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statistical_analysis.R
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statistical_analysis.R
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###################
# This is official R script to post with manuscript
rm(list=ls())
########## Load library ############
library(ape)
library(phyloseq)
library(ggplot2)
library(vegan)
library(vegan)
library(ggplot2)
library(reshape2)
library(corrplot)
library(DESeq2)
########## Load function ############
pairwise.adonis <- function(x,factors, sim.function = 'vegdist', sim.method = 'bray', p.adjust.m ='bonferroni')
{
co = combn(unique(as.character(factors)),2)
pairs = c()
F.Model =c()
R2 = c()
p.value = c()
for(elem in 1:ncol(co)){
if(sim.function == 'daisy'){
library(cluster); x1 = daisy(x[factors %in% c(co[1,elem],co[2,elem]),],metric=sim.method)
} else{x1 = vegdist(x[factors %in% c(co[1,elem],co[2,elem]),],method=sim.method)}
ad = adonis(x1 ~ factors[factors %in% c(co[1,elem],co[2,elem])] , permutations=9999);
pairs = c(pairs,paste(co[1,elem],'vs',co[2,elem]));
F.Model =c(F.Model,ad$aov.tab[1,4]);
R2 = c(R2,ad$aov.tab[1,5]);
p.value = c(p.value,ad$aov.tab[1,6])
}
p.adjusted = p.adjust(p.value,method=p.adjust.m)
sig = c(rep('',length(p.adjusted)))
sig[p.adjusted <= 0.05] <-'.'
sig[p.adjusted <= 0.01] <-'*'
sig[p.adjusted <= 0.001] <-'**'
sig[p.adjusted <= 0.0001] <-'***'
pairw.res = data.frame(pairs,F.Model,R2,p.value,p.adjusted,sig)
print("Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1")
return(pairw.res)
}
###################
# Read 16S rRNA sequencing data make Phyloseq object
###################
biom_file = "~/Box Sync/2017/7July/cobs/otu_table_mc2_w_tax.json.biom"
biom_otu_tax <- import_biom(biom_file)
tree = read.tree("~/Box Sync/2017/7July/cobs/rep_set.tre")
meta <- read.csv("~/Box Sync/2017/7July/cobs/meta.csv",header=T, row.names=1)
sampledata = sample_data(meta)
physeq = merge_phyloseq(sampledata, biom_otu_tax, tree)
only_july <- prune_samples(as.data.frame(sample_data(physeq))$month_sample == "July", physeq )
remove_corn_m <- prune_samples(!(as.data.frame(sample_data(only_july))$crop_system == "corn" & as.data.frame(sample_data(only_july))$agg_frac == "MM"), only_july)
remove_corn_s <- prune_samples(!(as.data.frame(sample_data(remove_corn_m))$crop_system == "corn" & as.data.frame(sample_data(remove_corn_m))$agg_frac == "SM"),remove_corn_m)
remove_p_m <- prune_samples(!(as.data.frame(sample_data(remove_corn_s ))$crop_system == "prairie" & as.data.frame(sample_data(remove_corn_s ))$agg_frac == "MM"),remove_corn_s )
remove_p_s <- prune_samples(!(as.data.frame(sample_data(remove_p_m))$crop_system == "prairie" & as.data.frame(sample_data(remove_p_m))$agg_frac == "SM"),remove_p_m)
only44sample <- remove_p_s
set.seed(1)
#rare <- rarefy_even_depth(only44sample)
#saveRDS(rare, "~/Box Sync/2018/2February/cobs/cobs_16s_44sample_rarefied.rds")
rare <- readRDS("~/Box Sync/2018/2February/cobs/cobs_16s_44sample_rarefied.rds")
# # this is all 120 files
# #rare <- readRDS("~/Box Sync/2017/7July/cobs/cobs_16s_rarefied.rds")
# glom <- tax_glom(rare, "Rank2")
# plot_bar(glom)
# sample_sums(glom)#13956
# #1081815 "k__Bacteria" "p__Actinobacteria" NA
# mean(otu_table(glom)[c("1081815"),]) / 13956
# taxa_sums(glom)
###################
# Supplementary figure 1
###################
rm(list=ls())
rare <- readRDS("~/Box Sync/2018/2February/cobs/cobs_16s_44sample_rarefied.rds")
#NMDS
data.selected <- t(otu_table(rare))
mds.all=metaMDS(data.selected,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$crop <- as.factor(sample_data(rare)$crop_system)
data.sm$for_label <- factor(data.sm$crop, labels= c("C", "P", "FP"))
pdf("~/Box Sync/2018/2February/cobs/writing/supplementary_figure1.pdf")
