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====== PCA ======
[[https://www.theanalysisfactor.com/the-fundamental-difference-between-principal-component-analysis-and-factor-analysis/|Difference between PCA and FA]] 
  * Both are data reduction techniques — they allow you to capture the variance in variables in a smaller set.
  * Both are usually run in stat software using the same procedure, and the output looks pretty much the same.
  * The steps you take to run them are the same-extraction, interpretation, rotation, choosing the number of factors or components.
  * Despite all these similarities, there is a fundamental difference between them: PCA is a linear combination of variables; Factor Analysis is a measurement model of a latent variable.
====== Some useful lectures ======

{{youtube>FgakZw6K1QQ}}
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{{youtube>g-Hb26agBFg}} 
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{{youtube>0Jp4gsfOLMs}} 
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{{pca_gsp.csv}}
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<code>## In this example, the data is in a matrix called
## data.matrix
## columns are individual samples (i.e. cells)
## rows are measurements taken for all the samples (i.e. genes)
## Just for the sake of the example, here's some made up data...
data.matrix <- matrix(nrow=100, ncol=10)
colnames(data.matrix) <- c(
  paste("wt", 1:5, sep=""),
  paste("ko", 1:5, sep=""))
rownames(data.matrix) <- paste("gene", 1:100, sep="")
for (i in 1:100) {
  wt.values <- rpois(5, lambda=sample(x=10:1000, size=1))
  ko.values <- rpois(5, lambda=sample(x=10:1000, size=1))
 
  data.matrix[i,] <- c(wt.values, ko.values)
}
head(data.matrix)
dim(data.matrix)
 
pca <- prcomp(t(data.matrix), scale=TRUE) 
 
## plot pc1 and pc2
plot(pca$x[,1], pca$x[,2])
 
## make a scree plot
pca.var <- pca$sdev^2
pca.var.per <- round(pca.var/sum(pca.var)*100, 1)
 
barplot(pca.var.per, main="Scree Plot", xlab="Principal Component", ylab="Percent Variation")
 
## now make a fancy looking plot that shows the PCs and the variation:
library(ggplot2)
 
pca.data <- data.frame(Sample=rownames(pca$x),
  X=pca$x[,1],
  Y=pca$x[,2])
pca.data
 
ggplot(data=pca.data, aes(x=X, y=Y, label=Sample)) +
  geom_text() +
  xlab(paste("PC1 - ", pca.var.per[1], "%", sep="")) +
  ylab(paste("PC2 - ", pca.var.per[2], "%", sep="")) +
  theme_bw() +
  ggtitle("My PCA Graph")
 
## get the name of the top 10 measurements (genes) that contribute
## most to pc1.
loading_scores <- pca$rotation[,1]
gene_scores <- abs(loading_scores) ## get the magnitudes
gene_score_ranked <- sort(gene_scores, decreasing=TRUE)
top_10_genes <- names(gene_score_ranked[1:10])
 
top_10_genes ## show the names of the top 10 genes
 
pca$rotation[top_10_genes,1] ## show the scores (and +/- sign)
 
#######
##
## NOTE: Everything that follow is just bonus stuff.
## It simply demonstrates how to get the same
## results using "svd()" (Singular Value Decomposition) or using "eigen()"
## (Eigen Decomposition).
##
#######
 
############################################
##
## Now let's do the same thing with svd()
##
## svd() returns three things
## v = the "rotation" that prcomp() returns, this is a matrix of eigenvectors
##     in other words, a matrix of loading scores
## u = this is similar to the "x" that prcomp() returns. In other words,
##     sum(the rotation * the original data), but compressed to the unit vector
##     You can spread it out by multiplying by "d"
## d = this is similar to the "sdev" value that prcomp() returns (and thus
##     related to the eigen values), but not
##     scaled by sample size in an unbiased way (ie. 1/(n-1)).
##     For prcomp(), sdev = sqrt(var) = sqrt(ss(fit)/(n-1))
##     For svd(), d = sqrt(ss(fit))
##
############################################
 
svd.stuff <- svd(scale(t(data.matrix), center=TRUE))
 
