---
title: "_Cardinal 3_: Statistical methods for mass spectrometry imaging"
author: "Kylie Ariel Bemis"
date: "Revised: May 16, 2019"
output:
  BiocStyle::html_document:
  toc: true
vignette: >
  %\VignetteIndexEntry{2. Cardinal 3: Statistical methods for mass spectrometry imaging}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r style, echo=FALSE, results='asis'}
BiocStyle::markdown()
```

```{r setup, echo=FALSE, message=FALSE}
library(Cardinal)
setCardinalBPPARAM(SerialParam())
setCardinalVerbose(FALSE)
RNGkind("Mersenne-Twister")
```

# Introduction 

*Cardinal 3* provides statistical methods for both supervised and unsupervised analysis of mass spectrometry (MS) imaging experiments. Class comparison can also be performed, provided an appropriate experimental design and sample size.

Before statistical analysis, it is important to identify the statistical goal of the experiment:

- __Unsupervised analysis__. The data has no class labels or conditions, and we are interested in exploratory analysis to *discover* regions of interest in the data.

- __Supervised analysis__. The data has class labels and we want to train a statistical or machine learning model to *predict* the class labels of new data.

- __Class comparison__. The data has class labels or conditions, and we want to *test* whether the abundance of the mass features is different between conditions.

*CardinalWorkflows* provides real experimental data and more detailed discussion of the statistical methods than will be covered in this brief overview.









# Exploratory analysis

Suppose we are exploring an unlabeled dataset, and wish to understand the structure of the data.

```{r eda-data, fig.height=3, fig.width=3, fig.align='center'}
set.seed(2020)
mse <- simulateImage(preset=2, npeaks=10, dim=c(20,20), sdnoise=0.5,
					peakheight=c(2,4), representation="centroid")

design <- makeFactor(circle=mse$circle, square=mse$square,
						bg=!(mse$circle | mse$square))

image(mse, design ~ x * y, key=TRUE)
```

```{r eda-image, fig.height=2, fig.width=7}
image(mse, feature=c(1,4,7), layout=c(1,3))
```

## Principal components analysis (PCA)

Principal components analysis is an unsupervised dimension reduction technique. It reduces the data to some number of "principal components" that are a linear combination of the original mass features, where each component is orthogonal to the last, and explains as much of the variance in the data as possible.

Use `PCA()` to perform PCA on a `MSImagingExperiment`.

```{r pca}
pca <- PCA(mse, ncomp=3)

summary(pca)
```

We can see that the first two principal components explain most of the variation in the data.

```{r pca-image, fig.height=2, fig.width=7}
image(pca, values="scores", superpose=FALSE, layout=c(1,3))
```

The loadings of the components show how each mass feature contributes to each component.

```{r pca-loadings, fig.height=2, fig.width=7}
plot(pca, values="loadings", superpose=FALSE, layout=c(1,3), lwd=2)
```

Plotting the principal component scores against each other is a useful way of visualization the separation between data classes.

```{r pca-scores, fig.height=2.5, fig.width=4, fig.align='center'}
pca_scores <- DataFrame(resultData(pca, 1, "scores"))

plot(pca_scores, PC1 ~ PC2, groups=design, pch=20)
```

## Feature colocalization

Finding other mass features colocalized with a particular image is a common task in analysis of MS imaging experiments.

Use `colocalize()` to find mass features that are colocalized with another image.

```{r colocalized}
coloc <- colocalized(mse, mz=1023)
coloc
```

By default, Pearson correlation is used to rank the colocalized features. Manders' colocalization coefficients (M1 and M2) are also provided.

```{r colocalized-images, fig.height=2, fig.width=7}
image(mse, mz=coloc$mz[1:3], layout=c(1,3))
```








# Image segmentation

Segmentation (clustering) a dataset is a useful way to summarize an MS imaging experiment and discover regions of interest within the sample.

## Spatial shrunken centroids clustering

Spatially-aware nearest shrunken centroids clustering allows simultaneous image segmentation and feature selection.

A smoothing radius `r`, initial number of clusters `k`, and sparsity parameters `s` must be provided.

The larger the sparsity parameter `s`, the fewer mass features will contribute to the segmentation.

Spatial shrunken centroids may result in fewer clusters than the initial number of clusters `k`, so it is recommended to use a value for `k` that is larger than the expected number of clusters, and allow the method to automatically choose the number of clusters.

```{r ssc-clustering}
ssc <- spatialShrunkenCentroids(mse, r=1, k=5, s=c(0,3,6,9))

summary(ssc)
```

Plotting the predicted cluster probabilities shows a clear segmentation into the ground truth image.

