---
title: "HTS-SIP data simulation"
author: "Nick Youngblut"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{HTS-SIP data simulation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Introduction

HTSSIP has some functionality for simulating basic HTS-SIP datasets. With that said, I recommend using a more sophisticated simulation toolset such as SIPSim **[LINK]** for applications other than simple testing of HTS-SIP data analysis functions.

HTSSIP relies *heavily* on the great R package [coenocliner](https://CRAN.R-project.org/package=coenocliner). See [this tutorial](http://www.fromthebottomoftheheap.net/code/r-packages/coenocliner/) for a short and simple introduction. 

# Simulating a HTS-SIP dataset

In this vignette, we're going to simulate gradient fraction communities for 6 gradients, with the basic experimental setup as follows:

* Treatments: 13C-glucose vs 12C-control 
* Treatment replicates: 3 (each)
* Total gradients: 6
* Fractions per gradient: 24

First, let's load some packages including `HTSSIP`. 

```{r, message=FALSE, warning=FALSE}
library(dplyr)
library(ggplot2)
library(HTSSIP)
```

OK, let's set the parameters needed for community simulations. We are basically going to follow the coenocliner tutorial, but instead of a transect along an environmental gradient, we are simulating communities in each fraction of a density gradient. 

```{r}
# setting parameters for tests
set.seed(318)                              # reproduciblility
M = 6                                      # number of OTUs (species)
ming = 1.67                                # gradient minimum...
maxg = 1.78                                # ...and maximum
nfrac = 24                                 # number of gradient fractions
locs = seq(ming, maxg, length=nfrac)       # Fraction BD's
tol  = rep(0.005, M)                       # species tolerances
h    = ceiling(rlnorm(M, meanlog=11))      # max abundances

opt = rnorm(M, mean=1.71, sd=0.008)        # species optima (drawn from a normal dist.)
params = cbind(opt=opt, tol=tol, h=h)      # put in a matrix
```

With the current parameters, we can simulate the gradient fraction communities for 1 density gradient: 

```{r}
df_OTU = gradient_sim(locs, params)
df_OTU
```

As you can see, the abundance distribution of each OTU is approximately Gaussian, with varying optima among OTUs. 

### Simulating the full SIP experiment

Let's make the communities of all gradient fractions for all gradients.  

If all OTUs in the 13C-treatment incorporated labeled isotope, then their abundance distributions should be shifted to 'heavier' buoyant densities. Let's set the 13C-treatment gradients to have a higher mean species optima. For kicks, let's also increase the species optima variance (representing more variable isotope incorporation percentages).

```{r}
opt1 = rnorm(M, mean=1.7, sd=0.005)      # species optima
params1 = cbind(opt=opt1, tol=tol, h=h)  # put in a matrix
opt2 = rnorm(M, mean=1.7, sd=0.005)     
params2 = cbind(opt=opt2, tol=tol, h=h) 
opt3 = rnorm(M, mean=1.7, sd=0.005)     
params3 = cbind(opt=opt3, tol=tol, h=h) 
opt4 = rnorm(M, mean=1.72, sd=0.008)    
params4 = cbind(opt=opt4, tol=tol, h=h) 
opt5 = rnorm(M, mean=1.72, sd=0.008)    
params5 = cbind(opt=opt5, tol=tol, h=h) 
opt6 = rnorm(M, mean=1.72, sd=0.008)    
params6 = cbind(opt=opt6, tol=tol, h=h) 

# we need a named list of parameters for the next step. The names in the list will be used as sample IDs
params_all = list(
  '12C-Con_rep1' = params1,
  '12C-Con_rep2' = params2,
  '12C-Con_rep3' = params3,
  '13C-Glu_rep1' = params4,
  '13C-Glu_rep2' = params5,
  '13C-Glu_rep3' = params6
)
```

### Additional sample metadata

Additional sample metadata can be added to the resulting phyloseq object that we are going to simulate. To add metadata, just make a data.frame object with a `Gradient` column, which will be used for matching to the simulated samples. The `Gradient` column values should match the names in the parameter list. 

```{r}
meta = data.frame(
  'Gradient' = c('12C-Con_rep1', '12C-Con_rep2', '12C-Con_rep3',
                 '13C-Glu_rep1', '13C-Glu_rep2', '13C-Glu_rep3'),
  'Treatment' = c(rep('12C-Con', 3), rep('13C-Glu', 3)),
  'Replicate' = c(1:3, 1:3)
)

# do the names match?
all(meta$Gradient %in% names(params_all))
```

OK. Let's make the phyloseq object with the parameters that we specified above. 

```{r}
## physeq object
physeq_rep3 = HTSSIP_sim(locs, params_all, meta=meta)
physeq_rep3
```

How does the `sample_data` table look? 

```{r}
phyloseq::sample_data(physeq_rep3) %>% head
```


## Simulating qPCR data

The q-SIP analysis requires qPCR measurements of gene copies for each gradient fraction. We can simulate this data for the HTS-SIP dataset phyloseq object that we just created. 

