---
title: "The DeLorean pseudotime estimation package"
author: "John Reid"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{DeLorean}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, echo=F}
knitr::opts_chunk$set(
    fig.width=12,
    fig.height=12/1.618,
    out.width='685px',
    dpi=144,
    message=FALSE)
```

Our understanding of dynamical biological systems such as developmental
processes or transitions into disease states is limited by our ability to
reverse-engineer these systems using available data.  Most medium- and
high-throughput experimental protocols (e.g. single cell RNA-seq) are
destructive in nature, generating cross-sectional time series in which it
is not possible to track the progress of one cell through the system.  In
typical systems, individual cells progress at different rates and a
sample's experimental capture time will not accurately reflect how far it
has progressed. In these cases, cross-sectional data can appear
particularly noisy.

## Pseudotime

We propose a probabilistic model that uses smoothness assumptions to
estimate and correct for this effect.  Each cell is assigned a pseudotime
that represents its progress through the system.  These pseudotimes are
related to but not determined by the cells' experimental capture times.
Replacing capture times with pseudotimes gives us a more representative
view of the underlying system, improving downstream analyses.

## Model

We model the smoothness assumption on each gene's expression profile $y_g$
using a Gaussian process over pseudotime. Gene-specific parameters in the
covariance function represent intrinsic measurement noise $\omega_g$
and variation of the profile over time $\psi_g$. Each sample's
pseudotime $\tau_c$ is given a normal prior centred on its capture time.
$$
\begin{aligned}
    y_{g} &\sim \mathcal{GP}(\phi_g, \Sigma_g) \\
    \Sigma_g(\tau_1, \tau_2)
    &= \psi_g \Sigma_\tau(\tau_1, \tau_2)
        + \omega_g \delta_{\tau_1,\tau_2} \\
    \log \psi_g &\sim \mathcal{N}(\mu_\psi, \sigma_\psi) \\
    \log \omega_g &\sim \mathcal{N}(\mu_\omega, \sigma_\omega) \\
    \Sigma_\tau(\tau_1, \tau_2)
        &= \textrm{Matern}_{3/2}\bigg(r=\frac{|\tau_1 - \tau_2|}{l}\bigg)
        = (1 + \sqrt{3}r) \exp[-\sqrt{3}r] \\
    \tau_c &\sim \mathcal{N}(k_c, \sigma_\tau)
\end{aligned}
$$
This model is effectively a one-dimensional Gaussian process latent
variable model with a structured prior on the latent variable (pseudotime).


## Example

Guo et al. assayed single cell expression values at 7 time points in mouse
embryonic cells and the data is contained in the `DeLorean` package.  We will
load this data and analyse a subset of it corresponding to the epiblast
lineage. First we must build a `de.lorean` object with the correct data.
Load the data.
```{r guoData}
library(DeLorean)
library(dplyr)
data(GuoDeLorean)
# Limit number of cores to 2 for CRAN
options(DL.num.cores=min(default.num.cores(), 2))
```

Create a de.lorean object from the full data set.
```{r}
dl <- de.lorean(guo.expr, guo.gene.meta, guo.cell.meta)
```

Estimate hyperparameters for the model from the whole data set. Here we set
the width of the normal prior on the pseudotimes to be `0.5`.
```{r}
dl <- estimate.hyper(
    dl,
    sigma.tau=0.5,
    length.scale=1.5,
    model.name='exact-sizes')
```
DeLorean also offers slight variations of the model, we could use
`model.name='lowrank'` or `model.name='exact'`. See the documentation
for `estimate.hyper` for more details.

### Choose cells

Choose a few cells from each capture point from the epiblast lineage.
```{r}
num.at.each.stage <- 5
epi.sampled.cells <- guo.cell.meta %>%
    filter(capture < "32C" |
           "EPI" == cell.type |
           "ICM" == cell.type) %>%
    group_by(capture) %>%
    do(sample_n(., num.at.each.stage))
dl <- filter_cells(dl, cells=epi.sampled.cells$cell)
```

### Choose genes

We only have data for a few genes and can easily model them all which is
typical for qPCR data. RNA-seq data often has far too many genes for the model
to fit. In any case most are probably irrelevant. In these cases we recommend
an analysis of variance across the capture times to choose those genes whose
means vary most across time. These are most likely to be relevant for fitting
the model.
```{r aov}
dl <- aov.dl(dl)
```
The most temporally variable genes (by p-value) are at the head of the result:
```{r}
head(dl$aov)
```
The least temporally variable genes (by p-value) are at the tail of the result:
```{r}
tail(dl$aov)
```
and for instance you could run the model on the 20 most variable
genes by executing
```{r, exec=FALSE}
dl <- filter_genes(dl, genes=head(dl$aov, 20)$gene)
```
otherwise do not call `filter_genes` and `DeLorean` will use all the genes.


### Fit the model

Now we have the data we can fit our model using Stan's ADVI variational Bayes
algorithm. To run the No-U-Turn sampler use `method='sample'`.
```{r fitDL, cache=TRUE, results='hide'}
dl <- fit.dl(dl, method='vb')
```


### Examine convergence

If running a sampler, Stan provides $\hat{R}$ statistics that can aid detecting
convergence problems. This makes no sense for ADVI but we show how to produce
the boxplots here for users of the samplers.
```{r}
dl <- examine.convergence(dl)
plot(dl, type='Rhat')
```


### Estimated pseudotimes

Plot the pseudotimes from the best sample (best in the sense of highest
likelihood).  The prior means for the capture points are shown as dashed lines.
```{r plot}
plot(dl, type='pseudotime')
```


### Expression profiles

Plot the expression data over the pseudotimes from the best sample.
```{r}
dl <- make.predictions(dl)
plot(dl, type='profiles')
```
