-saveRDS(av,file = paste0(datapath,'av.rds'))
-
-# tidy aggregated data 
-av0 = list()
-for (i in 1:length(pb)) {
-  ct = names(av)[i]
-  gs = rownames(av[[i]])
-  av0[[i]] = GetTidySummary(
-    av.exprs.list = av,
-    celltype.index = i,
-    genes.use = gs) %>%
-    mutate(response = if_else(str_sub(sample, 1, 3) %in%  high.responders, 'High', "Low")) %>%
-    mutate(response = factor(response, levels = c('Low', 'High')))
-}
-names(av0) = names(pb)
-saveRDS(av0, file = paste0(datapath,'av0.rds'))
diff --git a/mid_res/nat_adj/2.AS03.innatesigs.vand.validationcohort.r b/mid_res/nat_adj/2.AS03.innatesigs.vand.validationcohort.r
deleted file mode 100644
index eb32678..0000000
--- a/mid_res/nat_adj/2.AS03.innatesigs.vand.validationcohort.r
+++ /dev/null
@@ -1,41 +0,0 @@
-# R version 4.0.5 
-suppressMessages(library(here))
-suppressMessages(library(tidyverse))
-suppressMessages(library(scglmmr))
-suppressMessages(library(magrittr))
-source(here('functions/MattPMutils.r'))
-
-# set save paths 
-figpath = here("mid_res/nat_adj/figures/V4/")
-datapath = here("mid_res/nat_adj/generated_data//V4/")
-
-# define adjuvant signatures 
-# leading edge combined signature 
-# upregulated genes only 
-# mono and mDC specific and combined genes
-li.up = readRDS(file = here('mid_res/3_H1N1_H5N1_joint_pseudobulk_DE/dataV4/gsea/li.up.rds'))
-li2.up = readRDS(file = here('mid_res/3_H1N1_H5N1_joint_pseudobulk_DE/dataV4/gsea/li2.up.rds'))
-li.full = unlist(li.up,use.names = FALSE, recursive = TRUE) %>%  unique 
-mono.genes  = li.up$CD14_Mono %>%  unlist() %>% unique()
-mdc.genes = li2.up$mDC %>%  unlist() %>% unique()
-as03.sig = list('AS03_signature' = li.full, "AS03_Monocyte" = mono.genes, 'AS03_mDC' = mdc.genes)
-
-# vand validation for the highlighted signatures
-vand.fit = readRDS(file = here('mid_res/vand/generated_data/fit1.rds'))
-vand.rank = ExtractResult(model.fit.list = vand.fit,what = 'lmer.z.ranks', coefficient.number = 1, coef.name = 'delta')
-gvand = FgseaList(rank.list.celltype = vand.rank, pathways = as03.sig)
-
-# leading edge from each cell tpe 
-dc.va.le = gvand$DNC %>%  filter(pathway == 'AS03_mDC') %$% leadingEdge %>%  unlist()
-saveRDS(dc.va.le,file = paste0(datapath, 'dc.va.le.rds'))
-
-mono.va.le = gvand$MNC %>%  filter(pathway == 'AS03_Monocyte') %$% leadingEdge %>%  unlist() 
-saveRDS(mono.va.le, file = paste0(datapath, 'mono.va.le.rds'))
-
-
-#mono
-enrline = list(geom_line(color = "deepskyblue3", size = 2 ))
-p = fgsea::plotEnrichment(pathway = as03.sig$AS03_Monocyte, stats = vand.rank$MNC) + enrline
-# mDC
-p = fgsea::plotEnrichment(pathway = as03.sig$AS03_mDC, stats = vand.rank$DNC) + enrline
-ggsave(p, filename = paste0(figpath, 'dc.vand.enr.pdf'), width = 4, height = 2.5)
diff --git a/mid_res/nat_adj/3.natural.adjuvant.signatures.r b/mid_res/nat_adj/3.natural.adjuvant.signatures.r
deleted file mode 100644
index ecb8078..0000000
--- a/mid_res/nat_adj/3.natural.adjuvant.signatures.r
+++ /dev/null
@@ -1,150 +0,0 @@
-# R version 4.0.5 