## calculate the PCs
svd.data <- data.frame(Sample=colnames(data.matrix),
  X=(svd.stuff$u[,1] * svd.stuff$d[1]),
  Y=(svd.stuff$u[,2] * svd.stuff$d[2]))
svd.data
 
## alternatively, we could compute the PCs with the eigen vectors and the
## original data
svd.pcs <- t(t(svd.stuff$v) %*% t(scale(t(data.matrix), center=TRUE)))
svd.pcs[,1:2] ## the first to principal components
 
svd.df <- ncol(data.matrix) - 1
svd.var <- svd.stuff$d^2 / svd.df
svd.var.per <- round(svd.var/sum(svd.var)*100, 1)
 
ggplot(data=svd.data, aes(x=X, y=Y, label=Sample)) +
  geom_text() +
  xlab(paste("PC1 - ", svd.var.per[1], "%", sep="")) +
  ylab(paste("PC2 - ", svd.var.per[2], "%", sep="")) +
  theme_bw() +
  ggtitle("svd(scale(t(data.matrix), center=TRUE)")
 
############################################
##
## Now let's do the same thing with eigen()
##
## eigen() returns two things...
## vectors = eigen vectors (vectors of loading scores)
##           NOTE: pcs = sum(loading scores * values for sample)
## values = eigen values
##
############################################
cov.mat <- cov(scale(t(data.matrix), center=TRUE))
dim(cov.mat)
 
## since the covariance matrix is symmetric, we can tell eigen() to just
## work on the lower triangle with "symmetric=TRUE"
eigen.stuff <- eigen(cov.mat, symmetric=TRUE)
dim(eigen.stuff$vectors)
head(eigen.stuff$vectors[,1:2])
 
eigen.pcs <- t(t(eigen.stuff$vectors) %*% t(scale(t(data.matrix), center=TRUE)))
eigen.pcs[,1:2]
 
eigen.data <- data.frame(Sample=rownames(eigen.pcs),
  X=(-1 * eigen.pcs[,1]), ## eigen() flips the X-axis in this case, so we flip it back
  Y=eigen.pcs[,2]) ## X axis will be PC1, Y axis will be PC2
eigen.data
 
eigen.var.per <- round(eigen.stuff$values/sum(eigen.stuff$values)*100, 1)
 
ggplot(data=eigen.data, aes(x=X, y=Y, label=Sample)) +
  geom_text() +
  xlab(paste("PC1 - ", eigen.var.per[1], "%", sep="")) +
  ylab(paste("PC2 - ", eigen.var.per[2], "%", sep="")) +
  theme_bw() +
  ggtitle("eigen on cov(t(data.matrix))")
</code>

====== e.g. saq ======
SPSS Anxiety Questionnaire
{{:r:saq8.csv}}



<code>
# saq <- read.csv("http://commres.net/wiki/_media/r/saq.csv", header = T)
saq8 <- read.csv("http://commres.net/wiki/_media/r/saq8.csv", header = T)
head(saq8)
saq8 <- saq8[c(-1)]
</code>

<code>
> round(cor(saq8),3)
              stat_cry afraid_spss sd_excite nmare_pearson du_stat lexp_comp comp_hate good_math
stat_cry         1.000      -0.099    -0.337         0.436   0.402     0.217     0.305     0.331
afraid_spss     -0.099       1.000     0.318        -0.112  -0.119    -0.074    -0.159    -0.050s
sd_excite       -0.337       0.318     1.000        -0.380  -0.310    -0.227    -0.382    -0.259
nmare_pearson    0.436      -0.112    -0.380         1.000   0.401     0.278     0.409     0.349
du_stat          0.402      -0.119    -0.310         0.401   1.000     0.257     0.339     0.269
lexp_comp        0.217      -0.074    -0.227         0.278   0.257     1.000     0.514     0.223
comp_hate        0.305      -0.159    -0.382         0.409   0.339     0.514     1.000     0.297
good_math        0.331      -0.050    -0.259         0.349   0.269     0.223     0.297     1.000
> 
</code>

<code>
install.packages("Hmisc")
library(Hmisc)
saq8.rcorr <- rcorr(as.matrix(saq8))
 
print(saq8.rcorr$r, digits = 3)
</code>