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=for_label)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.11, 0.9)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))+stat_ellipse()+labs(color="Crop System")
dev.off()
pairwise.adonis(data.selected, as.factor(sample_data(rare)$crop_system))
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 prairie vs corn 3.976695 0.15308704 0.0004 0.0012 *
# 2 prairie vs prairieFert 1.996555 0.06239905 0.0173 0.0519
# 3 corn vs prairieFert 5.525448 0.15553492 0.0001 0.0003 **
# #only WS
# only_ws <- prune_samples( as.data.frame(sample_data(rare))$agg_frac == "WS", rare)
# data.selected <- t(otu_table(only_ws))
# mds.all=metaMDS(data.selected,distance="bray", k=2)
# data.sm=as.data.frame(scores(mds.all))
# data.sm$crop <- as.factor(sample_data(only_ws)$crop_system)
# ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=crop)) + geom_point(size=5) +
# geom_hline(yintercept=0.0, colour="grey", lty=2)+
# geom_vline(xintercept=0.0, colour="grey",lty=2) +
# theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
# theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
# pairwise.adonis(data.selected, as.factor(sample_data(only_ws)$crop_system))
# #only ws
# # [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# # pairs F.Model R2 p.value p.adjusted sig
# # 1 prairie vs corn 1.7470238 0.2255090 0.047 0.141
# # 2 prairie vs prairieFert 0.9227838 0.1332966 0.415 1.000
# # 3 corn vs prairieFert 1.9232605 0.2427360 0.064 0.192 ''
###################
# Supplementary figure 3A -> Supplementary Figure 5
###################
rm(list=ls())
rare <- readRDS("~/Box Sync/2018/2February/cobs/cobs_16s_44sample_rarefied.rds")
#only PF compare agg
only_pf <- prune_samples( as.data.frame(sample_data(rare))$crop_system == "prairieFert", rare)
data.selected <- t(otu_table(only_pf))
mds.all=metaMDS(data.selected,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$agg <- as.factor(sample_data(only_pf)$agg_frac)
data.sm$for_label <- factor(data.sm$agg, labels= c("LM","Micro","MM","SM","WS"))
pdf("~/Box Sync/2018/2February/cobs/writing/supplementary_figure5.pdf")
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=for_label)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.9, 0.22)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))+labs(color="Aggregates")
dev.off()
pairwise.adonis(data.selected, as.factor(sample_data(only_pf)$agg))
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 WS vs SM 0.7705684 0.1138115 0.7681 1
# 2 WS vs LM 0.7415927 0.1100026 0.6889 1
# 3 WS vs MM 0.6975571 0.1041510 0.9168 1
# 4 WS vs micro 0.9351090 0.1348370 0.5095 1
# 5 SM vs LM 1.1312410 0.1586317 0.2848 1
# 6 SM vs MM 0.9426523 0.1357770 0.5141 1
# 7 SM vs micro 0.7842446 0.1155979 0.7992 1
# 8 LM vs MM 0.8649800 0.1259989 0.6296 1
# 9 LM vs micro 1.3383675 0.1823795 0.1400 1
# 10 MM vs micro 1.1421073 0.1599118 0.3406 1
# #all compare agg
# data.selected <- t(otu_table(rare))
# mds.all=metaMDS(data.selected,distance="bray", k=2)
# data.sm=as.data.frame(scores(mds.all))
# data.sm$agg <- as.factor(sample_data(rare)$agg_frac)
# ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=agg)) + geom_point(size=5) +
# geom_hline(yintercept=0.0, colour="grey", lty=2)+
# geom_vline(xintercept=0.0, colour="grey",lty=2) +
# theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
# theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
# pairwise.adonis(data.selected, as.factor(sample_data(rare)$agg))
# # [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# # pairs F.Model R2 p.value p.adjusted sig
# # 1 micro vs WS 1.1533115 0.04981195 0.208 1.00
# # 2 micro vs LM 2.2920896 0.09435539 0.011 0.11
# # 3 micro vs SM 1.0423117 0.06929199 0.309 1.00
# # 4 micro vs MM 1.1609427 0.07657457 0.230 1.00
# # 5 WS vs LM 0.9230130 0.04026578 0.434 1.00
# # 6 WS vs SM 1.1664998 0.07691292 0.215 1.00
# # 7 WS vs MM 0.9156073 0.06138586 0.420 1.00
# # 8 LM vs SM 1.8013795 0.11400141 0.042 0.42
# # 9 LM vs MM 1.4393119 0.09322384 0.104 1.00
# # 10 SM vs MM 0.9485838 0.13651469 0.525 1.00
###################