```{r ssc-image, fig.height=3, fig.width=3}
image(ssc, model=list(s=9), values="probability")
```

Spatial shrunken centroids calculates t-statistics for each segment and each mass feature. These t-statistics a measure of the difference between the cluster center and the global mean.


```{r ssc-statistic, fig.height=2.5, fig.width=4, fig.align='center'}
plot(ssc, model=list(s=9), values="statistic", lwd=2)
```

Mass features with t-statistics of zero do not contribute to the segmentation. The sign of the t-statistic indicates whether the mass feature is over- or under-expressed in the given cluster relative to the global mean.

Use `topFeatures()` to rank mass features by t-statistic.


```{r ssc-top}
ssc_top <- topFeatures(ssc, model=list(s=9), class == 1)
ssc_top
```


## Spatial Dirichlet Gaussian mixture modeling

Spatially-aware Dirichlet Gaussian mixture models (spatial-DGMM) is a method of image segmentation applied to each mass feature individually, rather than the dataset as a whole.

This is useful for summarizing molecular ion images, and for discovering structures that clustering using all mass features together may miss.

```{r dgmm}
dgmm <- spatialDGMM(mse, r=1, k=5, method="adaptive")

summary(dgmm)
```

A different segmentation is fit for each mass feature.

```{r dgmm-image, fig.height=2, fig.width=7}
image(dgmm, model=list(feature=c(1,4,7)), layout=c(1,3))
```

Each image is modeled as a mixture of Gaussian distributions.

```{r dgmm-plot, fig.height=2, fig.width=7}
plot(dgmm, model=list(feature=c(1,4,7)), layout=c(1,3))
```

Spatial-DGMM segmentations can be especially useful for finding mass features colocalized with a region-of-interest.

When applied to a `SpatialDGMM` object, `colocalize()` is able to use match scores that can have a higher specificity than using Pearson correlation on the raw ion images.

```{r dgmm-colocalized}
coloc2 <- colocalized(dgmm, mse$square)
subset(coloc2, select=c(-r, -k, -group))
```









# Classification and cross-validation

Classification of pixels into different known classes (e.g., cancer vs normal) based on the mass spectra is a common application for MS imaging.

```{r classification-data, fig.height=2, fig.width=7}
set.seed(2020)
mse2 <- simulateImage(preset=7, npeaks=10, dim=c(10,10), sdnoise=0.5,
					nruns=3, peakdiff=2, representation="centroid")

class <- makeFactor(A=mse2$circleA, B=mse2$circleB)

image(mse2, class ~ x * y, key=TRUE, layout=c(1,3))
```


```{r classification-images, fig.height=2, fig.width=7}
image(mse2, feature=1, layout=c(1,3))
```

When performing classification, it is important to use cross-validation so that reported accuracies are not overly optimistic.

We strongly recomend making sure that all spectra from the same experiment run belong to the same fold, to reduce predictive bias due to run effects.



## Projection to latent structures (PLS)

Projection to latent structures (PLS), also called partial least squares, is a supervised dimension reduction technique. It can be thought of as being similar to PCA, but for classification or regression.

```{r pls-cv}
cv_pls <- crossValidate(mse2, .y=class, .fun=PLS, ncomp=1:5, .fold=run(mse2))

summary(cv_pls)
```

We can see that using 3 PLS components produces the best cross-validated accuracy.

```{r pls}
pls <- PLS(mse2, y=class, ncomp=3)

summary(pls)
```

We can plot the fitted values to visualize the prediction.

```{r pls-image, fig.height=2, fig.width=7}
image(pls, values="fitted", layout=c(1,3))
```

The PLS regression coefficients can be used to select influential features.

```{r pls-coefficients, fig.height=2.5, fig.width=4, fig.align='center'}
plot(pls, values="coefficients", lwd=2)
```

Like PCA, it can be useful to plot the PLS scores against each other to visualize the separation between classes.

```{r pls-scores, fig.height=2.5, fig.width=4, fig.align='center'}
pls_scores <- DataFrame(resultData(pls, 1, "scores"))

plot(pls_scores, C1 ~ C2, groups=class, pch=20)
```

Note that orthgonal PLS (O-PLS) is also available via `method="opls"` or by using the separate `OPLS()` method. Typically, both methods perform similarly, although O-PLS can sometimes produce more easily interpretable regression coefficients.


## Spatial shrunken centroids classification

Spatially-aware nearest shrunken centroids classification is an extension of nearest shrunken centroids that incorporates spatial information into the model.

Like in the clustering case of spatial shrunken centroids, a smoothing radius `r` must be provided along with sparsity parameters `s`.

```{r ssc-cv}
cv_ssc <- crossValidate(mse2, .y=class,
						.fun=spatialShrunkenCentroids,
						r=1, s=c(0,3,6,9), .fold=run(mse2))

summary(cv_ssc)
```

We can see that in this case, the fully dense model (`s=0`) that uses all mass features has the best cross-validated accuracy for the data.

