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iRep_lmer.R
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iRep_lmer.R
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library(tidyverse)
library(broom)
library(lme4)
library(lmerTest)
library(emmeans)
dat_QC <- read_tsv('iRep_estimates_QCd.tsv') |>
mutate(day=factor(day, levels = c('07', '35', '78')),
treatment=factor(treatment, levels = c('ctrl', 'sub', 'ther')))
dat_QC$day
dat_QC$treatment
mod_interact <- lmer(data=dat_QC, formula = iRep ~ treatment * day + (1|genome))
mod_simple <- lmer(data=dat_QC, formula = iRep ~ treatment + day + (1|genome))
summary(mod_simple)
summary(mod_interact)
# some of the interaction coefs are significant, ther group prob has different
# behavior over time
anova(mod_simple, mod_interact)
# interaction model fits the data better
plot(mod_simple)
plot(mod_interact)
resids <- mod_interact %>% resid()
LOOK <- dat_QC |> mutate(resids)
LOOK |> ggplot(aes(x=resids, y=iRep)) + geom_point()
treatment_effects <- emmeans(mod_interact, ~ treatment | day) %>%
contrast(method='pairwise', adjust='fdr')
time_effects <- emmeans(mod_interact, ~ day | treatment) %>%
contrast(method='pairwise', adjust='fdr')
all_effects <- rbind(treatment_effects, time_effects) %>%tidy(conf.int=T) |>
mutate(contrast=factor(contrast))
library(cowplot)
# treatment effects at different timepoints
all_effects |>
filter(treatment != '.') |>
ggplot(aes(x=fct_rev(contrast), color=treatment, y=estimate, ymin=conf.low, ymax=conf.high)) +
geom_linerange(position=position_dodge(width = .51), size=1.25, ) +
geom_point(aes(fill=treatment),position=position_dodge(width = .51), shape=21, size=4, color='black')+
coord_flip() +
geom_hline(yintercept = 0)+
theme_cowplot() +
theme(panel.grid.major = element_line(color='grey')) +
xlab('contrast between days')+
ylab('difference in iRep growth rates') +
ggtitle('Effects of time within each treatment') +
ylim(-.1, .3)
# time effects within different treatments
all_effects |>
filter(day != '.') |>
ggplot(aes(x=fct_rev(contrast),color=day, y=estimate, ymin=conf.low, ymax=conf.high)) +
geom_linerange(position=position_dodge(width = .51), size=1.25, ) +
geom_point(aes(fill=day),position=position_dodge(width = .51), shape=21, size=4, color='black')+
coord_flip() +
geom_hline(yintercept = 0)+
theme_cowplot() +
theme(panel.grid.major = element_line(color='grey'))+
xlab('contrast between treatments')+
ylab('difference in iRep growth rates') +
ggtitle('Treatment effects within each timepoint')+
ylim(-.1, .3)
# displays estimated means for each treatment group over time.
daily_means <- emmeans(mod_interact, ~ treatment | day) %>% tidy(conf.int=T)
daily_means %>%
ggplot(aes(x=day, y=estimate,fill=treatment, ymin=conf.low, ymax=conf.high, group=treatment, color=treatment)) +
geom_line(size=1.25, alpha=.9) +
geom_errorbar(size=1, width=.2, position=position_dodge(width = .1))+
geom_point(shape=21, size=4, color='black', position=position_dodge(width = .1)) +
theme_cowplot()+
theme(panel.grid.major = element_line(color='grey')) +
ylab('estimated iRep growth rate')
daily_means %>%
ggplot(aes(x=treatment, y=estimate,fill=day, ymin=conf.low, ymax=conf.high, group=day, color=day)) +
# geom_line(size=1.25, alpha=.9) +
geom_errorbar(size=1, width=.2, position=position_dodge(width = .1))+
geom_point(shape=21, size=4, color='black', position=position_dodge(width = .1)) +
theme_cowplot()+
theme(panel.grid.major = element_line(color='grey')) +
ylab('estimated iRep growth rate')
### heatmaps?
library(pheatmap)
dat_QC %>% filter(day=='07') %>%
transmute(sample, genome,log_rabund=log(`relative abundance`)) %>%
pivot_wider(names_from = genome, values_from = log_rabund, values_fill = -1) %>%
column_to_rownames(var='sample') %>%
pheatmap()
color = colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(100)
dat_QC %>% filter(day=='35') %>%
transmute(sample, genome,log_rabund=log(`relative abundance`)) %>%
pivot_wider(names_from = genome, values_from = log_rabund, values_fill = -1) %>%
column_to_rownames(var='sample') %>%
pheatmap()
dat_QC %>% filter(day=='78') %>%
transmute(sample, genome,log_rabund=log(`relative abundance`)) %>%
pivot_wider(names_from = genome, values_from = log_rabund, values_fill = -1) %>%
column_to_rownames(var='sample') %>%
pheatmap()