This document provides three examples of how to use epca to explore your data.

Example 1: simulated data

The first example shows how to call the two key functions: sca and sma. Let's simulate a rank-5 data matrix with some additive Gaussian noise.

n <- 300
p <- 50
k <- 5
Z <- shrinkage(svd(matrix(runif(n * k), n, k))\$u, gamma = sqrt(n))
B <- diag(5) * 3
Y <- shrinkage(svd(matrix(runif(p * k), p, k))\$u, gamma = sqrt(p))
E <- matrix(rnorm(n * p, sd = 0.01), n, p)
X <- scale(Z %*% B %*% t(Y) + E)

In the above code, matrices Z and Y are rank-5 and sparse. The shrinkage() function performs a soft-thresholding by default.

Now, apply sca to find 5 sparse PCs.

s.sca <- sca(X, k = 5)
s.sca
## Call:sca(A = X, k = 5)
##
##
## Cumulative proportion of variance explained (CPVE):
##                      CPVE
## First component:    0.095
## First 2 components: 0.188
## First 3 components: 0.277
## First 4 components: 0.365
## First 5 components: 0.394

Similarly, we can do sparse matrix decomposition.

s.sma <- sma(X, k = 5)
s.sma
## Call: sma(A = X, k = 5)
##
##
## Num. non-zero Z's:  171 174 168 177 225
## Num. non-zero Y's:  28 27 24 30 26
## Abs. sum Z's:  9.3834
## Abs. sum Y's:  3.352972

Example 2: pitprops data

For the second example example, we use the pitprops data.

We apply the sca function to find k = 3 sparse PCs with sparsity parameter gamma = 4.5. Here, the sparsity parameter (gamma) controls the L1 norm of the returned PC loadings. The default of gamma (if absent) is sqrt(p * k), where p is the number of original variables.

data("pitprops", package = "epca")
s.sca <- sca(pitprops, k = 3, gamma = 4.5)
print(s.sca, verbose = TRUE)
## Call:sca(A = pitprops, k = 3, gamma = 4.5)
##
##
## Cumulative proportion of variance explained (CPVE):
##                      CPVE
## First component:    0.349
## First 2 components: 0.605
## First 3 components: 0.768
##
##  Component  1 :
##
## 1 topdiam 0.343
## 2 length  0.345
## 3 ovensg  -0.143
## 4 ringbut 0.07
## 5 bowmax  0.209
## 6 bowdist 0.294
## 7 whorls  0.212
## 8 knots   -0.079
##
##  Component  2 :
##
## 1 testsg  0.008
## 2 ovensg  0.353
## 3 ringtop 0.382
## 4 ringbut 0.273
## 5 whorls  0.051
## 6 clear   -0.077
## 7 knots   -0.049
## 8 diaknot -0.339
##
##  Component  3 :
##
## 1 topdiam 0.028
## 2 moist   0.507
## 3 testsg  0.537
## 4 ringtop 0.052
## 5 bowmax  -0.088
## 6 knots   0.06

Here, option verbose = TRUE prints, for each PC, the original variable with non-zero loadings.

The followings find 6 sparse PCs with gamma = 6. Note that the sparsity parameter for

s.sca <- sca(pitprops, 6, gamma = 6)
print(s.sca, verbose = TRUE)
## Call:sca(A = pitprops, k = 6, gamma = 6)
##
##
## Cumulative proportion of variance explained (CPVE):
##                      CPVE
## First component:    0.250
## First 2 components: 0.397
## First 3 components: 0.482
## First 4 components: 0.600
## First 5 components: 0.722
## First 6 components: 0.839
##
##  Component  1 :
##
## 1 topdiam 0.333
## 2 length  0.336
## 3 ovensg  -0.18
## 4 bowmax  0.017
## 5 bowdist 0.22
## 6 whorls  0.078
##
##  Component  2 :
##
## 1 moist   0.498
## 2 testsg  0.527
##
##  Component  3 :
##
## 1 whorls  -0.087
## 2 clear   0.761
##
##  Component  4 :
##
## 1 ovensg  -0.283
## 2 bowmax  -0.058
## 3 knots   0.633
##
##  Component  5 :
##
## 1 ovensg  0.142
## 2 ringtop 0.531
## 3 ringbut 0.246
## 4 bowmax  -0.171
##
##  Component  6 :
##
## 1 bowmax  -0.24
## 2 whorls  -0.034
## 3 diaknot 0.625

Example 3: single-cell RNA-seq data

This example shows a large-scale application of sparse PCA to a single-cell RNA-seq data. For this example, we use the human/mouse pancreas single-cell RNA-seq data from Baron et al. (2017).

Fe used the single-cell RNA-seq data with the scRNAseq package. We removed the genes that do not have any variation across samples (i.e., zero standard deviation) and the cell types that contain fewer than 100 cells. This resulted in a sparse data matrix pancreas of 17499 genes (rows) and 8451 cells (columns) across nine cell types.

dat <- BaronPancreasData("human")
gene.select <- !!apply(counts(dat), 1, sd)
label.select <- colData(dat) %>% data.frame() %>% dplyr::count(label) %>% filter(n >
100)
dat1 <- dat[gene.select, colData(dat)\$label %in% label.select\$label]

For SCA, we use the expression count matrix (count) as the input, where count[i,j] is the expression level of gene j in cell i, with 10.8\% being non-zero.

count <- counts(dat1)

The dataset contains labels for each cell.

label <- setNames(factor(dat1\$label), colnames(dat1))

Next, We applied sca to the transpose of count to find k = 9 sparse gene PCs. Aiming for a small number of genes (i.e., non-zero loadings) in individual PCs, we set the sparsity parameter to gamma = log(pk), which is approximately 12.

scar <- sca(t(count), k = 9, gamma = 12, center = F, scale = F, epsilon = 0.001)

We can exam the number of original genes included by each gene PC.

n.gene
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9
##   1   1   1   8  15   1   1   3  61

Each gene PC uses a handful of original genes.

We can plot the component scores of the nine PCs, with dplyr and ggplot2 packages. Each panel displays one of nine cell types with the names of cell types and the number of cells reported on the top strips. For each cell type, a box depicts the component scores for nine sparse gene PCs.

scar\$scores %>% reshape2::melt(varnames = c("cell", "PC"), value.name = "scores") %>%
mutate(PC = factor(PC), label = label[cell]) %>% ggplot(aes(PC, scores/1000,
fill = PC)) + geom_boxplot(color = "grey30", outlier.shape = NA, show.legend = FALSE) +
labs(x = "gene PC", y = bquote("scores (" ~ 10^3 ~ ")")) + scale_x_discrete(labels = 1:9) +
facet_wrap(~label, nrow = 3) + scale_fill_brewer(palette = "Set3") + theme_classic() We observed that most of the gene PCs consist of one or a handful of genes, yet the component scores showed that these PCs distinguish different cell types effectively . For example, the PC 2 consists of only one gene (named SST), and the expression of the gene marks the “delta” cells among others. This result highlights power of scRNA-seq in capture cell-type specific information and suggests the applicability of our methods to biological data.