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metabolomics_met_methods_revised.Rmd
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metabolomics_met_methods_revised.Rmd
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---
title: "metabolomics analysis for mSphere, Carey et al. 2018"
author: "Maureen A Carey"
date: "updated March, 2018"
output: html_document
---
1 Set working directory and run helper function (to load libraries and functions) (warnings off here for package loading)
```{r "setup", include=FALSE, warning=F, message=F}
# Updated to R version 3.4.3 from 3.2.4 (2016-03-10) on Dec 15th 2017
source("/Users/maureencarey/local_documents/work/metabolomics_methods/code/helper_functions_met_methods.R",local = F)
opts_knit$set(root.dir = "/Users/maureencarey/local_documents/work/metabolomics_methods/figures")
```
2 Open datafile
```{r}
raw_file = read.csv("/Users/maureencarey/local_documents/work/metabolomics_methods/data/met_methods_raw_data.txt",
sep = "\t", header = T, colClasses = c(rep(NA, 14), rep('numeric',35)),
na.strings=c('',"NA"), stringsAsFactors = F)
fig1D = raw_file[1:375,-c(1,5:29)] # select specified samples and remove empty last row
raw_file_comp1 = raw_file[1:375,-c(1,3:29)] # select specified samples, and remove extra metabolite identifiers and last empty row
print(raw_file_comp1[raw_file_comp1$BIOCHEMICAL == 'kynurenine',])
print(raw_file_comp1[raw_file_comp1$BIOCHEMICAL == 'HEPES',])
print(raw_file_comp1[raw_file_comp1$BIOCHEMICAL == 'phenol red',])
```
3 Count mets detected and generate Fig 1D
``` {r}
raw = raw_file_comp1
print('number of metabolites in all samples')
dim(na.omit(raw)) #in all
print('number of metabolites in each sample')
# for (i in 2:ncol(raw)) { print(length(na.omit(raw[,i]))) }
rm(raw)
m = melt(fig1D, id.vars = c("BIOCHEMICAL", "SUPER_PATHWAY", "SUB_PATHWAY"))
m2 = mutate(m, val2 = ifelse(!is.na(value),1,0)) # Is metabolite measured in sample, yes/no
change_pathways = as.data.frame(rbind(c('Xenobiotics','Other'),
c('Amino Acid','AA/Peptides'),
c('Peptide','AA/Peptides'),
c('Cofactors and Vitamins','Cofactors')))
for (i in 1:nrow(change_pathways)) {
search_term = as.character(change_pathways$V1[i])
replace_term = as.character(change_pathways$V2[i])
m2$SUPER_PATHWAY = ifelse(m2$SUPER_PATHWAY == search_term,replace_term,m2$SUPER_PATHWAY)}
m4 = m2 %>%
mutate(variable2 = paste(substr(as.character(variable), 1, 4), substr(variable, 7, nchar(as.character(variable))), sep='')) %>%
group_by(BIOCHEMICAL, SUPER_PATHWAY, variable2) %>%
summarise(samples = sum(val2)) %>% arrange(samples) %>%
mutate(detected = ifelse(samples == 0, NA, 'detected')) %>% na.omit() %>%
mutate(drug = ifelse(substr(variable2, 4,6)== '_mi','untreated','with DHA'),
clone = substr(variable2, 1,3),
sens_res = ifelse(clone == 'BAT','Resistant (MRA-1240)','Sensitive (MRA-1238)'))
m5 = m2 %>%
mutate(variable2 = paste(substr(as.character(variable), 1, 4), substr(variable, 7, nchar(as.character(variable))), sep='')) %>%
group_by(BIOCHEMICAL, SUPER_PATHWAY) %>%
summarise(samples = sum(val2)) %>% arrange(samples) %>%
mutate(detected = ifelse(samples == 0, NA, 'detected')) %>% na.omit() %>%
mutate(group = 'all samples')
p1 = ggplot(data = m5, aes(x = SUPER_PATHWAY, y = samples, fill = SUPER_PATHWAY)) +
geom_boxplot() +
theme(axis.title = element_blank(),
axis.ticks = element_blank(),
strip.text.x = element_text(size = 7, family = "Helvetica"),
panel.grid.major = element_blank(),
axis.text.y = element_text(size = 7, family = "Helvetica"),
#strip.background = element_blank(),
legend.text = element_text(size = 6, family = "Helvetica"),
plot.margin = ggplot2::margin(1,1,1,1,"mm"),
axis.text.x = element_blank()) +
guides(fill = FALSE) + facet_wrap(~group)
p2 = ggplot(data = m4, aes(x = SUPER_PATHWAY, y = samples, color = SUPER_PATHWAY)) +
geom_jitter(size = .25) + facet_grid(sens_res~drug) +
theme(axis.title = element_blank(),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
axis.text.y = element_text(size = 7, family = "Helvetica"),
axis.text.x = element_text(size = 7, angle = -45, vjust = 1, hjust = 0, family = "Helvetica"),
legend.title = element_blank(),
plot.margin = ggplot2::margin(1,1,1,1,"mm"),
strip.text = element_text(size = 7, margin = ggplot2::margin(0.1,0.1,0.1,0.1,"mm"), family = "Helvetica"),
#strip.background = element_blank(),
legend.text = element_text(size = 6, family = "Helvetica"),
legend.position="bottom") + guides(color = F)
# grid.newpage()
# lay <- rbind(c(1,1,1,2,2,2,2,2,2),
# c(1,1,1,2,2,2,2,2,2))
# plot_final = grid.arrange(p1,p2,ncol = 2, nrow = 1, layout_matrix = lay)#, widths = 2:2)
# ggsave('dec122017_fig1D.pdf',plot_final, height = 3, width = 3.5, units = "in")
ggsave('feb2018_fig1C.pdf',p2, height = 2.8, width = 3.5, units = "in")
# devSVG(file = "dec122017_fig1D.svg", width = 3.5, height = 3,xmlHeader = T)
# plot(plot_final)
# dev.off()
```
``` {r}
rm(m, m2,m4, m5)
raw1 = prep_raw(raw_file_comp1)
identifiers = prep_sample_identifiers(raw1)
melted = melt(raw1); melted_na = na.omit(melted); tab = table(melted_na$variable)
print(tab)
measured_mets = as.data.frame(matrix(NA,nrow = 20, ncol = 1)); i = 1
for (x in tab) {
measured_mets[i,1] = x
rownames(measured_mets)[i] = rownames(tab)[i]
i = i+1 }
df_for_cor = identifiers[c(3,5,6,9),]; colnames(df_for_cor) = identifiers[3,]
df1 = as.data.frame(t(df_for_cor)); df2 = as.data.frame(tab); rownames(df2) = df2$Var1
