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DE_genus_deseq2.R
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# This script perform genus-level DE analysis by DESeq2
library(DESeq2)
setwd("/Users/zrayw/Desktop/Alex_Lab/PN_microbiome/analysis")
## non-lesional v.s. lesional areas at baseline, controlling for individual effect
map_temp = map_sample %>% filter(visit == "V3")
keep = names(which(tapply(map_temp$lesional, map_temp$subjid, function(x){length(unique(x))}) == 2))
count_data = shared %>%
mutate(sample = str_replace_all(string=sample, pattern="non_lesional", replacement="non-lesional")) %>%
separate(col="sample", into = c("unique", "subjid", "lesional","visit"), sep="_", remove=FALSE) %>%
filter(subjid %in% keep & visit == "V3") %>%
arrange(sample) %>%
select(-sample, -unique, -subjid, -lesional, -visit) %>%
t()
col_data = map_sample %>% filter(subjid %in% keep & visit == "V3") %>% arrange(sample)
dds = DESeqDataSetFromMatrix(
countData = count_data, colData = col_data,
design = ~ lesional + subjid
)
dds$lesional = relevel(dds$lesional, ref = "non_lesional")
dds = DESeq(dds)
res = results(dds, name = "lesional_lesional_vs_non_lesional", pAdjustMethod="fdr" )
de_genus_lesional = res@listData %>%
as.data.frame() %>%
mutate(otu = res@rownames) %>%
inner_join(., taxonomy) %>%
select(otu, log2FoldChange, stat, pvalue, padj, kingdom, phylum, class, order, family, genus)
## baseline v.s. week 12 at lesional areas, controlling for individual effect, in treatment groups
map_temp = map_sample %>% filter(lesional == "lesional") ###
keep = names(which(tapply(map_temp$visit, map_temp$subjid, function(x){length(unique(x))}) == 2)) ###
count_data = shared %>%
inner_join(., map_sample %>% select(sample, subjid, trt01p, lesional)) %>%
filter(subjid %in% keep & lesional == "lesional" & trt01p == "Nemolizumab 0.5mg/kg") %>% ###
arrange(sample) %>%
select(-sample, -subjid, -lesional, -trt01p) %>%
t()
col_data = map_sample %>%
filter(subjid %in% keep & lesional == "lesional" & trt01p == "Nemolizumab 0.5mg/kg") %>%
arrange(sample) ###
dds = DESeqDataSetFromMatrix(
countData = count_data, colData = col_data,
design = ~ visit + subjid
)
dds = DESeq(dds)
res = results(dds, name = "visit_V8_vs_V3", pAdjustMethod="fdr" )
de_genus_visit_treatment = res@listData %>%
as.data.frame() %>%
mutate(otu = res@rownames) %>%
inner_join(., taxonomy) %>%
select(otu, log2FoldChange, lfcSE, stat, pvalue, padj, kingdom, phylum, class, order, family, genus)
## baseline v.s. week 12 at lesional areas, controlling for individual effect, in placebo groups
map_temp = map_sample %>% filter(lesional == "lesional") ###
keep = names(which(tapply(map_temp$visit, map_temp$subjid, function(x){length(unique(x))}) == 2)) ###
count_data = shared %>%
inner_join(., map_sample %>% select(sample, subjid, trt01p, lesional)) %>%
filter(subjid %in% keep & lesional == "lesional" & trt01p == "Placebo") %>% ###
arrange(sample) %>%
select(-sample, -subjid, -lesional, -trt01p) %>%
t()
col_data = map_sample %>%
filter(subjid %in% keep & lesional == "lesional" & trt01p == "Placebo") %>% ###
arrange(sample)
dds = DESeqDataSetFromMatrix(
countData = count_data, colData = col_data,
design = ~ visit + subjid
