Setup configuration
```{r loadData}
devtools::load_all('..')
source('shared.R')
set.seed(1)

```



# Windram

Load the data
```{r loadData}
dl.windram <- readRDS("Data/Windram.rds")

```

```{r makePlots}
plots.windram <- list()
plots.windram$pseudo <- with(dl.windram, {
    (ggplot(samples.l$tau %>% filter(iter == best.sample),
                    aes(x=tau, y=obstime.orig, color=capture),
                    environment=environment())
        + geom_point()
        # + scale_x_continuous(name="pseudotime")
        # + scale_y_continuous(name="capture time")
        # + scale_color_discrete(name="low-resolution\ngroup")
        + scale_y_continuous(name="true capture time")
        + scale_x_continuous(name="pseudotime")
        + scale_color_discrete(name="model\ncapture\ntime")
    )
})
plots.windram$tau.posterior <- (
    ggplot(dl.windram$samples.l$tau, aes(x=capture.orig, y=tau, color=capture))
    + geom_boxplot()
    + scale_x_discrete(name="true capture time")
    + scale_y_continuous(name="pseudotime")
    + scale_color_discrete(name="model\ncapture\ntime")
    + coord_flip())
posterior.cor <- (
    dl.windram$samples.l$tau
    %>% group_by(iter)
    %>% dplyr::summarise(pseudotime.capture.cor=cor(tau, obstime.orig,
                                             method="spearman"))
)
posterior.cor.mean <- mean(posterior.cor$pseudotime.capture.cor)
posterior.cor.best <- filter(posterior.cor,
                             dl.windram$best.sample == iter)$pseudotime.capture.cor
monocle.cor <- 0.9269565
plots.windram$post.cor <- (ggplot(posterior.cor, aes(x=pseudotime.capture.cor))
    + geom_histogram()
    + geom_vline(xintercept=monocle.cor, linetype='dotted')
    + geom_vline(xintercept=posterior.cor.best, linetype='dashed')
    + scale_x_continuous(name="correlation"))
plots.windram$profiles <- plot(
    dl.windram, type="profiles",
    genes=sample(dl.windram$genes.high.psi, 12))
plots.windram$roughnesses <- plot(dl.windram, type="roughnesses")

```


```{r savePlots}
config <- bioinf.sizes
theme <- bioinf.theme
ggsave('Figures/Bioinf/Windram-pseudotime-vs-obfuscated.eps',
       plots.windram$pseudo + theme, units="in",
       width=sizes$width, height=sizes$height)
ggsave('Figures/Bioinf/Windram-tau-posterior.eps',
       plots.windram$tau.posterior + theme, units=sizes$units,
       width=sizes$width, height=sizes$height)
ggsave('Figures/Bioinf/Windram-posterior-cor.eps',
       plots.windram$post.cor + theme, units=sizes$units,
       width=sizes$width, height=sizes$height)
ggsave('Figures/Bioinf/Windram-profiles.pdf',
       plots.windram$profiles + theme, units=sizes$units,
       width=bioinf.double.w, height=bioinf.double.h)
ggsave('Figures/Bioinf/Windram-roughnesses.eps',
       plots.windram$roughnesses + theme, units=sizes$units,
       width=sizes$width, height=sizes$height)

```


# McDavid

Load the data
```{r loadData}
dl.mcdavid <- readRDS("Data/McDavid.rds")

```

```{r makePlots}
plots.mcdavid <- list()
# A function to make times periodic
periodise <- function(tau, period=3) {
    wave = floor(tau/period)
    tau - wave * period
}
# A function to map from pseudotimes (tau) to cbPeaktimes
tau.to.cbtime <- function(tau) 100*periodise((tau - .5)/3, 1)
# and vice versa
cbtime.to.tau <- function(cbtime) periodise(cbtime/100*3+.5, 3)
#
# Add the peak times to a profile plot.
add.peak.times <- function(dl.mcdavid, gp) {
    plot.peaktimes <- (
        dl.mcdavid$gene.meta
        # Only use those genes that are in the plot and have a peak time
        %>% filter(gene %in% gp$data[['gene']], ! is.na(cbPeaktime))
        # Convert the CycleBase peak time to pseudotime
        %>% mutate(peak.tau=cbtime.to.tau(cbPeaktime))
    )
    # Add the peak times as vertical lines to the plot
    (
        gp
        + geom_vline(data=plot.peaktimes,
                     aes(xintercept=peak.tau),
                     linetype='dashed', alpha=.7)
    )
}
genes.for.profiles <- with(dl.mcdavid,
    samples.l$psi
    %>% filter(best.sample == iter)
    %>% left_join(samples.l$omega)
    %>% arrange(-psi/omega)
    %>% head(12)
    %>% left_join(gene.map)
)
plots.mcdavid$profiles <- (
    plot(dl.mcdavid, type="profiles", genes=genes.for.profiles$gene)
    + scale_x_continuous(name='Pseudotime', limits=c(0, 3))
)
plots.mcdavid$profiles <- (
    add.peak.times(dl.mcdavid, plots.mcdavid$profiles)
    + scale_x_continuous(name='Pseudotime',
                         breaks=c(0,1,2,3),
                         labels=c('G2/M', 'G0/G1', 'S', 'G2/M'))
    + theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1))
)