df_for_cor = merge(df1, df2, by = "row.names")
df_for_cor$CLIENT_IDENTIFIER = NULL; df_for_cor$Var1 = NULL
rownames(df_for_cor) = df_for_cor$Row.names; df_for_cor = df_for_cor[,-c(1)]
indx = sapply(df_for_cor, is.factor)
df_for_cor[indx] = lapply(df_for_cor[indx], function(x) as.character(x))
indx = sapply(df_for_cor, is.character)
df_for_cor[indx] = lapply(df_for_cor[indx], function(x) as.numeric(x))
cor_df = cor(df_for_cor)
print(cor_df)
c = cor.test(df_for_cor$Freq, df_for_cor$DSDNA_ASSAY, alternative = c("two.sided"), method = c("pearson"))
print('p value for correlation between # mets detected and DNA')
print(c$p.value)
print('correlation between # mets detected and DNA')
print(c$estimate)
rm(raw1,df_for_cor,melted,melted_na,identifiers,df1,df2,cor_df)
```
4 Plot parasite/DNA/Protein for Fig 2C
```{r}
raw1 = prep_raw(raw_file_comp1)
identifiers = prep_sample_identifiers(raw1)
# process sample data for plotting
df = identifiers[c(3,5,6,8,9),] # keep only protein, dna, parasite measures and IDS
df = as.data.frame(t(df)) # transpose
rownames(df) = df$CLIENT_IDENTIFIER; df$CLIENT_IDENTIFIER = NULL
indx = sapply(df, is.factor); df[indx] = lapply(df[indx], function(x) as.character(x))
df$CLIENT_PROTEIN = as.numeric(df$CLIENT_PROTEIN); df$DSDNA_ASSAY = as.numeric(df$DSDNA_ASSAY)
df$TOTAL_PARASITES = as.numeric(df$TOTAL_PARASITES)
df = merge(df, measured_mets, by = 'row.names'); rownames(df) = df$Row.names; df$Row.names = NULL
colnames(df)[5] = 'measured_mets'
df = mutate(df,
treated = ifelse(grepl('_UE',df$GROUP_ID),'untreated','treated'),
group = ifelse(grepl('Res1',df$GROUP_ID),'group 1','group2'))
df = mutate(df,
measured_mets_scaled = measured_mets/mean(measured_mets),
CLIENT_PROTEIN_scaled = CLIENT_PROTEIN/mean(CLIENT_PROTEIN),
TOTAL_PARASITES_scaled = TOTAL_PARASITES/mean(TOTAL_PARASITES))
corr_eqn_r <- function(x,y, digits = 4) { corr_coef = round(cor(x, y), digits = digits)
paste("italic(r) == ", corr_coef) }
corr_eqn_p <- function(x,y, digits = 4) { p_value = round(cor.test(x,y)$p.value, digits = digits)
paste("italic(pvalue) == ", format(p_value, scientific=FALSE)) }
# plot parasite #, dna amount, and protein amount NOTE LOG10 AXIS
summary = ggplot(df, aes(x = TOTAL_PARASITES, y = DSDNA_ASSAY)) +
custom_theme_pca + theme(plot.margin = unit(c(1,1,1,1),"mm"),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(family = "Helvetica")) +
geom_smooth(colour = "black", fill = "gray", method = "lm") +
geom_point(size=2, aes(color = CLIENT_PROTEIN)) +
scale_colour_gradient("Protein \nAmount", low="lightgrey", high="black",
breaks = c(50,110,161), limits = c(50,161),
labels=c("60","","160")) +
scale_shape(NULL) +
scale_y_log10() + scale_x_log10() +
xlab("Parasite number") + ylab("dsDNA quantification")
summary
s = summary + guides(shape = F, color = F)
# ggsave('fig_1c_sample_characteristics_dec2017.pdf', s, width = 1.75, height = 3, units = "in")
df_test = df[,c(1,2,4,5)]; colnames(df_test) = c('protein','dsDNA','parasite count','metabolites')
df_test2 = melt(df_test, id.vars = c('dsDNA'))
plot_final = ggplot(df_test2, aes(x = dsDNA, y = value, color = variable)) + geom_point(size =2) +
scale_color_manual(values = c("black","darkblue","darkred")) +
facet_wrap(~variable, scales = "free_y") +
geom_smooth(fill = "gray", method = "lm") +
custom_theme_pca + theme(plot.margin = unit(c(1,1,1,1),"mm"),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title = element_text(family = "Helvetica"),
panel.spacing.x = unit(.1, "lines")) +
xlab("dsDNA") + ylab(NULL) + guides(color = F)
ggsave('march2018_fig2B.pdf',plot_final, height = 3, width = 4, units = "in", useDingbats = F)
# devSVG(file = "fig_1c_sample_characteristics_dec2017.svg", width = 1.75, height = 3, xmlHeader = T)
# s
# dev.off()
rm(raw1,identifiers,df,indx,s,summary,tab)
```
5 Check to see if median and SD scale (Bias requires log transformation)
```{r}
raw1 = prep_raw(raw_file_comp1)
identifiers = prep_sample_identifiers(raw1)
raw2 = remove_NAs(raw1)
raw3 = as.matrix(imputing_missing(raw2,0))
# scale to median (so all metabolite abundances hover around 1)
df = as.data.frame(raw3); df$mets = rownames(df)
df = mutate(df, med = apply(df[,-c(21)],1,median), stand_dev = apply(df[,-c(21)],1,sd))
df2 = df[,c(21:23)] # keep just median and SD
df2= df2[order(df2$med,df2$stand_dev),]
p1 = ggplot(data = df2, aes(x = med, y = stand_dev)) + geom_point() +
scale_x_log10() + scale_y_log10() + geom_smooth(method = 'loess') +
xlab('Median') + ylab('SD') +
theme(axis.text = element_blank(), axis.ticks = element_blank()) +
ggtitle("Median & Variance scale (common bias in Mass Spec)")
# repeat with logged data
df = as.data.frame(log2(raw3));df$mets = rownames(df)
df = mutate(df, log_med = apply(df[,-c(21)],1,median), log_stand_dev = apply(df[,-c(21)],1,sd))
df2 = df[,c(21:23)] # keep just median and SD
df2= df2[order((df2$log_med),(df2$log_stand_dev)),]
p2 = ggplot(data = df2, aes(x = log_med, y = log_stand_dev)) + geom_point() +
scale_x_log10() + scale_y_log10() + geom_smooth(method = 'loess') +
xlab('Median') + ylab('SD') + theme(axis.text = element_blank(), axis.ticks = element_blank(), axis.title = element_text(family = "Helvetica")) +
ggtitle("Bias removed: Median & Variance no longer scale\n note: log scale data from now on")
grid.newpage()
lay <- rbind(c(1,1),
c(2,2))
plot_final = grid.arrange(p1,p2,ncol = 1, nrow = 2, layout_matrix = lay)#, widths = 2:2)