-suppressMessages(library(here))
-suppressMessages(library(tidyverse))
-suppressMessages(library(scglmmr))
-source(here('functions/MattPMutils.r'))
-
-# set save paths 
-figpath = here("mid_res/nat_adj/figures/V4/")
-datapath = here("mid_res/nat_adj/generated_data/V4/")
-
-# set plot themes to distinguisn between groups / signatures being tested 
-# AS03 theme 
-cu = c("grey48", "grey", "grey48",  "deepskyblue3")
-cu.alpha = sapply(cu, col.alpha, alpha = 0.4) %>% unname()
-mtheme = list(
-  geom_boxplot(show.legend = FALSE, outlier.shape = NA), 
-  theme_bw(base_size = 10.5), 
-  theme(axis.text.x=element_text(angle = -90, hjust = 0)), 
-  theme(strip.background = element_blank(),
-        panel.grid.major = element_blank(), 
-        panel.grid.minor = element_blank(), 
-        strip.text = element_text(face = "bold",family = "Helvetica"), 
-        axis.text.y =  element_text(size = 6), 
-        axis.title.y = element_text(size = 10))
-  )
-
-cua = sapply(c('dodgerblue', 'red'), col.alpha, 0.2) %>% unname()
-cub = c('dodgerblue', 'red')
-baselinetheme = list(
-  mtheme, 
-  theme(axis.text.x=element_text(angle = 0, hjust = 0.5, color = 'black', size = 10), 
-        axis.title.y = element_text(size = 14)), 
-  geom_boxplot(show.legend = FALSE, outlier.shape = 21),
-  scale_y_continuous( breaks= scales::pretty_breaks(), expand = c(0.15,0)), 
-  scale_x_discrete(labels = c("low", "high")), 
-  xlab("Antibody Response"), 
-  scale_fill_manual(values = cua), 
-  scale_color_manual(values = cub) 
-)
-
-# baseline bulk theme 
-cu1 = sapply(c('dodgerblue','grey', 'red'), col.alpha, 0.2) %>% unname()
-cu2 = c('dodgerblue','grey', 'red')
-bulktheme = list(
-  mtheme, 
-  theme(axis.text.x=element_text(angle = 0, hjust = 0.5, color = 'black', size = 10), 
-        axis.title.y = element_text(size = 14)), 
-  geom_boxplot(show.legend = FALSE, outlier.shape = 21),
-  scale_y_continuous( breaks= scales::pretty_breaks(), expand = c(0.15,0)), 
-  scale_x_discrete(labels = c("low", "mid", "high")), 
-  xlab("Antibody Response"), 
-  scale_fill_manual(values = cu1), 
-  scale_color_manual(values = cu2) 
-)
-my_compare = list(l1 = c("1", "0"), l2 = c("2", "0"))
-
-# define adjuvant signatures 
-# leading edge combined signature 
-# upregulated gnees only 
-# mono and mDC specific and combined genes
-li.up = readRDS(file = here('mid_res/3_H1N1_H5N1_joint_pseudobulk_DE/dataV4/gsea/li.up.rds'))
-li2.up = readRDS(file = here('mid_res/3_H1N1_H5N1_joint_pseudobulk_DE/dataV4/gsea/li2.up.rds'))
-li.full = unlist(li.up,use.names = FALSE, recursive = TRUE) %>%  unique 
-mono.genes  = li.up$CD14_Mono %>%  unlist() %>% unique()
-mdc.genes = li2.up$mDC %>%  unlist() %>% unique()
-as03.sig = list('AS03_signature' = li.full, "AS03_Monocyte" = mono.genes, 'AS03_mDC' = mdc.genes)
-
-
-# leading edge signatures from validation cohort
-mono.va.le = readRDS(file = here('mid_res/nat_adj/generated_data/V4/mono.va.le.rds'))
-mono.va.le = list('AS03_Monocyte_LE' = mono.va.le)