# 2. genomic potential, 1) no different in crop system
###################
### MNDS for all kegg enzyme
data.selected=read.csv("input_all.csv",header=T)
head(data.selected)
dim(data.selected)
sub_data <- subset(data.selected, Sample != "H5")
dim(sub_data)
data.selected <- sub_data
## NMDS
mds.all=metaMDS(data.selected[,2:2726],distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$Aggregates=as.factor(data.selected[,2727])
data.sm
levels(data.sm$Aggregates)
data.sm$Aggregates <- factor(data.sm$Aggregates, levels=c("Whole Soil", "Large", "Medium", "Small", "Micro"))
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=Aggregates)) + geom_point(size=5) + xlim(-0.12, 0.12) + ylim(-0.07, 0.07)+
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
## MRPP & ANOSIM
trt=as.factor(data.selected[,2727])
enzymes=as.matrix(data.selected[,2:2726])
mrpp(enzymes, group=trt, distance="bray")
anosim(enzymes, grouping=trt, permutations=999, distance="bray")
###################
# Supplementary figure 2
###################
###################
# read count table all pro+euk
kegg_all <- as.data.frame(read.table("~/Box Sync/2018/1January/cobs/merged_kegg_cobs.tsv", header =T, row.names=1))
table_kegg_all <- t(kegg_all)
table <- table_kegg_all
###################
# Read meta
meta=read.csv("~/Box Sync/2017/10October/cobs/sample_data_cobs.csv")
##############################
# remove poor quality, add R1 and R2
new_meta = data.frame()
fin_table = data.frame()
#uni = unique(meta$filename)
i=1
for ( one_sample in unique(meta$filename) ){
#one_sample = uni[i]
one_pair = paste0(gsub("_001","",one_sample),"_R1_001")
if (meta[ meta$filename_pair == one_pair, ]$Note == "Poor Quality") next
pair_to_remove = paste0(gsub("_001","",one_sample),"_R2_001")
fin_table = rbind(fin_table, one_sample = rowSums( table[, c(one_pair, pair_to_remove)] ) )
rownames(fin_table)[i] = toString(one_sample )
new_meta = rbind(new_meta, meta[ meta$filename_pair == one_pair, ])
i = i + 1
}
colnames(fin_table) <- rownames(table)
dim(new_meta)
dim(fin_table)
full_table <- merge(fin_table, new_meta, by.x = "row.names", by.y = "filename")
dim(full_table)
# sub_table <- subset(full_table, Crop == "PF")
# sub_table <- full_table
# Y = t(sub_table[, 2:(ncol(fin_table)+1)])
# colnames(Y) <- sub_table$FileName.prefix
# GRP = sub_table[,(ncol(fin_table)+2):ncol(sub_table)]
# data.selected <- t(Y)
# dim(data.selected)
# #sub_data <- subset(data.selected, rownames(data.selected) != "H5")
# #sub_meta <- subset(GRP, FileName.prefix != "H5")
# dim(sub_data)
# #data.selected <- sub_data
# mds.all=metaMDS(data.selected,distance="bray", k=2)
# data.sm=as.data.frame(scores(mds.all))
# data.sm$Aggregates = as.factor(sub_meta$SoilFrac)
# data.sm$Crop = as.factor(GRP$Crop)
# ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=Crop)) + geom_point(size=5) + xlim(-0.12, 0.12) + ylim(-0.07, 0.07)+
# geom_hline(yintercept=0.0, colour="grey", lty=2)+
# geom_vline(xintercept=0.0, colour="grey",lty=2) +
# theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
# theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
# pairwise.adonis(data.selected, as.factor(GRP$Crop))
# interest <- c("ec.3.2.1.4", "ec.3.2.1.21","ec.3.2.1.86","ec.3.2.1.91","ec.2.4.1.20","ec.2.7.1.205")
# carbon <- data.selected[,interest]
# data.selected <- carbon
# mds.all=metaMDS(data.selected,distance="bray", k=2)
# data.sm=as.data.frame(scores(mds.all))
# data.sm$Crop = as.factor(GRP$Crop)
#normalize?