ggsave('feb3018_suppl1.pdf',plot_final, height = 6, width = 6.875, units = "in")
# devSVG(file = "dec122017_suppl1.svg", width = 6.875, height = 6,xmlHeader = T)
# plot(plot_final)
# dev.off()
rm(df,df2, raw1, raw2, raw3,identifiers)
```
Notes to self: Check Aug1 file for history of changes
Aug 9 has correlation plots and exploration of median v mean centering, removal of outliers
Oct drops troph stage samples
6 Preprocess data
```{r}
raw1 = prep_raw(raw_file_comp1) # adjust formating
identifiers = prep_sample_identifiers(raw1) # extract relevant samples
raw_remove_NAs = remove_NAs(raw1) # check no nondetected metabolites due to subset
no_missing_data = imputing_missing(raw_remove_NAs, 0) #imput missing values
labels_clone = c("S1","S1","S1","S1","S1",
"S1","S1","S1","S1","S1",
"S2","S2","S2","S2","S2",
"S2","S2","S2","S2","S2")
labels_drug = c("S1","S1","S1","S1","S1",
"S2","S2","S2","S2","S2",
"S1","S1","S1","S1","S1",
"S2","S2","S2","S2","S2")
# Sample normalization
norm_dna = normalize(no_missing_data,identifiers,"dna") # DNA NORMALIZATION
norm_pro = normalize(no_missing_data,identifiers,"protein") # PROTEIN NORMALIZATION
norm_para = normalize(no_missing_data,identifiers,"para") # PARASITE NUMBER NORMALIZATION
# Feature normalization (log, center, scale)
# log
log_nonorm = log2(no_missing_data)
log_dna = log2(norm_dna)
log_pro = log2(norm_pro)
log_para = log2(norm_para)
# prep format for PCA
data_transposed_unnorm = t(log_nonorm)
data_transposed_dna = t(log_dna)
data_transposed_pro = t(log_pro)
data_transposed_para = t(log_para)
# [median] center & [sd] scale
data_for_pca_unnorm = median_center_scale_data(data_transposed_unnorm)
data_for_pca_dna = median_center_scale_data(data_transposed_dna)
data_for_pca_pro = median_center_scale_data(data_transposed_pro)
data_for_pca_para = median_center_scale_data(data_transposed_para)
rm(raw1,identifiers,raw_remove_NAs,no_missing_data,data_transposed_unnorm,data_transposed_dna,data_transposed_para,data_transposed_pro,log_pro,log_para,log_dna,log_nonorm,norm_para,norm_pro,norm_dna,raw_file_comp1)
```
7 Plot HEPES and Phenol Red boxplots
```{r}
list_o_data = list(data_for_pca_unnorm,data_for_pca_dna,data_for_pca_pro,data_for_pca_para)
for (x in 1:4) {
data = as.data.frame(list_o_data[[x]])
if (x == 1) { title = 'Unnormalized'}
else if (x == 2) {title = 'Normalized to DNA'}
else if (x == 3) {title = 'Normalized to Protein'}
else { title = 'Normalized to Parasite Number'}
p = data %>%
mutate(group = ifelse(grepl('PUR',rownames(data)),'Sensitive','Resistant'),
treated = ifelse(grepl('plus',rownames(data)),'treated','untreated')) %>%
group_by(group, treated) %>%
summarize(mean=mean(HEPES), sd=sd(HEPES)) %>%
mutate( grouping = paste(group,treated)) %>%
ggplot(aes(x = as.factor(grouping), color = treated)) +
geom_boxplot(aes(
lower = mean - sd,
upper = mean + sd,
middle = mean,
ymin = mean - 3*sd,
ymax = mean + 3*sd),
stat = "identity") + #ggtitle(paste('HEPES abundance, ',title)) +
ggtitle('HEPES') +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(family = "Helvetica"),
axis.text.x = element_blank(),
axis.text.y = element_text(size = 6),
axis.ticks = element_blank(),
strip.text = element_text(size = 6, family = "Helvetica"),
title = element_text(size = 7, family = "Helvetica"),
panel.grid.minor = element_blank()) + guides(color = FALSE) +
facet_wrap(~group, scales = "free_x") + scale_y_continuous(limits = c(-6, 6))
#print(p)
if (x == 1) {p1 = p}}
list_o_data = list(data_for_pca_unnorm,data_for_pca_dna,data_for_pca_pro,data_for_pca_para)
for (x in 1:4) {
data = as.data.frame(list_o_data[[x]])
if (x == 1) { title = 'Unnormalized'}
else if (x == 2) {title = 'Normalized to DNA'}
else if (x == 3) {title = 'Normalized to Protein'}
else { title = 'Normalized to Parasite Number'}
p = data %>%
mutate(group = ifelse(grepl('PUR',rownames(data)),'Sensitive','Resistant'),
treated = ifelse(grepl('plus',rownames(data)),'treated','untreated')) %>%
group_by(group, treated) %>%
summarize(mean=mean(phenolred), sd=sd(phenolred)) %>%
mutate( grouping = paste(group,treated)) %>%
ggplot(aes(x = as.factor(grouping), color = treated)) +
geom_boxplot(aes(
lower = mean - sd,
upper = mean + sd,
middle = mean,
ymin = mean - 3*sd,
ymax = mean + 3*sd),
stat = "identity") +
ggtitle('Phenol Red') +
#ggtitle(paste('Phenol Red abundance, ',title)) +
theme(axis.title.x = element_blank(),
axis.title.y = element_text(family = "Helvetica"),
axis.text.x = element_blank(),
axis.ticks = element_blank(),
strip.text = element_text(size = 6),
title = element_text(size = 7,family = "Helvetica"),
axis.text.y = element_text(size = 6,family = "Helvetica"),
panel.grid.minor = element_blank()) + guides(color = FALSE)+
facet_wrap(~group, scales = "free_x") + scale_y_continuous(limits = c(-6, 6))
#print(p)
if (x == 1) {p2 = p}}
grid.newpage()
lay <- rbind(c(1,2),
c(1,2))
plot_final = grid.arrange(p1,p2,ncol = 2, nrow = 1, layout_matrix = lay)#, widths = 2:2)
ggsave('feb2018_suppl2.pdf',plot_final, width = 2.237, height = 2, units = "in")
# devSVG(file = "dec122017_suppl2.svg", width = 2.237, height = 2, xmlHeader = T)
# plot(plot_final)
# dev.off()
rm(p,x,title,data)
```
8 Plot heatmap of correlation to HEPES and Phenol Red
```{r}
# HEPES
cor_mat = c(); cor_mat_count = c()
for (x in 1:4) {
data = as.data.frame(list_o_data[[x]])
cor_mat_temp = as.data.frame(matrix(NA, nrow = 0, ncol = ncol(data)))
for (y in 1:ncol(data)) {