```


```{r savePlots}
ggsave('Figures/Bioinf/McDavid-profiles.pdf',
       plots.mcdavid$profiles + theme, units="in",
       width=bioinf.double.w, height=bioinf.double.h)

```



# Shalek

Load the data
```{r loadData}
dl.shalek <- readRDS("Data/Shalek.rds")
input.shalek <- readRDS('Data/Shalek-input.rds')

```

```{r makePlots}
plots.shalek <- list()
clusters <- c("Id", "IIIb", "IIIc", "IIId")
held.out.genes <- with(dl.shalek, input.shalek$gene.variances
                           %>% left_join(gene.meta)
                           %>% filter(! gene %in% gene.map$gene)
                           %>% filter(cluster %in% clusters)
                           # %>% filter(cluster != "Id")
                           %>% arrange(-Psi/Omega)
                           %>% head(getOption('Shalek.held.out', 100))
)
# Get an expression matrix of held out genes in the cells of interest
held.out.expr <- shalek.A.expr[as.character(held.out.genes$gene),
                               as.character(dl.shalek$cell.map$cell)]
time.course.cells <- (
    dl.shalek$cell.meta
    %>% filter(! is.na(total),
               "" == assay,
               "LPS" == stimulant | "" == stimulant,
               "" == ko,
               FALSE == disrupted,
               total > 1e6,
               "" == replicate))
clustered <- dl.shalek$gene.meta %>% filter(! is.na(cluster))
fitted.time.course.cells <- (
    time.course.cells
    %>% filter(cell %in% dl.shalek$cell.map[['cell']])
)$cell
clustered.expr <- shalek.A.expr[as.character(clustered$gene),
                                as.character(fitted.time.course.cells)]
# rownames(clustered.expr)
# colnames(clustered.expr)
clustered.expr.l <- melt.expr(dl.shalek, clustered.expr) %>% left_join(dl.shalek$gene.meta)
names(clustered.expr.l)
# sample_n(clustered.expr.l, 14)
module.scores <- (
    clustered.expr.l
    %>% group_by(cluster, cell)
    %>% dplyr::summarise(module.score=mean(x))
    %>% left_join(dl.shalek$samples.l$tau
                  %>% filter(dl.shalek$best.sample == iter)
                  %>% dplyr::select(cell, tau)))
module.scores
stopifnot(all(! is.na(module.scores)))
# Find the precocious cells
core.antiviral <- (
    module.scores
    %>% left_join(dl.shalek$cell.meta %>% dplyr::select(cell, capture))
    %>% filter("Id" == cluster)
    %>% arrange(-module.score))
precocious <- core.antiviral %>% filter("1h" == capture) %>% head(2)
precocious
precocious$cell
module.scores <- (
    module.scores %>% mutate(type=ifelse(cell %in% precocious$cell,
                                         "precocious",
                                         "not precocious"))
)
plots.shalek$core.antiviral <- (
    ggplot(core.antiviral,
           aes(x=module.score, color=capture))
    + geom_density()
)
# Plot the core antiviral, the maturity, the peaked inflammation and
# the sustained inflammation module scores against pseudotime.
plots.shalek$module <- (
    ggplot(module.scores
           %>% filter(! is.na(tau),
                      cluster %in% c("Id", "IIIb", "IIIc", "IIId")),
           aes(x=tau, y=module.score, colour=cluster))
    + stat_smooth()
    + geom_point()
)
# Same with just core antiviral coloured by capture
plots.shalek$core <- (
    ggplot(module.scores
           %>% left_join(dl.shalek$cell.meta %>% dplyr::select(cell, capture))
           %>% filter(! is.na(tau),
                      cluster == "Id"),
           aes(x=tau, y=module.score, colour=capture, shape=type))
    + stat_smooth(aes(group="", color=NULL))
    + geom_point()
)
# Examine what pseudotimes the model estimated for the precocious genes
(
    dl.shalek$samples.l$tau
    %>% filter(dl.shalek$best.sample == iter,
               cell %in% precocious$cell)
)
dl.shalek <- roughness.test(dl.shalek, held.out.expr)
plots.shalek$roughnesses <- plot(dl.shalek, type="roughnesses")

```


```{r savePlots}
ggsave('Figures/Bioinf/Shalek-core.pdf',
       plots.shalek$core + theme, units="in",
       width=bioinf.single.w, height=bioinf.single.h)
ggsave('Figures/Bioinf/Shalek-roughnesses.eps',
       plots.shalek$roughnesses + theme, units="in",
       width=bioinf.single.w, height=bioinf.single.h)

```