#sub_table <- subset(full_table, Crop == "PF")
#sub_table <- subset(full_table, SoilFrac == "WS")
sub_table <- full_table
Y = t(sub_table[, 2:(ncol(fin_table)+1)])
colnames(Y) <- sub_table$FileName.prefix
GRP = sub_table[,(ncol(fin_table)+2):ncol(sub_table)]
## Create a DESeqDataSet object for holding the counts and phenotype data.
#deseq2_deseqds <- DESeqDataSetFromMatrix(countData = Y, colData = GRP, design = ~ SoilFrac)
deseq2_deseqds <- DESeqDataSetFromMatrix(countData = Y, colData = GRP, design = ~ Crop)
## Differential expression analysis.
deseq2_de <- DESeq(deseq2_deseqds)
# significantly different between medium and whole sooil
#deseq2_de_out <- results(deseq2_de, contrast=c("SoilFrac","MM","WS"))
count <- counts(deseq2_de, normalized=TRUE)
data.selected <- t(count)
sub_data <- subset(data.selected, rownames(data.selected) != "H5")
sub_meta <- subset(GRP, FileName.prefix != "H5")
data.selected <- sub_data
GRP <- sub_meta
mds.all=metaMDS(data.selected,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$Crop = as.factor(GRP$Crop)
data.sm$for_label <- factor(data.sm$Crop, labels= c("C", "P", "FP"))
pdf("~/Box Sync/2018/2February/cobs/writing/supplementary_figure2.pdf")
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=for_label)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.11, 0.9)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))+labs(color="Crop System")
dev.off()
#+stat_ellipse()
pairwise.adonis(data.selected, as.factor(GRP$Crop))
#only whole soil, compare crop
# pairs F.Model R2 p.value p.adjusted sig
# 1 CC vs P 2.148548 0.2636725 0.061 0.183
# 2 CC vs PF 3.942315 0.3965188 0.031 0.093
# 3 P vs PF 2.257778 0.2734123 0.066 0.198
#all
#with H5 <- this is what i use
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 PF vs CC 4.5064336 0.13059691 0.0007 0.0021 *
# 2 PF vs P 0.9813639 0.03167594 0.4004 1.0000
# 3 CC vs P 2.9264497 0.11740339 0.0098 0.0294 .
#without H3
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 PF vs CC 4.8254460 0.1426573 0.004 0.012 .
# 2 PF vs P 0.9687456 0.0323252 0.404 1.000
# 3 CC vs P 2.9264497 0.1174034 0.012 0.036 .
#only PF compare agg
mds.all=metaMDS(data.selected,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$agg = as.factor(GRP$SoilFrac)
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=agg)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
#+stat_ellipse()
pairwise.adonis(data.selected, as.factor(GRP$SoilFrac))
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 MM vs Micro 9.8345371 0.6210814 0.028 0.28
# 2 MM vs SM 6.6725171 0.5716434 0.024 0.24
# 3 MM vs LM 9.9772689 0.6244665 0.034 0.34
# 4 MM vs WS 10.7699642 0.6422175 0.028 0.28
# 5 Micro vs SM 0.6476039 0.1146688 0.745 1.00
# 6 Micro vs LM 2.6351489 0.3051654 0.039 0.39
# 7 Micro vs WS 1.0469403 0.1485666 0.439 1.00
# 8 SM vs LM 1.6638300 0.2496807 0.180 1.00
# 9 SM vs WS 0.6489030 0.1148724 0.686 1.00
# 10 LM vs WS 1.1869516 0.1651537 0.361 1.00
#only carbon
interest <- c("ec.3.2.1.4", "ec.3.2.1.21","ec.3.2.1.86","ec.3.2.1.91","ec.2.4.1.20","ec.2.7.1.205")
carbon <- data.selected[,interest]
data.selected <- carbon
sub_data <- subset(data.selected, rownames(data.selected) != "H5")
sub_meta <- subset(GRP, FileName.prefix != "H5")
data.selected <- sub_data
GRP <- sub_meta
mds.all=metaMDS(data.selected,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$frac = as.factor(GRP$SoilFrac)
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=frac)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
pairwise.adonis(data.selected, as.factor(GRP$SoilFrac))
#PF only carbon only
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 MM vs Micro 76.37241153 0.92716008 0.038 0.38
# 2 MM vs SM 99.20194262 0.95201625 0.025 0.25
# 3 MM vs LM 19.19044091 0.76181441 0.018 0.18
# 4 MM vs WS 52.78186879 0.89792771 0.029 0.29
# 5 Micro vs SM 0.08884346 0.01745848 0.851 1.00
# 6 Micro vs LM 0.19981845 0.03222973 0.866 1.00
# 7 Micro vs WS 0.60484347 0.09157575 0.500 1.00
# 8 SM vs LM 0.32771802 0.06151189 0.656 1.00
# 9 SM vs WS 0.63369463 0.11248296 0.478 1.00
# 10 LM vs WS 0.15414995 0.02504813 0.777 1.00
#carbon function not diff in crop
for_plot <- data.frame(count = carbon[,"ec.3.2.1.4"], name = rownames(carbon))
ggplot(for_plot, aes(x=name, y=count))+geom_boxplot()
# compare agg only PF
#normalize?