-dc.va.le = readRDS(file = here('mid_res/nat_adj/generated_data/V4/dc.va.le.rds'))
-dc.va.le = list('AS03_mDC_LE' = dc.va.le)
-as03.sig = c(as03.sig, mono.va.le, dc.va.le)
-
-
-# load average day 1 comparison cohort data 
-av_tidy = readRDS(file = here('mid_res/3_H1N1_H5N1_joint_pseudobulk_DE/dataV4/gene_dist/av_tidy.rds'))
-
-# load average baseline high  and low responders from unadjuvanted cohort 
-av0 = readRDS(file = here('mid_res/nat_adj/generated_data/V4/av0.rds'))
-
-# load bulk microarray data and process to baseline samples 
-array = data.table::fread("data/CHI_H1N1_data/microarray/CHI_GE_matrix_gene.txt", data.table = F) %>% 
-  tibble::remove_rownames() %>% 
-  tibble::column_to_rownames("gene") %>% 
-  select(., matches("day0")) %>% 
-  select(-matches("day70")) %>% 
-  select(-matches("pre")) %>%
-  select(-matches("day1"))
-
-#############################
-# CD14 Monocytes 
-#############################
-# Monocyte combined AS03 signature average across time between groups 
-#natural adjuvant test
-mono.sig.av2 = 
-  av0$CD14_Mono %>% 
-  filter(gene %in% as03.sig$AS03_Monocyte_LE) %>% 
-  group_by(sample, response) %>% 
-  summarize(meansig = mean(count))
-#plot
-p = ggplot(mono.sig.av2, aes(x = response, y = meansig, fill = response , color = response)) +
-  baselinetheme  +
-  ggpubr::stat_compare_means(method = 'wilcox',paired = FALSE, show.legend = FALSE) + 
-  ylab('Monocyte AS03 Adjuvant Signature') +
-  ggtitle('Baseline: CD14 Monocytes\nunadjuvanted subjects')
-p
-ggsave(p, filename = paste0(figpath, 'baseline_monosig_monocytes_LeadingEdgeVand.pdf'), width = 2.7, height = 4)
-
-#############################
-# mDC
-#############################
-
-# mdc Combined AS03 signature average across time between groups 
-mdc.sig.av = 
-  av_tidy$mDC %>% 
-  filter(gene %in% mdc.genes) %>% 
-  group_by(sample, group) %>% 
-  summarize(meansig = mean(count))
-#plot
-p = ggplot(mdc.sig.av, aes(x = group, y = meansig, fill = group , color = group)) +
-  mtheme + 
-  theme(axis.title.x = element_blank()) +
-  ylab('mDC AS03 Adjuvant Signature') +
-  scale_color_manual(values = cu) +
-  scale_fill_manual(values = cu.alpha) + 
-  ggtitle('mDC')
-ggsave(p,filename = paste0(figpath, 'as03_mDC_sig.pdf'), width = 1.9, height = 3)
-
-
-# natural adjuvant test - mDC
-mdc.sig.av2 = 
-  av0$mDC %>% 
-  filter(gene %in% as03.sig$AS03_mDC) %>% 
-  group_by(sample, response) %>% 
-  summarize(meansig = mean(count))
-#plot
-p = ggplot(mdc.sig.av2, aes(x = response, y = meansig, fill = response , color = response)) +
-  baselinetheme  +
-  ggpubr::stat_compare_means(method = 'wilcox',paired = FALSE, show.legend = FALSE) + 
-  ylab('mDC AS03 Adjuvant Signature') +
-  ggtitle('Baseline: mDCs\nunadjuvanted subjects')
-p
-ggsave(p, filename = paste0(figpath, 'baseline_mDC_mDC_LeadingEdgeVand.pdf'), width = 2.7, height = 4)
-
-
-for (i in 1:length(as03.sig)) {
-  data.table::fwrite(as03.sig[i],file = paste0(datapath, names(as03.sig)[i],'.txt'))
-}
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