)
dds = DESeq(dds)
res = results(dds, name = "visit_V8_vs_V3", pAdjustMethod="fdr" )
de_genus_visit_placebo = res@listData %>%
as.data.frame() %>%
mutate(otu = res@rownames) %>%
inner_join(., taxonomy) %>%
select(otu, log2FoldChange, lfcSE, stat, pvalue, padj, kingdom, phylum, class, order, family, genus)
## log fold change correlation with lesional v.s. non-lesional at baseline (treatments/ placebos)
### treatments
data = inner_join(
de_genus_lesional %>% select(otu, log2FoldChange, padj),
de_genus_visit_treatment %>% select(otu, log2FoldChange, padj),
by = "otu", suffix = c(".1", ".2")
)
cor.test(
data$log2FoldChange.1,
data$log2FoldChange.2,
method = "spearman"
) # -0.181157 p = 0.001356
data %>%
summarize(con.t = sum(log2FoldChange.1*log2FoldChange.2 < 0)) # 178
data %>%
mutate(
sig.1 = (padj.1 <= 0.1)*1,
sig.2 = (padj.2 <= 0.1)*1,
group = sig.1 + sig.2
) %>%
ggplot(aes(
x = log2FoldChange.1, y = log2FoldChange.2,
color = as.character(sig.1),
shape = as.character(sig.2),
size = as.character(group)
)) +
geom_point() +
geom_abline(intercept = 0, slope = -1, lty = 2) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
labs(
title = NULL,
x = "non-lesional vs\nlesional at baseline",
y = "baseline vs week-\n12 in lesional skin"
) +
coord_cartesian(xlim = c(-10, 10), ylim = c(-10, 10)) +
scale_shape_manual(breaks = c("0", "1"), values = c(16, 17)) +
scale_color_manual(breaks = c("0", "1"), values = c("grey", "black")) +
scale_size_manual(breaks = c("0", "1", "2"), values = c(2, 3, 5)) +
theme_classic() +
theme(
legend.position = "none",
axis.title = element_text(size = 18, face = "bold", family = "Times"),
axis.text = element_text(size = 13, face = "bold", family = "Times")
)
ggsave("manuscript figures/scatter.logfc_deseq2_genus_treatment.tiff", width = 4, height = 4, dpi = 500)
### placebos
data = inner_join(
de_genus_lesional %>% select(otu, log2FoldChange, padj),
de_genus_visit_placebo %>% select(otu, log2FoldChange, padj),
by = "otu", suffix = c(".1", ".2")
)
cor.test(
data$log2FoldChange.1,
data$log2FoldChange.2,
method = "spearman"
) # -0.16408 p = 0.003748
data %>%
summarize(con.t = sum(log2FoldChange.1*log2FoldChange.2 < 0)) # 163
data %>%
mutate(
sig.1 = (padj.1 <= 0.1)*1,
sig.2 = (padj.2 <= 0.1)*1,
group = sig.1 + sig.2
) %>%
ggplot(aes(
x = log2FoldChange.1, y = log2FoldChange.2,
color = as.character(sig.1),
shape = as.character(sig.2),
size = as.character(group)
)) +
geom_point() +
geom_abline(intercept = 0, slope = -1, lty = 2) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
labs(
title = NULL,
x = "non-lesional vs\nlesional at baseline",
y = "baseline vs week-\n12 in lesional skin"
) +
coord_cartesian(xlim = c(-10, 10), ylim = c(-10, 10)) +
scale_shape_manual(breaks = c("0", "1"), values = c(16, 17)) +
scale_color_manual(breaks = c("0", "1"), values = c("grey", "black")) +
scale_size_manual(breaks = c("0", "1", "2"), values = c(2, 3, 5)) +
theme_classic() +
theme(
legend.position = "none",
axis.title = element_text(size = 18, face = "bold", family = "Times"),
axis.text = element_text(size = 13, face = "bold", family = "Times")
)
ggsave("manuscript figures/scatter.logfc_deseq2_genus_placebo.tiff", width = 4, height = 4, dpi = 500)