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errorbar-bar-circle.R
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#久生
library(readxl)
library(tidyverse)
library(Hmisc)
dd <- read_excel('/Users/congliu/Work/GEOCHIP.xlsx', sheet = 2)
dim(dd)
dd[!complete.cases(dd),]
s1 <- group_by(dd, Subcategory1) %>% summarise(n())
t <- filter(dd, Subcategory1=='Aromatics')
cal_sd <- function(variables) {
df <- variables
df[is.na(df)] <- 0
rf <- df[8:14]
md <- df[15:21]
for (i in seq(dim(rf)[1])) {
rf1 <- rf[i,]
md1 <- md[i,]
hehe <- t.test(as.numeric(rf1), as.numeric(md1))
print(hehe)
}
}
cal_sd(t)
for (i in s1[,1]) {
t <- filter(dd, Subcategory1==i)
cal_sd(t)
}
#################
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# 计算长度
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# 以 groupvars 为组,计算每组的长度,均值,以及标准差
# ddply 就是 dplyr 中的 group_by + summarise
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# 重命名
datac <- plyr::rename(datac, c("mean" = measurevar))
# 计算标准偏差
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
# 计算置信区间
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
###画图，黑白柱状图，加bar
df <- read_tsv('df.tsv')
df <- select(df, -X1)
chao <- select(df, c('Sample','chao1','Type'))
shannon <- select(df, c('Sample','shannon','Type'))
simpson <- select(df, c('Sample','simpson','Type'))
aa <- summarySE(df, measurevar=c("chao1"), groupvars=c("Type"))
aa$Type <- factor(aa$Type)
simpson <- ggplot(data = aa, aes(x=Type, y=simpson)) + geom_bar(stat = "identity",position=position_dodge(), fill=c('white', 'gray', 'black'), color='black') +
geom_errorbar(aes(ymin=simpson-se, ymax=simpson+se), position=position_dodge(0.9), width=.2) +
xlab('simpson')
shannon <- ggplot(data = aa, aes(x=Type, y=shannon)) + geom_bar(stat = "identity",position=position_dodge(), fill=c('white', 'gray', 'black'), color='black') +
geom_errorbar(aes(ymin=shannon-se, ymax=shannon+se), position=position_dodge(0.9), width=.2) +
xlab('shannon')
chao_p <- ggplot(data = aa, aes(x=Type, y=chao1)) + geom_bar(stat = "identity",position=position_dodge(), fill=c('white', 'gray', 'black'), color='black') +
geom_errorbar(aes(ymin=chao1-se, ymax=chao1+se), position=position_dodge(0.9), width=.2) +
xlab('chao1')
summarySE(chao_p, shannon, simpson, axis=2)
pp <- function(arg){
aa <- summarySE(df, measurevar=c(arg), groupvars=c("Type"))
aa$Type <- factor(aa$Type)
ggplot(data = aa, aes(x=Type, y=arg)) + geom_bar(stat = "identity",position=position_dodge(), fill=c('white', 'gray', 'black'), color='black') +
geom_errorbar(aes(ymin=arg-se, ymax=arg+se), position=position_dodge(0.9), width=.2) +
xlab(arg)
}
pp('')
##丰度图
zryh <- read_delim('/Users/congliu/prs/fenxi/zryh.tsv', delim = '\t')
zryh <- select(zryh, -c('X1','Description'))
p <- reshape2::melt(zryh, variable.name="Sample")
ggplot(data = p, aes(x=Sample, y=value, fill=Type)) + geom_bar(stat = "identity",position = "dodge")+
ylab('Relative Abundance') + coord_flip() + theme_bw()+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank()) + xlab('')
#PLDA图
library(tidyverse)
plda <- read_delim('/Users/congliu/prs/fenxi/meta/pls-da_result.txt', delim = '\t', col_names = F)
pld <- plda[c('X2','X3','X4')]
ggplot(data = pld) + geom_point(aes(x=pld$X3, y=pld$X4, color = pld$X2, shape=pld$X2)) +