cor_data = cor.test(data$HEPES,data[,y], alternative = c("two.sided"), method = c("pearson"))
cor_r = cor_data$estimate
cor_p = cor_data$p.value
colnames(cor_mat_temp)[y] = colnames(data)[y]
cor_mat_temp[1,y] = cor_r
cor_mat_temp[2,y] = cor_p }
cor_mat_temp[2,] = p.adjust(cor_mat_temp[2,], method = "fdr")
for (i in 1:ncol(cor_mat_temp)) {
if (cor_mat_temp[2,i] > 0.05) {
cor_mat_temp[2,i] = NA; cor_mat_temp[1,i] = NA } # if significant p value
else if (abs(cor_mat_temp[1,i]) < 0.5) {
cor_mat_temp[2,i] = NA; cor_mat_temp[1,i] = NA } } # if notable correlation coefficient
cor_mat[[x]] = cor_mat_temp
cor_mat_count[[x]] = cor_mat_temp[colSums(!is.na(cor_mat_temp)) > 0] }
rm(x,y,cor_p,cor_r,cor_mat_temp,cor_data)
for (x in 1:4) {
# prep labels
if (x == 1) {
title = 'Unnormalized'; title2 = 'unnorm_'
} else if (x == 2) {
title = 'Normalized to DNA'
title2 = 'dna_norm_'
} else if (x == 3) {
title = 'Normalized to Protein'
title2 = 'pro_norm_'
} else {
title = 'Normalized to Parasite Number'
title2 = 'para_norm_'
}
print(paste(title, '- mets correlated to HEPES'))
print(dim(cor_mat_count[[x]])[2])}
heatmap_matrix = as.data.frame(matrix(NA, nrow = 0, ncol = ncol(cor_mat[[2]])))
for (x in 1:4) {
if (x == 1) {
title = ' Unnormalized'
} else if (x == 2) {
title = 'DNA'
} else if (x == 3) {
title = 'Protein'
} else {
title = 'Parasite Number' }
df = as.data.frame(cor_mat[[x]][1,])
heatmap_matrix = rbind(heatmap_matrix,df)
rownames(heatmap_matrix)[x] = title
}
df = heatmap_matrix %>% mutate(norm_approach = rownames(heatmap_matrix)) %>% melt(id.vars = c("norm_approach"))
mets_to_correct_prep = df[df$norm_approach == ' Unnormalized',]
mets_to_correct_hepes = unique(mets_to_correct_prep[(mets_to_correct_prep$value) > 0.5,2])
count_cor = df[!is.na(df$value),]; table(count_cor$norm_approach)
rm(i, mets_to_correct_prep,heatmap_matrix,data,count_cor,cor_mat,cor_mat_count,x,title,title2)
df$variable = sub('Isobar:fructose16-diphosphateglucose16-diphosphatemyo-inositol14or13-diphosphate','fructose/glucosePP or myoinositolPP',df$variable)
df$variable = sub("adenosine5\\'-monophosphate\\(AMP\\)",'AMP',df$variable)
df$variable = sub("cytidine5'-monophosphate\\(5\\'-CMP\\)","5'-CMP",df$variable)
df$variable = sub("uridine5\\'\\-diphosphate\\(UDP\\)",'UDP',df$variable)
df$variable = sub("palmitoylsphingomyelin\\(d18\\:1\\/16\\:0\\)",'palmitoyl sphingomyelin',df$variable)
df$variable = sub("N\\-palmitoyl\\-sphingosine\\(d18\\:1\\/16\\:0\\)",'palmitoyl sphingosine',df$variable)
df$variable = sub("lactosyl\\-N\\-palmitoyl\\-sphingosine",'lactosyl palmitoyl sphingosine',df$variable)
df$variable = sub("glutathioneoxidized\\(GSSG\\)",'glutathione, oxidized',df$variable)
df$variable = sub("glutathionereduced\\(GSH\\)",'glutathione, reduced',df$variable)
df$variable = sub("eicosapentaenoate\\(EPA\\;20\\:5n3\\)",'eicosapentaenoate',df$variable)
df$variable = sub("gamma-aminobutyrate\\(GABA\\)",'GABA',df$variable)
df$variable = sub("1-stearoyl-GPS\\(18\\:0\\)\\*","1-stearoyl-GPS",df$variable)
df$variable = sub("pyridoxine\\(VitaminB6\\)","pyridoxine",df$variable)
df$variable = sub("glycerol3-phosphate","glycerol 3-phosphate",df$variable)
df$variable = sub("phenolred","phenol red",df$variable)
df$variable = sub("aconitate\\[cisortrans\\]","aconitate",df$variable)
df$variable = sub("cytidinediphosphate","cytidine diphosphate",df$variable)
df$variable = sub("methioninesulfoxide","methionine sulfoxide",df$variable)
df$variable = sub("cysteine-glutathionedisulfide","cysteine glutathione disulfide",df$variable)
df$variable = sub("1-stearoyl-2-arachidonoyl-GPS\\(18:0\\/20:4\\)",'1-stearoyl-2-arachidonoyl-GPS',df$variable)
df$variable = sub("1-stearoyl-2-arachidonoyl-GPE\\(18:0/20:4\\)","1-stearoyl-2-arachidonoyl-GPE",df$variable)
df$variable = sub("1-arachidonoyl-GPE\\(20\\:4n6\\)\\*",'1-arachidonoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-stearoyl\\)-2-oleoyl-GPE\\(P-18\\:0\\/18\\:1\\)","1-(1-enyl-stearoyl)-2-oleoyl-GPE",df$variable)
df$variable = sub("1-\\(1-enyl-stearoyl\\)-2-arachidonoyl-GPE\\(P-18:0\\/20:4\\)\\*",'1-(1-enyl-stearoyl)-2-arachidonoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-palmitoyl\\)-2-arachidonoyl-GPE\\(P-16:0\\/20:4\\)\\*",'1-(1-enyl-palmitoyl)-2-arachidonoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-palmitoyl\\)-2-linoleoyl-GPE\\(P-16:0\\/18:2\\)\\*",'1-(1-enyl-palmitoyl)-2-linoleoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-palmitoyl\\)-2-oleoyl-GPE\\(P-16:0\\/18:1\\)\\*",'1-(1-enyl-palmitoyl)-2-oleoyl-GPE',df$variable)
unnorm_only = df[df$norm_approach == ' Unnormalized',]
unnorm_only = na.omit(unnorm_only); unnorm_only = unnorm_only[unnorm_only$value>0.5,]
unnorm_only = unnorm_only[unnorm_only$variable != "HEPES",]
unnorm_cluster = as.data.frame(unnorm_only[,c(2,3)]); rownames(unnorm_cluster) = unnorm_cluster$variable
order_mets = unnorm_cluster[order(unnorm_cluster$value),1]
df2 = subset(df, df$variable %in% unnorm_only$variable)
df2 = df2 %>% mutate(variable = factor(variable),
variable = factor(variable, levels = (order_mets)))
x = ggplot(df2) + geom_tile(aes(x = as.factor(variable), y = norm_approach, fill = value))+
scale_fill_gradientn(limits = c(0,1),
colours=c("navyblue", "blue", "red"),
breaks=c(0,0.9,1), labels=format(c(0,0.9,1))) + coord_flip() +
theme(axis.text.x = element_text(size = 6, angle = -45, hjust = 1, vjust = 1, family = "Helvetica"),
axis.text.y = element_text(size = 6,family = "Helvetica"),
axis.ticks = element_blank(),
