# 3 PCA on the `multidrug`

study

To illustrate PCA and is variants, we will analyse the `multidrug`

case study available in the package. This pharmacogenomic study investigates the patterns of drug activity in cancer cell lines (Szakács et al. 2004). These cell lines come from the NCI-60 Human Tumor Cell Lines established by the Developmental Therapeutics Program of the National Cancer Institute (NCI) to screen for the toxicity of chemical compound repositories in diverse cancer cell lines. NCI-60 includes cell lines derived from cancers of colorectal (7 cell lines), renal (8), ovarian (6), breast (8), prostate (2), lung (9) and central nervous system origin (6), as well as leukemia (6) and melanoma (8).

Two separate data sets (representing two types of measurements) on the same NCI-60 cancer cell lines are available in `multidrug`

(see also `?multidrug`

):

`$ABC.trans`

: Contains the expression of 48 human ABC transporters measured by quantitative real-time PCR (RT-PCR) for each cell line.`$compound`

: Contains the activity of 1,429 drugs expressed as GI50, which is the drug concentration that induces 50% inhibition of cellular growth for the tested cell line.

Additional information will also be used in the outputs:

`$comp.name`

: The names of the 1,429 compounds.`$cell.line`

: Information on the cell line names (`$Sample`

) and the cell line types (`$Class`

).

In this activity, we illustrate PCA performed on the human ABC transporters `ABC.trans`

, and sparse PCA on the compound data `compound`

.

## 3.1 Load the data

The input data matrix \(\boldsymbol{X}\) is of size \(N\) samples in rows and \(P\) variables (e.g. genes) in columns. We start with the `ABC.trans`

data.

```
library(mixOmics)
data(multidrug)
<- multidrug$ABC.trans
X dim(X) # Check dimensions of data
```

`## [1] 60 48`

## 3.2 Example: PCA

### 3.2.1 Choose the number of components

Contrary to the minimal code example, here we choose to also scale the variables for the reasons detailed earlier. The function `tune.pca()`

calculates the cumulative proportion of explained variance for a large number of principal components (here we set `ncomp = 10`

). A screeplot of the proportion of explained variance relative to the total amount of variance in the data for each principal component is output (Fig. 3.1):

```
<- tune.pca(X, ncomp = 10, scale = TRUE)
tune.pca.multi plot(tune.pca.multi)
```

`# tune.pca.multidrug$cum.var # Outputs cumulative proportion of variance`

From the numerical output (not shown here), we observe that the first two principal components explain 22.87% of the total variance, and the first three principal components explain 29.88% of the total variance. The rule of thumb for choosing the number of components is not so much to set a hard threshold based on the cumulative proportion of explained variance (as this is data-dependent), but to observe when a drop, or elbow, appears on the screeplot. The elbow indicates that the remaining variance is spread over many principal components and is not relevant in obtaining a low dimensional ‘snapshot’ of the data. This is an empirical way of choosing the number of principal components to retain in the analysis. In this specific example we could choose between 2 to 3 components for the final PCA, however these criteria are highly subjective and the reader must keep in mind that visualisation becomes difficult above three dimensions.

### 3.2.2 PCA with fewer components

Based on the preliminary analysis above, we run a PCA with three components. Here we show additional input, such as whether to center or scale the variables.

```
<- pca(X, ncomp = 3, center = TRUE, scale = TRUE)
final.pca.multi # final.pca.multi # Lists possible outputs
```

The output is similar to the tuning step above. Here the total variance in the data is:

`$var.tot final.pca.multi`

`## [1] 47.98305`

By summing the variance explained from all possible components, we would achieve the same amount of explained variance. The proportion of explained variance per component is:

`$prop_expl_var$X final.pca.multi`

```
## PC1 PC2 PC3
## 0.12677541 0.10194929 0.07011818
```

The cumulative proportion of variance explained can also be extracted (as displayed in Figure 3.1):

`$cum.var final.pca.multi`

```
## PC1 PC2 PC3
## 0.1267754 0.2287247 0.2988429
```

### 3.2.3 Identify the informative variables

To calculate components, we use the variable coefficient weights indicated in the loading vectors. Therefore, the absolute value of the coefficients in the loading vectors inform us about the importance of each variable in contributing to the definition of each component. We can extract this information through the `selectVar()`

function which ranks the most important variables in decreasing order according to their absolute loading weight value for each principal component.

```
# Top variables on the first component only:
head(selectVar(final.pca.multi, comp = 1)$value)
```

```
## value.var
## ABCE1 0.3242162
## ABCD3 0.2647565
## ABCF3 0.2613074
## ABCA8 -0.2609394
## ABCB7 0.2493680
## ABCF1 0.2424253
```