sub_table <- subset(full_table, Crop == "PF")
sub_table <- full_table
Y = t(sub_table[, 2:(ncol(fin_table)+1)])
colnames(Y) <- sub_table$FileName.prefix
GRP = sub_table[,(ncol(fin_table)+2):ncol(sub_table)]
library(DESeq2)
library(ggplot2)
## Create a DESeqDataSet object for holding the counts and phenotype data.
deseq2_deseqds <- DESeqDataSetFromMatrix(countData = Y, colData = GRP, design = ~ SoilFrac)
## Differential expression analysis.
deseq2_de <- DESeq(deseq2_deseqds)
# significantly different between medium and whole sooil
deseq2_de_out <- results(deseq2_de, contrast=c("SoilFrac","MM","WS"))
count <- counts(deseq2_de, normalized=TRUE)
data.selected <- t(count)
GRP$Crop == "PF"
sub_data <- subset(data.selected, GRP$Crop == "PF")
sub_data <- subset(sub_data, rownames(sub_data) != "H5")
sub_meta <- subset(GRP, Crop == "PF")
sub_meta <- subset(sub_meta , FileName.prefix != "H5")
dim(GRP)
dim(count)
mds.all=metaMDS(sub_data,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$Aggregates = as.factor(sub_meta$SoilFrac)
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=Aggregates)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.08, 0.82)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))
pairwise.adonis(sub_data, as.factor(sub_meta$SoilFrac))
#no diff among agg
[# 1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 MM vs Micro 9.7708479 0.6195512 0.020 0.20
# 2 MM vs SM 6.6608214 0.5712137 0.028 0.28
# 3 MM vs LM 9.8461874 0.6213600 0.034 0.34
# 4 MM vs WS 10.6630512 0.6399219 0.040 0.40
# 5 Micro vs SM 0.6406335 0.1135747 0.745 1.00
# 6 Micro vs LM 2.5611280 0.2991578 0.035 0.35
# 7 Micro vs WS 1.0515111 0.1491186 0.399 1.00
# 8 SM vs LM 1.5758623 0.2396435 0.201 1.00
# 9 SM vs WS 0.6299204 0.1118880 0.722 1.00
# 10 LM vs WS 1.1370738 0.1593193 0.411 1.00
#only carbon
interest <- c("ec.3.2.1.4", "ec.3.2.1.21","ec.3.2.1.86","ec.3.2.1.91","ec.2.4.1.20")
carbon <- sub_data[,interest]
mds.all=metaMDS(carbon,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$Aggregates = as.factor(sub_meta$SoilFrac)
pairwise.adonis(sub_data, as.factor(sub_meta$SoilFrac))
#no diff among agg in carbon
#########################################################
# Supplementary figure 3B -> Supplementary Figure 6
#########################################################
###################
# read count table all pro+euk
kegg_all <- as.data.frame(read.table("~/Box Sync/2018/1January/cobs/merged_kegg_cobs.tsv", header =T, row.names=1))
table_kegg_all <- t(kegg_all)
table <- table_kegg_all
###################
# Read meta
meta=read.csv("~/Box Sync/2017/10October/cobs/sample_data_cobs.csv")
##############################
# remove poor quality, add R1 and R2
new_meta = data.frame()
fin_table = data.frame()
#uni = unique(meta$filename)
i=1
for ( one_sample in unique(meta$filename) ){
#one_sample = uni[i]
one_pair = paste0(gsub("_001","",one_sample),"_R1_001")
if (meta[ meta$filename_pair == one_pair, ]$Note == "Poor Quality") next
pair_to_remove = paste0(gsub("_001","",one_sample),"_R2_001")
fin_table = rbind(fin_table, one_sample = rowSums( table[, c(one_pair, pair_to_remove)] ) )
rownames(fin_table)[i] = toString(one_sample )
new_meta = rbind(new_meta, meta[ meta$filename_pair == one_pair, ])
i = i + 1
}
colnames(fin_table) <- rownames(table)
dim(new_meta)
dim(fin_table)
full_table <- merge(fin_table, new_meta, by.x = "row.names", by.y = "filename")
dim(full_table)
##############################
# Deseq2
sub_table <- subset(full_table, Crop == "PF")
Y = t(sub_table[, 2:(ncol(fin_table)+1)])