axis.title = element_blank()) +
ggtitle(NULL) + guides(fill = FALSE) + scale_y_discrete(position = "top")
x
ggsave('fig_Xb_feb2018.pdf', x, width = 2.4, height = 7, units = "in")
# devSVG(file = "fig_Xb_dec2017.svg", width = 2.4, height = 7, xmlHeader = T)
# x
# dev.off()
rm(unnorm_only,df)
#Def need to filter because peaks are maintained from unnorm to everything else
# PHENOL RED
cor_mat = c(); cor_mat_count = c()
for (x in 1:4) {
data = as.data.frame(list_o_data[[x]])
cor_mat_temp = as.data.frame(matrix(NA, nrow = 0, ncol = ncol(data)))
for (y in 1:ncol(data)) {
cor_data = cor.test(data$phenolred,data[,y], alternative = c("two.sided"), method = c("pearson"))
cor_r = cor_data$estimate
cor_p = cor_data$p.value
colnames(cor_mat_temp)[y] = colnames(data)[y]
cor_mat_temp[1,y] = cor_r
cor_mat_temp[2,y] = cor_p }
cor_mat_temp[2,] = p.adjust(cor_mat_temp[2,], method = "fdr")
for (i in 1:ncol(cor_mat_temp)) {
if (cor_mat_temp[2,i] > 0.05) {
cor_mat_temp[2,i] = NA; cor_mat_temp[1,i] = NA }
else if (abs(cor_mat_temp[1,i]) < 0.5) {
cor_mat_temp[2,i] = NA; cor_mat_temp[1,i] = NA } }
cor_mat[[x]] = cor_mat_temp
cor_mat_count[[x]] = cor_mat_temp[colSums(!is.na(cor_mat_temp)) > 0] }
rm(x,y,cor_p,cor_r,cor_mat_temp,cor_data)
for (x in 1:4) {
# prep labels
if (x == 1) {
title = ' Unnormalized'; title2 = 'unnorm_'
} else if (x == 2) {
title = 'Normalized to DNA'
title2 = 'dna_norm_'
} else if (x == 3) {
title = 'Normalized to Protein'
title2 = 'pro_norm_'
} else {
title = 'Normalized to Parasite Number'
title2 = 'para_norm_'
}
print(paste(title, '- mets correlated to Phenol Red'))
print(dim(cor_mat_count[[x]])[2])}
heatmap_matrix = as.data.frame(matrix(NA, nrow = 0, ncol = ncol(cor_mat[[2]])))
for (x in 1:4) {
if (x == 1) {
title = ' Unnormalized'
} else if (x == 2) {
title = 'DNA'
} else if (x == 3) {
title = 'Protein'
} else {
title = 'ParasiteNumber' }
df = as.data.frame(cor_mat[[x]][1,])
heatmap_matrix = rbind(heatmap_matrix,df)
rownames(heatmap_matrix)[x] = title
}
df = heatmap_matrix %>% mutate(norm_approach = rownames(heatmap_matrix)) %>% melt(id.vars = c("norm_approach"))
mets_to_correct_prep_pr = df[df$norm_approach == ' Unnormalized',]
mets_to_correct_phenolred = unique(mets_to_correct_prep_pr[(mets_to_correct_prep_pr$value) > 0.5,2])
count_cor = df[!is.na(df$value),]; table(count_cor$norm_approach)
rm(i,heatmap_matrix,data,count_cor,cor_mat,cor_mat_count,x,title,title2)
df$variable = sub('Isobar:fructose16-diphosphateglucose16-diphosphatemyo-inositol14or13-diphosphate','fructose/glucosePP or myoinositolPP',df$variable)
df$variable = sub("adenosine5\\'-monophosphate\\(AMP\\)",'AMP',df$variable)
df$variable = sub("cytidine5'-monophosphate\\(5\\'-CMP\\)","5'-CMP",df$variable)
df$variable = sub("uridine5\\'\\-diphosphate\\(UDP\\)",'UDP',df$variable)
df$variable = sub("palmitoylsphingomyelin\\(d18\\:1\\/16\\:0\\)",'palmitoyl sphingomyelin',df$variable)
df$variable = sub("N\\-palmitoyl\\-sphingosine\\(d18\\:1\\/16\\:0\\)",'palmitoyl sphingosine',df$variable)
df$variable = sub("lactosyl\\-N\\-palmitoyl\\-sphingosine",'lactosyl palmitoyl sphingosine',df$variable)
df$variable = sub("glutathioneoxidized\\(GSSG\\)",'glutathione, oxidized',df$variable)
df$variable = sub("glutathionereduced\\(GSH\\)",'glutathione, reduced',df$variable)
df$variable = sub("eicosapentaenoate\\(EPA\\;20\\:5n3\\)",'eicosapentaenoate',df$variable)
df$variable = sub("gamma-aminobutyrate\\(GABA\\)",'GABA',df$variable)
df$variable = sub("1-stearoyl-GPS\\(18\\:0\\)\\*","1-stearoyl-GPS",df$variable)
df$variable = sub("pyridoxine\\(VitaminB6\\)","pyridoxine",df$variable)
df$variable = sub("glycerol3-phosphate","glycerol 3-phosphate",df$variable)
df$variable = sub("phenolred","phenol red",df$variable)
df$variable = sub("aconitate\\[cisortrans\\]","aconitate",df$variable)
df$variable = sub("cytidinediphosphate","cytidine diphosphate",df$variable)
df$variable = sub("methioninesulfoxide","methionine sulfoxide",df$variable)
df$variable = sub("cysteine-glutathionedisulfide","cysteine glutathione disulfide",df$variable)
df$variable = sub("1-stearoyl-2-arachidonoyl-GPS\\(18:0\\/20:4\\)",'1-stearoyl-2-arachidonoyl-GPS',df$variable)
df$variable = sub("1-stearoyl-2-arachidonoyl-GPE\\(18:0/20:4\\)","1-stearoyl-2-arachidonoyl-GPE",df$variable)
df$variable = sub("1-arachidonoyl-GPE\\(20\\:4n6\\)\\*",'1-arachidonoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-stearoyl\\)-2-oleoyl-GPE\\(P-18\\:0\\/18\\:1\\)","1-(1-enyl-stearoyl)-2-oleoyl-GPE",df$variable)
df$variable = sub("1-\\(1-enyl-stearoyl\\)-2-arachidonoyl-GPE\\(P-18:0\\/20:4\\)\\*",'1-(1-enyl-stearoyl)-2-arachidonoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-palmitoyl\\)-2-arachidonoyl-GPE\\(P-16:0\\/20:4\\)\\*",'1-(1-enyl-palmitoyl)-2-arachidonoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-palmitoyl\\)-2-linoleoyl-GPE\\(P-16:0\\/18:2\\)\\*",'1-(1-enyl-palmitoyl)-2-linoleoyl-GPE',df$variable)
df$variable = sub("1-\\(1-enyl-palmitoyl\\)-2-oleoyl-GPE\\(P-16:0\\/18:1\\)\\*",'1-(1-enyl-palmitoyl)-2-oleoyl-GPE',df$variable)
df$variable = sub("phosphoenolpyruvate\\(PEP\\)",'phosphoenolpyruvate',df$variable)
df$variable = sub("adenosine5\\'-diphosphoribose\\(ADP-ribose\\)",'ADP-ribose',df$variable)
df$variable = sub("uridine5\\'-monophosphate\\(UMP\\)",'UMP',df$variable)
df$variable = sub("S-adenosylhomocysteine\\(SAH\\)",'S-adenosylhomocysteine',df$variable)