stat_ellipse(aes(x=pld$X3, y=pld$X4, color = pld$X2), type = "t") +
theme(legend.position = 'right', legend.title = element_text(''))+
xlab('COMPONENT 1') +
ylab('COMPONENT 2') +
theme_bw() +
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank()) +
scale_shape_manual(values=c(21, 22, 23, 24)) + labs(color='') + labs(shape='')
#scale_color_manual()
library(ggord)
library(yyplot)
#OR值图
#Odds Ratio = (A / B)/(C / D) = A D/ B C
#95% CI of ln(OR) = ln(OR)±1.96(1/A + 1/B + 1/C + 1/D)0.5
#95% CI of OR = e95% CI of ln(OR)
or <- read_delim('/Users/congliu/prs/fenxi/meta/diff/H_CIA__vs__H_MTX_meta_diff.txt', delim = '\t', col_names = T)
odds <- or %>% select(OTU, oddsRatio, lower, upper, pvalues, `+samples in group 0`, `+samples in group 1`)
#odds <- or %>% select(OTU, oddsRatio)
odds_f <- odds %>% filter(oddsRatio != Inf, oddsRatio != 0, pvalues <= 0.05)
odds_f <- odds_f %>% arrange(desc(`+samples in group 0`), desc(`+samples in group 1`))
odds_f <- odds_f %>% filter(OTU %in% c('s__Bacteroides_finegoldi','f__Bacteroidaceae','s__Enterococcus_faecalis',
's__Lactobacillus_kitasatonis','s__Lactococcus_garvieae','f__Streptococcaceae',
'o__Lactobacillales','s__Clostridium_isatidis','s__Paeniclostridium_sordelli',
'f__Helicobacteraceae', 'o__Campylobacterales'))
odds_l <- tidyr::gather(odds_f, num, value, -OTU)
odds_f <- slice(odds_f, 1:10)
ggplot(data = odds_f) +
geom_bar(aes(y = oddsRatio, x = OTU), stat = "identity",position = "dodge") +
xlab('Species') + ylab('or_value')
geom_errorbar(aes(ymin=lower, ymax=upper, x = OTU), position=position_dodge(0.9), width=.2)
##丰度图
paths = list.files('/Users/congliu/prs/fenxi/huetu')
setwd('/Users/congliu/prs/fenxi/huetu')
foo <- function(path){
name <- str_split(basename(path), '[.]')[[1]][1]
zryh <- read_delim(path, delim = '\t')
p <- select(zryh, -c('X1'))
p <- reshape2::melt(p, variable.name="Sample")
ggplot(data = p, aes(x=Sample, y=value, fill=group)) + geom_bar(stat = "identity",position = "dodge")+
ylab('Relative Abundance') + coord_flip() + theme_bw()+
theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank()) + xlab('')
#ggsave(pp, filename = paste('/Users/congliu/prs/fenxi/lefse/tupian/',name, '.pdf', sep = ''), width=8, height=8)
#pdf(paste('/Users/congliu/prs/fenxi/huetu/',name, '.pdf', sep = ''))
}
for (i in paths) {
foo(i)
}
foo("lefse_input_H_CIA_H_Control.txt")
foo("lefse_input_H_CIA_H_Control_H_MTX_H_HUMSCs.txt")
foo("lefse_input_H_CIA_H_MTX.txt")
foo("lefse_input_H_CIA_M_CIA.txt")
foo("lefse_input_H_HUMSCs_H_CIA.txt")
foo("lefse_input_H_HUMSCs_H_CIA_H_Control_H_MTX.txt")
foo("lefse_input_H_HUMSCs_H_Control.txt")
foo("lefse_input_H_HUMSCs_H_MTX.txt")
foo("lefse_input_H_HUMSCs_M_HUMSCs.txt")
foo("lefse_input_H_MTX_H_Control.txt")
foo("lefse_input_H_MTX_M_MTX.txt")
foo("lefse_input_M_Control_H_Control.txt")
foo("lefse_input_M_Control_M_CIA.txt")
foo("lefse_input_M_Control_M_HUMSCs.txt")
foo("lefse_input_M_Control_M_MTX.txt")
foo("lefse_input_M_HUMSCs_M_CIA.txt")
foo("lefse_input_M_HUMSCs_M_MTX.txt")
foo("lefse_input_M_MTX_M_CIA.txt")
foo("lefse_input_M_MTX_M_Control_M_HUMSCs_M_CIA.txt")
foo("lefse_input_H_CIA_H_Control_H_MTX_H_HUMSCs.txt")
foo("lefse_input_H_HUMSCs_H_CIA.txt")
foo("lefse_input_M_HUMSCs_M_CIA.txt")