Note:

*Here the variables are not selected (all are included), but ranked according to their importance in defining each component.*

### 3.2.4 Sample plots

We project the samples into the space spanned by the principal components to visualise how the samples cluster and assess for biological or technical variation in the data. We colour the samples according to the cell line information available in `multidrug$cell.line$Class`

by specifying the argument `group`

(Fig. 3.2).

```
plotIndiv(final.pca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
```

Because we have run PCA on three components, we can examine the third component, either by plotting the samples onto the principal components 1 and 3 (PC1 and PC3) in the code above (`comp = c(1, 3)`

) or by using the 3D interactive plot (code shown below). The addition of the third principal component only seems to highlight a potential outlier (sample 8, not shown). Potentially, this sample could be removed from the analysis, or, noted when doing further downstream analysis. The removal of outliers should be exercised with great caution and backed up with several other types of analyses (e.g. clustering) or graphical outputs (e.g. boxplots, heatmaps, etc).

```
# Interactive 3D plot will load the rgl library.
plotIndiv(final.pca.multi, style = '3d',
group = multidrug$cell.line$Class,
title = 'ABC transporters, PCA comp 1 - 3')
```

These plots suggest that the largest source of variation explained by the first two components can be attributed to the melanoma cell line, while the third component highlights a single outlier sample. Hence, the interpretation of the following outputs should primarily focus on the first two components.

Note:

*Had we not scaled the data, the separation of the melanoma cell lines would have been more obvious with the addition of the third component, while PC1 and PC2 would have also highlighted the sample outliers 4 and 8. Thus, centering and scaling are important steps to take into account in PCA.*

### 3.2.5 Variable plot: correlation circle plot

Correlation circle plots indicate the contribution of each variable to each component using the `plotVar()`

function, as well as the correlation between variables (indicated by a ‘cluster’ of variables). Note that to interpret the latter, the variables need to be centered and scaled in PCA:

```
plotVar(final.pca.multi, comp = c(1, 2),
var.names = TRUE,
cex = 3, # To change the font size
# cutoff = 0.5, # For further cutoff
title = 'Multidrug transporter, PCA comp 1 - 2')
```

The plot in Figure 3.3 highlights a group of ABC transporters that contribute to PC1: ABCE1, and to some extent the group clustered with ABCB8 that contributes positively to PC1, while ABCA8 contributes negatively. We also observe a group of transporters that contribute to both PC1 and PC2: the group clustered with ABCC2 contributes positively to PC2 and negatively to PC1, and a cluster of ABCC12 and ABCD2 that contributes negatively to both PC1 and PC2. We observe that several transporters are inside the small circle. However, examining the third component (argument `comp = c(1, 3)`

) does not appear to reveal further transporters that contribute to this third component. The additional argument `cutoff = 0.5`

could further simplify this plot.

### 3.2.6 Biplot: samples and variables

A biplot allows us to display both samples and variables simultaneously to further understand their relationships. Samples are displayed as dots while variables are displayed at the tips of the arrows. Similar to correlation circle plots, data must be centered and scaled to interpret the correlation between variables (as a cosine angle between variable arrows).

```
biplot(final.pca.multi, group = multidrug$cell.line$Class,
legend.title = 'Cell line')
```

The biplot in Figure 3.4 shows that the melanoma cell lines seem to be characterised by a subset of transporters such as the cluster around ABCC2 as highlighted previously in Figure 3.3. Further examination of the data, such as boxplots (as shown in Fig. 3.5), can further elucidate the transporter expression levels for these specific samples.

```
<- scale(X[, 'ABCC2'], center = TRUE, scale = TRUE)
ABCC2.scale
boxplot(ABCC2.scale ~
$cell.line$Class, col = color.mixo(1:9),
multidrugxlab = 'Cell lines', ylab = 'Expression levels, scaled',
par(cex.axis = 0.5), # Font size
main = 'ABCC2 transporter')
```

## 3.3 Example: sparse PCA

In the `ABC.trans`

data, there is only one missing value. Missing values can be handled by sPCA via the NIPALS algorithm . However, if the number of missing values is large, we recommend imputing them with NIPALS, as we describe in our website in www.mixOmics.org.