colnames(Y) <- sub_table$FileName.prefix
GRP = sub_table[,(ncol(fin_table)+2):ncol(sub_table)]
data.selected <- t(Y)
dim(data.selected)
sub_data <- subset(data.selected, rownames(data.selected) != "H5")
sub_meta <- subset(GRP, FileName.prefix != "H5")
dim(sub_data)
data.selected <- sub_data
mds.all=metaMDS(data.selected,distance="bray", k=2)
data.sm=as.data.frame(scores(mds.all))
data.sm$Aggregates = as.factor(sub_meta$SoilFrac)
data.sm$for_label <- factor(data.sm$Aggregates, labels= c("LM", "Micro","MM","SM","WS"))
pdf("~/Box Sync/2018/2February/cobs/writing/supplementary_figure6.pdf")
ggplot(data=data.sm, aes(x=NMDS1, y=NMDS2, color=for_label)) + geom_point(size=5) +
geom_hline(yintercept=0.0, colour="grey", lty=2)+
geom_vline(xintercept=0.0, colour="grey",lty=2) +
theme_bw() + scale_color_brewer(palette="Set1") + theme(legend.position = c(0.1, 0.85)) +
theme(legend.background = element_rect(fill="white", size=0.3, linetype="solid", colour="black"))+labs(color="Aggregates")
dev.off()
## MRPP & ANOSIM
trt=as.factor(sub_meta$SoilFrac)
enzymes=as.matrix(data.selected)
mrpp(enzymes, group=trt, distance="bray")
anosim(enzymes, grouping=trt, permutations=999, distance="bray")
pairwise.adonis(data.selected, sub_meta$SoilFrac)
# [1] "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
# pairs F.Model R2 p.value p.adjusted sig
# 1 MM vs Micro 2.79395960 0.31771349 0.101 1.00
# 2 MM vs SM 5.65194747 0.53060227 0.039 0.39
# 3 MM vs LM 0.94036450 0.13549209 0.451 1.00
# 4 MM vs WS 0.40820371 0.06370018 0.687 1.00
# 5 Micro vs SM 0.07160578 0.01411896 0.916 1.00
# 6 Micro vs LM 0.49284761 0.07590623 0.909 1.00
# 7 Micro vs WS 1.08128636 0.15269632 0.350 1.00
# 8 SM vs LM 0.62422001 0.11098784 0.770 1.00
# 9 SM vs WS 1.47985899 0.22837827 0.310 1.00
# 10 LM vs WS 0.16316631 0.02647443 0.714 1.00
#pairwise.adonis(data.selected, GRP$SoilFrac)
###################
# 3. Figure 1: genomic potential of cellulose decomposition enzymes in FP among agg.
###################
#Figure 1C
###################
# read count table all pro+euk
kegg_all <- as.data.frame(read.table("~/Box Sync/2018/1January/cobs/merged_kegg_cobs.tsv", header =T, row.names=1))
table_kegg_all <- t(kegg_all)
table <- table_kegg_all
###################
# Read meta
meta=read.csv("~/Box Sync/2017/10October/cobs/sample_data_cobs.csv")
##############################
# remove poor quality, add R1 and R2
new_meta = data.frame()
fin_table = data.frame()
#uni = unique(meta$filename)
i=1
for ( one_sample in unique(meta$filename) ){
#one_sample = uni[i]
one_pair = paste0(gsub("_001","",one_sample),"_R1_001")
if (meta[ meta$filename_pair == one_pair, ]$Note == "Poor Quality") next
pair_to_remove = paste0(gsub("_001","",one_sample),"_R2_001")
fin_table = rbind(fin_table, one_sample = rowSums( table[, c(one_pair, pair_to_remove)] ) )
rownames(fin_table)[i] = toString(one_sample )
new_meta = rbind(new_meta, meta[ meta$filename_pair == one_pair, ])
i = i + 1
}
colnames(fin_table) <- rownames(table)
dim(new_meta)
dim(fin_table)
full_table <- merge(fin_table, new_meta, by.x = "row.names", by.y = "filename")
dim(full_table)
##############################
# Deseq2
sub_table <- subset(full_table, Crop == "PF")
Y = t(sub_table[, 2:(ncol(fin_table)+1)])
colnames(Y) <- sub_table$FileName.prefix
GRP = sub_table[,(ncol(fin_table)+2):ncol(sub_table)]
library(DESeq2)
library(ggplot2)
## Create a DESeqDataSet object for holding the counts and phenotype data.
deseq2_deseqds <- DESeqDataSetFromMatrix(countData = Y, colData = GRP, design = ~ SoilFrac)