df$variable = sub("inosine5\\'-monophosphate\\(IMP\\)",'IMP',df$variable)
unnorm_only = df[df$norm_approach == ' Unnormalized',]; unnorm_only = na.omit(unnorm_only); unnorm_only = unnorm_only[unnorm_only$value>0.5,];
unnorm_only = unnorm_only[unnorm_only$variable != "phenol red",]
unnorm_cluster = as.data.frame(unnorm_only[,c(2,3)]); rownames(unnorm_cluster) = unnorm_cluster$variable
order_mets = unnorm_cluster[order(unnorm_cluster$value),1]
df2 = subset(df, df$variable %in% unnorm_only$variable)
df2 = df2 %>% mutate(variable = factor(variable),
variable = factor(variable, levels = (order_mets)))
x = ggplot(df2) + geom_tile(aes(x = as.factor(variable), y = norm_approach, fill = value))+
scale_fill_gradientn(limits = c(0,1),
colours=c("navyblue", "blue", "red"),
breaks=c(0,0.9,1), labels=format(c(0,0.9,1))) + coord_flip() +
theme(axis.text.x = element_text(size = 6, angle = -45, hjust = 1, vjust = 1,family = "Helvetica"),
axis.text.y = element_text(size = 6,family = "Helvetica"),
axis.ticks = element_blank(),
axis.title = element_blank()) +
ggtitle(NULL) + guides(fill = FALSE) + scale_y_discrete(position = "top")
x
ggsave('fig_2e_feb2018.pdf', x, width = 2.4, height = 7, units = "in")
# devSVG(file = "fig_2e_dec2017.svg", width = 2.4, height = 7, xmlHeader = T)
# x
# dev.off()
rm(unnorm_only,df, mets_to_correct_prep_pr)
```
10 Princple Component Analysis without NORMALIZATION
```{r}
pca_data <- prcomp(data_for_pca_unnorm, center = F, scale. = F)
summary(pca_data)
data_plot = data.frame(pca_data$x[,c(1,2)]) # Adjust for plotting
metadata = as.data.frame(rownames(pca_data$x)) %>%
mutate(sample = rownames(pca_data$x), treatment = ifelse(grepl('minus',sample),"untreated","treated"),
clone = ifelse(grepl('BAT',sample),"resistant","sensitive")); metadata[,1] = NULL
t = adonis(data_plot ~ treatment*clone, data = metadata, method = "eu") # PerMANOVA
print(t)
unscaled = qplot(x = PC1, y = PC2, data = data_plot, shape = factor(labels_clone),
color = factor(labels_drug),
size=I(2), show.legend = FALSE, alpha = .9) +
custom_theme_pca + scale_shape_manual(values = c(16,17,18,9)) +
theme(plot.margin = unit(c(1,1,1,0),"mm"), title = element_text(family = "Helvetica"),
axis.text = element_text(family = "Helvetica")) + # top, right, bottom, and left
ggtitle("No normalization,\ntreatment p-value = NS\nclone p-value = NS\nboth p-value = NS") + ylab(paste("PC2",get_2nd(pca_data),sep = " ")) + xlab(paste("PC1",get_1st(pca_data),sep = " "))
ggsave('fig_2d_no_norm_jan2018.pdf', unscaled, width = 2.237, height = 2, units = "in")
# devSVG(file = "fig_2d_no_norm_dec2017.svg", width = 2.237, height = 2, xmlHeader = T)
# unscaled
# dev.off()
rotation_a = pca_data$rotation #loadings
# PC 1 loadings (most important variable in PC1 in full dataset without normalization)
t = c(); t$PC1_no_norm = names(tail(sort(abs(rotation_a[,1])),10))
# PC 2 loadings (most important variable in PC2 in full dataset without normalization)
t$PC2_no_norm = names(tail(sort(abs(rotation_a[,2])),10))
rm(pca_data,data_plot,rotation_a,x)
```
11 Now explore normalization methods
```{r}
# PCA for DNA NORMALIZATION
pca_dna <- prcomp(data_for_pca_dna, center = F, scale. = F)
summary(pca_dna)
data_plot = data.frame(pca_dna$x[,c(1,2)])
t1 = adonis(data_plot ~ treatment*clone, data = metadata, method = "eu") # PerMANOVA
t1
dna = qplot(x = PC1, y = PC2, data = data_plot, shape = factor(labels_clone),
color = factor(labels_drug), size=I(2), show.legend = FALSE, alpha = .9) +
custom_theme_pca + scale_shape_manual(values = c(16,17,18,9)) +
theme(plot.margin = unit(c(1,3,1,1),"mm"), title = element_text(family = "Helvetica"),
axis.text = element_text(family = "Helvetica")) + # top, right, bottom, and left
ggtitle("Normalization: DNA,\ntreatment p-value < 0.05\nclone p-value < 0.05\nboth p-value = NS") + ylab(paste("PC2",get_2nd(pca_dna),sep = " ")) + xlab(paste("PC1",get_1st(pca_dna),sep = " "))
ggsave('fig_2d_dna_norm_jan2018.pdf', dna,width = 2.237, height = 2, units = "in")
# devSVG(file = "fig_2d_dna_norm_dec2017.svg", width = 2.237, height = 2, xmlHeader = T)
# dna
# dev.off()
rotation_a = pca_dna$rotation #loadings
# PC 1 loadings (most important variable in PC1 in full dataset without normalization)
t$PC1_dna = names(tail(sort(abs(rotation_a[,1])),10))
# PC 2 loadings (most important variable in PC2 in full dataset without normalization)
t$PC2_dna = names(tail(sort(abs(rotation_a[,2])),10))
rm(data_plot,pca_dna, rotation_a)
# Princple Component Analysis for PARASITE NUMBER NORMALIZATION
pca_para <- prcomp(data_for_pca_para, center = F, scale. = F)
summary(pca_para)
data_plot = data.frame(pca_para$x[,c(1,2)])
t2 = adonis(data_plot ~ treatment*clone, data = metadata, method = "eu") # PerMANOVA
t2
para = qplot(x = PC1, y = PC2, data = data_plot, shape = factor(labels_clone),
color = factor(labels_drug), size=I(2), show.legend = FALSE, alpha = .9 ) +
custom_theme_pca + scale_shape_manual(values = c(16,17,18,9)) +
theme(plot.margin = unit(c(1,1,1,0),"mm"), title = element_text(family = "Helvetica"),
axis.text = element_text(family = "Helvetica")) +
ggtitle("Normalization: Parasite number,\ntreatment p-value = NS\nclone p-value < 0.05\nboth p-value = NS") + ylab(paste("PC2",get_2nd(pca_para),sep = " ")) + xlab(paste("PC1",get_1st(pca_para),sep = " "))
ggsave('fig_2d_para_norm_jan2018.pdf', para, width = 2.237, height = 2, units = "in")
# devSVG(file = "fig_2d_para_norm_dec2017.svg", width = 2.237, height = 2, xmlHeader = T)
# para
# dev.off()
rotation_a = pca_para$rotation #loadings