### 3.3.1 Choose the number of variables to select

First, we must decide on the number of components to evaluate. The previous tuning step indicated that `ncomp = 3`

was sufficient to explain most of the variation in the data, which is the value we choose in this example. We then set up a grid of `keepX`

values to test, which can be thin or coarse depending on the total number of variables. We set up the grid to be thin at the start, and coarse as the number of variables increases. The `ABC.trans`

data includes a sufficient number of samples to perform repeated 5-fold cross-validation to define the number of folds and repeats (leave-one-out CV is also possible if the number of samples \(N\) is small by specifying `folds =`

\(N\)). The computation may take a while if you are not using parallelisation (see additional parameters in `tune.spca()`

), here we use a small number of repeats for illustrative purposes. We then plot the output of the tuning function.

```
<- c(seq(5, 30, 5))
grid.keepX # grid.keepX # To see the grid
set.seed(30) # For reproducibility with this handbook, remove otherwise
<- tune.spca(X, ncomp = 3,
tune.spca.result folds = 5,
test.keepX = grid.keepX, nrepeat = 10)
# Consider adding up to 50 repeats for more stable results
$choice.keepX tune.spca.result
```

```
## comp1 comp2 comp3
## 25 30 30
```

`plot(tune.spca.result)`

The tuning function outputs the averaged correlation between predicted and actual components per `keepX`

value for each component. It indicates the optimal number of variables to select for which the averaged correlation is maximised on each component. Figure 3.6 shows that this is achieved when selecting 25 transporters on the first component, and 30 on the second. Given the drop in values in the averaged correlations for the third component, we decide to only retain two components.

Note:

*If the tuning results suggest a large number of variables to select that is close to the total number of variables, we can arbitrarily choose a much smaller selection size.*

### 3.3.2 Final sparse PCA

Based on the tuning above, we perform the final sPCA where the number of variables to select on each component is specified with the argument `keepX`

. Arbitrary values can also be input if you would like to skip the tuning step for more exploratory analyses:

```
# By default center = TRUE, scale = TRUE
<- tune.spca.result$choice.keepX[1:2]
keepX.select
<- spca(X, ncomp = 2, keepX = keepX.select)
final.spca.multi
# Proportion of explained variance:
$prop_expl_var$X final.spca.multi
```

```
## PC1 PC2
## 0.1201529 0.1040587
```

Overall when considering two components, we lose approximately 0.5 % of explained variance compared to a full PCA, but the aim of this analysis is to identify key transporters driving the variation in the data, as we show below.

### 3.3.3 Sample and variable plots

We first examine the sPCA sample plot:

```
plotIndiv(final.spca.multi,
comp = c(1, 2), # Specify components to plot
ind.names = TRUE, # Show row names of samples
group = multidrug$cell.line$Class,
title = 'ABC transporters, sPCA comp 1 - 2',
legend = TRUE, legend.title = 'Cell line')
```

In Figure 3.7, component 2 in sPCA shows clearer separation of the melanoma samples compared to the full PCA. Component 1 is similar to the full PCA. Overall, this sample plot shows that little information is lost compared to a full PCA.

A biplot can also be plotted that only shows the selected transporters (Figure 3.8):

```
biplot(final.spca.multi, group = multidrug$cell.line$Class,
legend =FALSE)
```

The correlation circle plot highlights variables that contribute to component 1 and component 2 (Fig. 3.9):

```
plotVar(final.spca.multi, comp = c(1, 2), var.names = TRUE,
cex = 3, # To change the font size
title = 'Multidrug transporter, sPCA comp 1 - 2')
```

The transporters selected by sPCA are amongst the most important ones in PCA. Those coloured in green in Figure 3.3 (ABCA9, ABCB5, ABCC2 and ABCD1) show an example of variables that contribute positively to component 2, but with a larger weight than in PCA. Thus, they appear as a clearer cluster in the top part of the correlation circle plot compared to PCA. As shown in the biplot in Figure 3.8, they contribute in explaining the variation in the melanoma samples.

We can extract the variable names and their positive or negative contribution to a given component (here 2), using the `selectVar()`

function:

```
# On the first component, just a head
head(selectVar(final.spca.multi, comp = 2)$value)
```

```
## value.var
## ABCA9 0.3744148
## ABCB5 0.3690872
## ABCC2 0.3639616
## ABCD1 0.3418706
## ABCD2 -0.2581438
## ABCA5 0.2536514
```

The loading weights can also be visualised with `plotLoading()`

, where variables are ranked from the least important (top) to the most important (bottom) in Figure 3.10). Here on component 2:

`plotLoadings(final.spca.multi, comp = 2)`