## Differential expression analysis.
deseq2_de <- DESeq(deseq2_deseqds)
# significantly different between medium and whole sooil
deseq2_de_out <- results(deseq2_de, contrast=c("SoilFrac","MM","WS"))
count <- counts(deseq2_de, normalized=TRUE)
####################
# cellulose decomposition enzymes
# Figure 1B
file_name = "~/Box Sync/2018/2February/cobs/writing/figure1C.pdf"
pdf(file_name, width=6, height=9)
one_ec = "ec.3.2.1.4"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
d$for_label <- factor(d$SoilFrac, labels = c("LM", "Micro","MM","SM","WS"))
p1 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 3.2.1.4")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 4 6549154 1637289 21.42 4.64e-06 ***
# Residuals 15 1146379 76425
one_ec = "ec.3.2.1.91"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
d$for_label <- factor(d$SoilFrac, labels = c("LM", "Micro","MM","SM","WS"))
p2 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 3.2.1.91")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 4 309191 77298 34.83 1.98e-07 ***
# Residuals 15 33286 2219
one_ec = "ec.3.2.1.21"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
d$for_label <- factor(d$SoilFrac, labels = c("LM", "Micro","MM","SM","WS"))
p3 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 3.2.1.21")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 4 87601920 21900480 9.811 0.000418 ***
# Residuals 15 33483966 2232264
one_ec = "ec.2.4.1.20"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
d$for_label <- factor(d$SoilFrac, labels = c("LM", "Micro","MM","SM","WS"))
p4 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 2.4.1.20")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 4 225011 56253 12.85 9.7e-05 ***
# Residuals 15 65645 4376
one_ec = "ec.3.2.1.86"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
d$for_label <- factor(d$SoilFrac, labels = c("LM", "Micro","MM","SM","WS"))
p5 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 3.2.1.86")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 4 1199699 299925 5.679 0.00547 **
# Residuals 15 792160 52811
# one_ec = "ec.2.7.1.205"
# d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
# p6 <- ggplot(d, aes(x=SoilFrac, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 2.7.1.205")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
# for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
# fit <- aov(count ~ frac, data=for_aov)
# summary(fit)
# # Df Sum Sq Mean Sq F value Pr(>F)
# # frac 4 3.0 0.750 0.079 0.988
# # Residuals 15 142.3 9.484
library("gridExtra")
grid.arrange(p1,p2,p3,p4,p5, nrow=3)
dev.off()
#########################################################
### figure 1C -> 1B
##############################
###################
# read count table all pro+euk
kegg_all <- as.data.frame(read.table("~/Box Sync/2018/1January/cobs/merged_kegg_cobs.tsv", header =T, row.names=1))
table_kegg_all <- t(kegg_all)
table <- table_kegg_all
###################
# Read meta
meta=read.csv("~/Box Sync/2017/10October/cobs/sample_data_cobs.csv")
##############################
# remove poor quality, add R1 and R2
new_meta = data.frame()
fin_table = data.frame()
#uni = unique(meta$filename)
i=1
for ( one_sample in unique(meta$filename) ){
#one_sample = uni[i]
one_pair = paste0(gsub("_001","",one_sample),"_R1_001")
if (meta[ meta$filename_pair == one_pair, ]$Note == "Poor Quality") next
pair_to_remove = paste0(gsub("_001","",one_sample),"_R2_001")
fin_table = rbind(fin_table, one_sample = rowSums( table[, c(one_pair, pair_to_remove)] ) )
rownames(fin_table)[i] = toString(one_sample )
new_meta = rbind(new_meta, meta[ meta$filename_pair == one_pair, ])
i = i + 1
}
colnames(fin_table) <- rownames(table)
dim(new_meta)
dim(fin_table)
full_table <- merge(fin_table, new_meta, by.x = "row.names", by.y = "filename")
dim(full_table)
# Deseq2
sub_table <- full_table
#sub_table <- subset(full_table, SoilFrac == "WS")
Y = t(sub_table[, 2:(ncol(fin_table)+1)])
colnames(Y) <- sub_table$FileName.prefix
GRP = sub_table[,(ncol(fin_table)+2):ncol(sub_table)]
library(DESeq2)
library(ggplot2)
## Create a DESeqDataSet object for holding the counts and phenotype data.
deseq2_deseqds <- DESeqDataSetFromMatrix(countData = Y, colData = GRP, design = ~ Crop)