```{r ssc-classification}
ssc2 <- spatialShrunkenCentroids(mse2, y=class, r=1, s=0)

summary(ssc2)
```

Plotting the predicted class probabilities produces a more easily interpretable visualization than PLS in this case.

```{r ssc-image-2, fig.height=2, fig.width=7}
image(ssc2, layout=c(1,3))
```

Plotting t-statistics shows the first three features have much higher abundance in condition "B".

```{r ssc-statistic-2, fig.height=2.5, fig.width=4, fig.align='center'}
plot(ssc2, values="statistic", lwd=2)
```

```{r ssc-top-2}
topFeatures(ssc2, class=="B") %>% subset(select=c(-diff, -r, -k, -s))
```








# Class comparison

Statistical hypothesis testing is used to determine whether the abundance of a feature is different between two or more conditions.

In order to account for additional factors like the effect of experimental runs, subject-to-subject variability, etc., this is often done most appropriately using linear models.

This example uses a simple experiment with two conditions "A" and "B", with three replicates in each condition.


```{r test-data, fig.height=5, fig.width=7}
set.seed(2020)
mse3 <- simulateImage(preset=4, npeaks=10, dim=c(10,10), sdnoise=0.3,
					nruns=3, peakdiff=1, representation="centroid")

trt <- makeFactor(A=mse3$circleA, B=mse3$circleB)

image(mse3, trt ~ x * y, key=TRUE, layout=c(2,3))
```

```{r test-image, fig.height=5, fig.width=7}
image(mse3, feature=1, layout=c(2,3))
```

We know from the design of the simulation that the first 5 (of 10) mass features differ between the conditions.

```{r test-diff}
featureData(mse3)
```

## Group means-based testing

Use `meansTest()` to fit linear models with the most basic summarization. The `groups` indicating the observational units must be provided. Each group is summarized by its mean, and then a linear model is fit to the summaries. *The number of groups is the effective sample size.*

Here, we specify `condition` as the sole fixed effect. Internally, the model will call either `lm()` or `lme()` depending on whether any random effects are provided.

```{r test-mean-test}
mtest <- meansTest(mse3, ~ condition, groups=run(mse3))

summary(mtest)
```

By default, the models are summarized by performing likelihood ratio tests against the null model (with no fixed effects, retaining any random effects).

Box-and-whisker plots can be used to visualize the differences (if any) between the conditions.

```{r test-mean-plot, fig.height=3, fig.width=7}
plot(mtest, layout=c(2,5), ylab="intensity")
```

Use `topFeatures()` to extract the significant results.

```{r test-mean-top}
topFeatures(mtest, p.adjust="fdr", AdjP < .05)
```


## Segmentation-based testing

Testing of `SpatialDGMM` objects is implemented by `segmentationTest()`. The key idea here is that spatial-DGMM segmentation captures within-sample heterogeneity, so testing between spatial-DGMM segments is more sensitive that simply summarizing a whole sample by its mean.

First, we must segment the data with `spatialDGMM()`, while making sure that each observational unit is segmented within a different group (as specified by `groups`).

*The number of groups is the effective sample size.*

```{r test-segment-dgmm}
dgmm2 <- spatialDGMM(mse3, r=1, k=5, groups=run(mse3))
```

Now use `segmentationTest()` to fit the models.

In order to fit the models, a representative spatial-DGMM segment must be selected for each group. There are two automated ways to do this via `classControl`: `"Ymax"` means use the segment with the highest mean, and `"Mscore"` means use the segment with the highest match score with the fixed effects.

(A list of character vectors giving the explicit mapping between group and representative spatial-DGMM segment can also be given to `classControl`.)

```{r test-segment-test}
stest <- segmentationTest(dgmm2, ~ condition, classControl="Ymax")

summary(stest)
```

As with `meansTest()`, the models are summarized by performing likelihood ratio tests against the null model (with no fixed effects, retaining any random effects).

Box-and-whisker plots can be used to visually compare the conditions.

```{r test-segment-plot, fig.height=3, fig.width=7}
plot(stest, layout=c(2,5), ylab="intensity")
```

If an automated method for `classControl` was used, it can be helpful to plot the mapping to see what segments were used to represent each group.

```{r test-segment-mapping}
image(stest, model=list(feature=3), values="mapping")
```

In this case, `segmentationTest()` finds two more significant mass features compared to `meansTest()`.

```{r test-segment-top}
topFeatures(stest, p.adjust="fdr", AdjP < .05) %>% subset(select=c(-diff, -r, -k))
```


# Session information

```{r session-info}
sessionInfo()
```