# PC 1 loadings (most important variable in PC1 in full dataset without normalization)
t$PC1_para = names(tail(sort(abs(rotation_a[,1])),10))
# PC 2 loadings (most important variable in PC2 in full dataset without normalization)
t$PC2_para = names(tail(sort(abs(rotation_a[,2])),10))
rm(data_plot,pca_para,rotation_a)
# Princple Component Analysis for PROTEIN NORMALIZATION
pca_pro <- prcomp(data_for_pca_pro, center = F, scale. = F)
summary(pca_pro)
data_plot = data.frame(pca_pro$x[,c(1,2)])
t3 = adonis(data_plot ~ treatment*clone, data = metadata, method = "eu") # PerMANOVA
t3
pro = qplot(x = PC1, y = PC2, data = data_plot, shape = factor(labels_clone),
color = factor(labels_drug), size=I(2), show.legend = F, alpha = .9 ) +
custom_theme_pca + scale_shape_manual(values = c(16,17,18,9))+
theme(plot.margin = unit(c(1,3,1,1),"mm"), title = element_text(family = "Helvetica"),
axis.text = element_text(family = "Helvetica")) +
ggtitle("Normalization: Protein,\ntreatment p-value = NS\nclone p-value = NS\nboth p-value = NS") + ylab(paste("PC2",get_2nd(pca_pro),sep = " ")) + xlab(paste("PC1",get_1st(pca_pro),sep = " "))
ggsave('fig_2d_pro_norm_Feb2018.pdf', pro, width = 2.237, height = 2, units = "in")
# devSVG(file = "fig_2d_pro_norm_dec2017.svg", width = 2.237, height = 2, xmlHeader = T)
# pro
# dev.off()
rotation_a = pca_pro$rotation #loadings
# PC 1 loadings (most important variable in PC1 in full dataset without normalization)
t$PC1_pro = names(tail(sort(abs(rotation_a[,1])),10))
# PC 2 loadings (most important variable in PC2 in full dataset without normalization)
t$PC2_pro = names(tail(sort(abs(rotation_a[,2])),10))
rm(rotation_a,data_plot,pca_pro)
# save plot
print(multiplot(unscaled,para,dna,pro,cols = 2))
tiff(filename = "2abcd_pca_multi_panel_jan2018.tiff", width = 4.475, height = 5,
units = "in", res = 300)
# devSVG(file = "2abcd_pca_multi_panel_dec2017.svg", width = 4.475, height = 5, xmlHeader = T)
# multiplot(unscaled,para,dna,pro,cols = 2)
# dev.off()
# save loadings
write.table(as.data.frame(t), "PC_list_Feb2018.csv", sep = ',',col.names = T, row.names = F)
```
13 Attempt correction for metabolites correlated with HEPES or Phenol Red
```{r}
#mets_to_correct = intersect(mets_to_correct_hepes,mets_to_correct_phenolred)
mets_to_correct = mets_to_correct_phenolred
corrected_dna_norm = as.data.frame(data_for_pca_dna)
for (x in 1:ncol(corrected_dna_norm)) {
if (colnames(corrected_dna_norm)[x] %in% mets_to_correct) {
for (i in 1:nrow(corrected_dna_norm)) {
corrected_dna_norm[i,x] = NA
} } }
corrected_dna_norm2 = corrected_dna_norm[colSums(!is.na(corrected_dna_norm)) > 0]
pca_hepes <- prcomp(corrected_dna_norm2, center = F, scale. = F)
summary(pca_hepes)
data_plot = data.frame(pca_hepes$x[,c(1,2)])
t4 = adonis(data_plot ~ treatment*clone, data = metadata, method = "eu") # PerMANOVA
t4
PR = qplot(x = PC1, y = PC2, data = data_plot, shape = factor(labels_clone),
color = factor(labels_drug), size=I(2), show.legend = F, alpha = .9 ) +
custom_theme_pca + scale_shape_manual(values = c(16,17,18,9))+
theme(plot.margin = unit(c(1,2,1,1),"mm"), title = element_text(family = "Helvetica"),
axis.text = element_text(family = "Helvetica")) +
ggtitle("Normalization: DNA/Phenol Red,\ntreatment p-value < 0.05\nclone p-value < 0.05\nboth p-value = NS") + ylab(paste("PC2",get_2nd(pca_hepes),sep = " ")) + xlab(paste("PC1",get_1st(pca_hepes),sep = " "))
ggsave('fig_2f_2018.pdf', PR, width = 2.237, height = 2, units = "in")
# devSVG(file = "fig_2f_2017.svg", width = 2.237, height = 2, xmlHeader = T)
# PR
# dev.off()
# print(multiplot(unscaled,dna,para,pro,hepes2, cols = 5))
rm(unscaled,dna,para,pro,t,pca_hepes,mets_to_correct_hepes,mets_to_correct_phenolred,corrected_dna_norm,data_plot)
```
14 Calculate FC
```{r}
data_list = c(t(data_for_pca_unnorm),t(data_for_pca_dna),t(data_for_pca_para),t(data_for_pca_pro),t(corrected_dna_norm2)) # metabolites as rows again
for (x in 1:5) {
# calculate SD and mean
data = data_list[[x]]
j = nrow(data)
data2 = as.data.frame(rbind(data,c(rep(-10,5),rep(10,5),rep(-20,5),rep(20,5)))) # groups
j = nrow(data2); data2 = make_numeric(data2)
data3 = mutate(data2, met = rownames(data2),
met_sd_group1_minus = apply(data2[,data2[j,] == -10], 1, sd), # SD
met_mean_group1_minus = rM(data2[,data2[j,] == -10]),
met_sd_group2_minus = apply(data2[,data2[j,] == -20], 1, sd ), # SD
met_mean_group2_minus = rM(data2[,data2[j,] == -20]),
met_sd_group1_plus = apply(data2[,data2[j,] == 10], 1, sd), # SD
met_mean_group1_plus = rM(data2[,data2[j,] == 10]),
met_sd_group2_plus = apply(data2[,data2[j,] == 20], 1, sd ), # SD
met_mean_group2_plus = rM(data2[,data2[j,] == 20]))
# calc FC and signficance
fc_data = data3
#fc_data = mutate(data3,fc = met_mean_group1/met_mean_group2)
j = nrow(fc_data); fc_data$pval[j] = NA
for (i in 1:(j-1)) {
val = as.vector(as.numeric(fc_data[i,c(1:20)]))
i_result = aov(val ~as.factor(as.character(fc_data[j,c(1:20)])), data = fc_data) # ANOVA
fc_data$pval[i] = summary(i_result)[[1]][["Pr(>F)"]][[1]]} # UNCORRECTED
# Multiple testing correction
fc_data = mutate(fc_data, adj_pval = p.adjust(fc_data$pval, method = "fdr"))
# significant comparisons
significant = subset(fc_data, fc_data$adj_pval <0.05)
write.table(significant,"fc_G1_v_G2_feb2018_log.csv",sep = ',',col.names = T,row.names = T)
if (dim(significant)[1] >= 1) {