## Differential expression analysis.
deseq2_de <- DESeq(deseq2_deseqds)
# significantly different between medium and whole sooil
#deseq2_de_out <- results(deseq2_de, contrast=c("SoilFrac","MM","WS"))
count <- counts(deseq2_de, normalized=TRUE)
####################
# cellulose decomposition enzymes
# Figure 1C -> 1B
file_name = "~/Box Sync/2018/2February/cobs/writing/figure1B.pdf"
pdf(file_name, width=6, height=9)
one_ec = "ec.3.2.1.4"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "Crop", returnData=T)
d$for_label <- factor(d$Crop, labels = c("C", "P","FP"))
p1 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("C", "P","FP"))+ggtitle("EC 3.2.1.4")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Crop System",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$Crop)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
#WS
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 89895 44947 2.272 0.159
# Residuals 9 178012 19779
#all soil
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 646703 323351 1.101 0.342
# Residuals 41 12038627 293625
one_ec = "ec.3.2.1.91"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "Crop", returnData=T)
d$for_label <- factor(d$Crop, labels = c("C", "P","FP"))
p2 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("C", "P","FP"))+ggtitle("EC 3.2.1.91")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Crop System",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$Crop)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
#WS
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 732 366.0 0.388 0.689
# Residuals 9 8495 943.9
#all soil
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 22186 11093 1.008 0.374
# Residuals 41 451397 11010
one_ec = "ec.3.2.1.21"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "Crop", returnData=T)
d$for_label <- factor(d$Crop, labels = c("C", "P","FP"))
p3 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("C", "P","FP"))+ggtitle("EC 3.2.1.21")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Crop System",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$Crop)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# WS
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 3832901 1916451 2.131 0.175
# Residuals 9 8092311 899146
#all soil
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 10682075 5341037 1.048 0.36
# Residuals 41 208943430 5096181
one_ec = "ec.2.4.1.20"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "Crop", returnData=T)
d$for_label <- factor(d$Crop, labels = c("C", "P","FP"))
p4 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("C", "P","FP"))+ggtitle("EC 2.4.1.20")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Crop System",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$Crop)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# WS
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 6706 3353 0.658 0.541
# Residuals 9 45885 5098
#all soil
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 60364 30182 2.836 0.0702 .
# Residuals 41 436373 10643
one_ec = "ec.3.2.1.86"
d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "Crop", returnData=T)
d$for_label <- factor(d$Crop, labels = c("C", "P","FP"))
p5 <- ggplot(d, aes(x=for_label, y=count))+geom_boxplot()+scale_x_discrete(limits=c("C", "P","FP"))+ggtitle("EC 3.2.1.86")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Crop System",y="Normalized Count")
for_aov <- data.frame(count = count[one_ec,], frac =GRP$Crop)
fit <- aov(count ~ frac, data=for_aov)
summary(fit)
# WS
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 28711 14356 0.639 0.55
# Residuals 9 202131 22459
#all soil
# Df Sum Sq Mean Sq F value Pr(>F)
# frac 2 136637 68318 0.99 0.38
# Residuals 41 2828435 68986
# one_ec = "ec.2.7.1.205"
# d <- plotCounts(deseq2_de, gene = one_ec, intgroup = "SoilFrac", returnData=T)
# p6 <- ggplot(d, aes(x=SoilFrac, y=count))+geom_boxplot()+scale_x_discrete(limits=c("WS", "LM","MM","SM","Micro"))+ggtitle("EC 2.7.1.205")+theme(axis.line=element_line(size=1.25), aspect.ratio=1, panel.border=element_blank(),axis.ticks=element_line(size=1.25, colour="black"), panel.background=element_blank(), axis.text=element_text(size=10, face="bold", colour="black"), axis.title=element_text(size=12, face="bold", colour="black"), plot.title = element_text(size=15, face="bold", hjust=0.5))+labs(x="Aggregates Fraction",y="Normalized Count")
# for_aov <- data.frame(count = count[one_ec,], frac =GRP$SoilFrac)
# fit <- aov(count ~ frac, data=for_aov)
# summary(fit)
# # Df Sum Sq Mean Sq F value Pr(>F)
# # frac 4 3.0 0.750 0.079 0.988
# # Residuals 15 142.3 9.484
library("gridExtra")
grid.arrange(p1,p2,p3,p4,p5, nrow=3)
dev.off()
#########################################################
# Supplementary figure 4. Heatmap -> Supplemetary Figure 7
#########################################################
#heat map
# NMDS??
# start from here
deseq2_de_out <- results(deseq2_de, contrast=c("SoilFrac","MM","WS"))
summary(deseq2_de_out)
# out of 3546 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up) : 375, 11%
# LFC < 0 (down) : 342, 9.6%
deseq2_de_out_sig <- subset(deseq2_de_out, padj < 0.01)
summary(deseq2_de_out_sig)