The qPCR value simulation function is fairly flexible. For input, functions are provided that define how qPCR values (averages & variability) relate to buoyant density. For example, you can set the 'peak' of qPCR values to be located at a BD of 1.7 for unlabeled gradient samples and a 'peak' at a BD of 1.75 for labeled samples. For this example, let's set the error among replicate qPCR reactions (technical replicates) to scale with the mean qPCR values for that gradient sample. 

```{r}
# unlabeled control qPCR values
control_mean_fun = function(x) dnorm(x, mean=1.70, sd=0.01) * 1e8
# error will scale with the mean 
control_sd_fun = function(x) control_mean_fun(x) / 3
# labeled treatment values
treat_mean_fun = function(x) dnorm(x, mean=1.75, sd=0.01) * 1e8
# error will scale with the mean 
treat_sd_fun = function(x) treat_mean_fun(x) / 3
```

OK. Let's simulate the qPCR data.

```{r}
physeq_rep3_qPCR = qPCR_sim(physeq_rep3,
                control_expr='Treatment=="12C-Con"',
                control_mean_fun=control_mean_fun,
                control_sd_fun=control_sd_fun,
                treat_mean_fun=treat_mean_fun,
                treat_sd_fun=treat_sd_fun)

physeq_rep3_qPCR %>% names
```

The output is a list containing:

* 'raw' data
    * qPCR values for each PCR reaction
* 'summary' data
    * qPCR mean values (& standard deviations) for each gradient fraction sample.

```{r}
physeq_rep3_qPCR$raw %>% head
```

```{r}
physeq_rep3_qPCR$summary %>% head
```

Let's plot the data.

```{r, fig.height=3, fig.width=7}
x_lab = 'Buoyant density (g ml^-1)'
y_lab = '16S rRNA gene copies'

ggplot(physeq_rep3_qPCR$summary, aes(Buoyant_density, qPCR_tech_rep_mean,
                      ymin=qPCR_tech_rep_mean-qPCR_tech_rep_sd,
                      ymax=qPCR_tech_rep_mean+qPCR_tech_rep_sd,
                      color=IS_CONTROL, shape=Replicate)) +
  geom_pointrange() +
  scale_color_discrete('Unlabeled\ncontrol') +
  labs(x=x_lab, y=y_lab) +
  theme_bw()
```

With this simulation, we made the separation between labeled and unlabeled DNA pretty easy to distinguish. 

OK. Just to show the flexiblity of the qPCR value simulation function, let's try using some other functions as input. 

```{r}
# using the Cauchy distribution instead of normal distributions
control_mean_fun = function(x) dcauchy(x, location=1.70, scale=0.01) * 1e8
control_sd_fun = function(x) control_mean_fun(x) / 3
treat_mean_fun = function(x) dcauchy(x, location=1.75, scale=0.01) * 1e8
treat_sd_fun = function(x) treat_mean_fun(x) / 3
# simulating qPCR values
physeq_rep3_qPCR = qPCR_sim(physeq_rep3,
                control_expr='Treatment=="12C-Con"',
                control_mean_fun=control_mean_fun,
                control_sd_fun=control_sd_fun,
                treat_mean_fun=treat_mean_fun,
                treat_sd_fun=treat_sd_fun)
```

Now, how does the data look?

```{r, fig.height=3, fig.width=7}
ggplot(physeq_rep3_qPCR$summary, aes(Buoyant_density, qPCR_tech_rep_mean,
                      ymin=qPCR_tech_rep_mean-qPCR_tech_rep_sd,
                      ymax=qPCR_tech_rep_mean+qPCR_tech_rep_sd,
                      color=IS_CONTROL, shape=Replicate)) +
  geom_pointrange() +
  scale_color_discrete('Unlabeled\ncontrol') +
  labs(x=x_lab, y=y_lab) +
  theme_bw()
```


# Session info

```{r}
sessionInfo()
```