hi = ggplot(data = significant) + geom_histogram(aes(significant$fc), bins = 100) +
#geom_vline(xintercept = 2, color = "red", size = 2) +
custom_theme2 + xlab("Fold Change") + ylab(NULL)
# print(hi)
# ggsave('distribution_FC_dec2017.pdf',hi,width = 3,height = 3)
rm(hi)}
else {print('this comparison has no significantly differentially abundant metabolites')}}
rm(significant,i,i_result,j,data,data2,data3,fc_data,x)
```
15 Comparison 2: Process blood batch data
```{r}
blood_batch = read.table("/Users/maureencarey/local_documents/work/metabolomics_methods/data/blood_batch_data.csv",sep = ',', header = T)
blood_batch = blood_batch[-c(1:15),]; rownames(blood_batch) = NULL
blood_batch$GROUP_NUMBER = sapply(blood_batch$GROUP_NUMBER, function(x) {gsub("(-)", "_minus", x,fixed = TRUE)})
blood_batch$GROUP_NUMBER = sapply(blood_batch$GROUP_NUMBER, function(x) {gsub("(+)", "_plus", x,fixed = TRUE)})
blood_batch$GROUP_NUMBER = sapply(blood_batch$GROUP_NUMBER, function(x) {gsub("-", "_", x,fixed = TRUE)})
blood_batch$GROUP_NUMBER = sapply(blood_batch$GROUP_NUMBER, function(x) {gsub(" ", "", x,fixed = TRUE)})
rownames(blood_batch) = blood_batch$GROUP_NUMBER
#data = as.data.frame(merge(blood_batch, data_for_pca_dna_comp2, by = 'row.names'))
data_list = list(data_for_pca_unnorm,data_for_pca_dna,data_for_pca_para,data_for_pca_pro, corrected_dna_norm2)
use_for_pca = c()
for (x in 1:5) {
data = as.data.frame(merge(blood_batch, data_list[[x]], by = 'row.names'))
rownames(data) = data$Row.names; data = data[,-c(1:3,5)]; colnames(data)[1] = 'blood'
use_for_pca[[x]] = make_numeric(as.data.frame(data))
use_for_pca[[x]]$blood = as.factor(as.character(use_for_pca[[x]]$blood)) }
```
16 PCA blood batch
```{r}
for (x in 1:5) {
# prep labels
if (x == 1) {
title = 'Unnormalized'; title2 = 'unnorm_'
} else if (x == 2) {
title = 'Normalized to DNA'
title2 = 'dna_norm_'
} else if (x == 3) {
title = 'Normalized to Protein'
title2 = 'pro_norm_'
} else if (x == 4 ) {
title = 'Normalized to Parasite Number'
title2 = 'para_norm_'
} else {
title = ' Normalized to DNA and HEPES'
title2 = 'dna_HEPES_norm_' }
labels_unique = rownames(use_for_pca[[x]]); substring(labels_unique, 5, 5) = "_"
labels_blood_batch = use_for_pca[[x]]$blood
labels_drug = lapply(rownames(use_for_pca[[x]]),
function(x) {substr(x,nchar(x)-4,nchar(x))})
labels_drug = unlist(labels_drug)
labels_drug = ifelse(labels_drug == '_plus','plus','minus')
# pca
pca_dna <- prcomp(use_for_pca[[x]][,-c(1)], center = F, scale. = F)
summary(pca_dna)
data_plot = data.frame(pca_dna$x[,c(1:4)])
# extra = ggplot(data_plot, aes(x = PC1, y = PC2)) +
# geom_point(aes(color = factor(labels_blood_batch),
# shape = factor(labels_unique)),
# size=I(4), alpha = .9) +
# custom_theme3 +
# scale_shape_manual(values = c(15:18)) +
# theme(plot.margin = unit(c(1,6,6,6),"mm"), # top, right, bottom, and left
# legend.title = element_text(size = 12)) +
# guides(color = guide_legend(title = 'Blood Batch'),
# shape = guide_legend(title = 'Parasite with drug')) +
# ggtitle(paste(title," blood batch v drug treatment")) + ylab("PC2") + xlab("PC1")
# extra
# fig_for_viewing = ggplot(data_plot, aes(x = PC1, y = PC2)) +
# geom_point(aes(color = factor(labels_blood_batch),
# shape = factor(labels_drug)),
# size=I(2), alpha = .9) +
# scale_shape_manual(values = c(15:16)) +
# custom_theme3 + theme( plot.margin = unit(c(1,1,1,1),"mm"),
# legend.title=element_text(size = 8),
# legend.text=element_text(size = 6),
# legend.key = element_rect(fill = NA),
# legend.key.height = unit(4, "mm"),
# legend.key.width = unit(2, "mm"),
# legend.position = "bottom",
# legend.direction = 'vertical') +
# guides(color = guide_legend(title = 'Blood\nBatch'),
# shape = guide_legend(title = 'Parasite\nwith drug')) +
# ggtitle(paste(title," blood batch v drug treatment")) +
# ylab("PC2") + xlab("PC1")
# print(fig_for_viewing)
fig = ggplot(data_plot, aes(x = PC1, y = PC2)) +
geom_point(aes(color = factor(labels_blood_batch),
shape = factor(labels_drug)),
size=I(2), alpha = .9) +
scale_shape_manual(values = c(15:16)) +
custom_theme3 + theme( plot.margin = unit(c(1,1,1,1),"mm")) +
guides(color = FALSE,shape = FALSE) +
ylab("PC2") + xlab("PC1")
ggsave(paste(title2,'blood_batch_drug_pca_feb2018.pdf'), fig, width = 60, height = 58, units = "mm") }
# devSVG(file = paste(title2,'blood_batch_drug_pca_dec2017.svg'), width = 2.237, height = 2, xmlHeader = T)
# fig
# dev.off() }
rm(extra, fig_for_viewing,labels_unique,labels_drug,labels_blood_batch,data_plot,pca_dna,title,title2,x,val,fig,fig_for_viewing,blood_batch,data)
# SEPARATE TO VIEW PLOTS
```
17 Predict blood batch con't (hide warnings here)
```{r, warning= F}
comparison_2_df = c()
for (x in 1:5) {
comparison_2_df[[x]] = remove_characters_from_colnames(use_for_pca[[x]])
which(duplicated(colnames(comparison_2_df[[x]])))
}
if ((sum(is.na(colnames(comparison_2_df[[4]]))) + sum(is.na(colnames(comparison_2_df[[5]]))) + sum(is.na(colnames(comparison_2_df[[3]]))) + sum(is.na(colnames(comparison_2_df[[2]]))) + sum(is.na(colnames(comparison_2_df[[1]])))) > 0) {print('there are NAs, FIX')}
```
FC, if you aren't getting sig hits here, classification based methods will not reveal anything unless sample size is large enough for non linear effects to emerge.
Distance heatmap in Aug 9 version
18 Calculate Blood batch FC
```{r}
for (x in 1:5) {
if (x == 1) {
title = 'Unnormalized'; title2 = 'unnorm_'
} else if (x == 2) {
title = 'Normalized to DNA'