StatQuest: Random Forests Part 2: Missing data and clustering

Advertisements

StatQuest: PCA in Python

## NOTE: This is Python 3 code.
import pandas as pd
import numpy as np
import random as rd
from sklearn.decomposition import PCA
from sklearn import preprocessing
import matplotlib.pyplot as plt # NOTE: This was tested with matplotlib v. 2.1.0

#########################
#
# Data Generation Code
#
#########################
## In this example, the data is in a data frame called data.
## 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, we'll use made up data...
genes = ['gene' + str(i) for i in range(1,101)]

wt = ['wt' + str(i) for i in range(1,6)]
ko = ['ko' + str(i) for i in range(1,6)]

data = pd.DataFrame(columns=[*wt, *ko], index=genes)

for gene in data.index:
    data.loc[gene,'wt1':'wt5'] = np.random.poisson(lam=rd.randrange(10,1000), size=5)
    data.loc[gene,'ko1':'ko5'] = np.random.poisson(lam=rd.randrange(10,1000), size=5)

print(data.head())
print(data.shape)

#########################
#
# Perform PCA on the data
#
#########################
# First center and scale the data
scaled_data = preprocessing.scale(data.T)

pca = PCA() # create a PCA object
pca.fit(scaled_data) # do the math
pca_data = pca.transform(scaled_data) # get PCA coordinates for scaled_data

#########################
#
# Draw a scree plot and a PCA plot
#
#########################

#The following code constructs the Scree plot
per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)
labels = ['PC' + str(x) for x in range(1, len(per_var)+1)]

plt.bar(x=range(1,len(per_var)+1), height=per_var, tick_label=labels)
plt.ylabel('Percentage of Explained Variance')
plt.xlabel('Principal Component')
plt.title('Scree Plot')
plt.show()

#the following code makes a fancy looking plot using PC1 and PC2
pca_df = pd.DataFrame(pca_data, index=[*wt, *ko], columns=labels)

plt.scatter(pca_df.PC1, pca_df.PC2)
plt.title('My PCA Graph')
plt.xlabel('PC1 - {0}%'.format(per_var[0]))
plt.ylabel('PC2 - {0}%'.format(per_var[1]))

for sample in pca_df.index:
    plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample]))

plt.show()

#########################
#
# Determine which genes had the biggest influence on PC1
#
#########################

## get the name of the top 10 measurements (genes) that contribute
## most to pc1.
## first, get the loading scores
loading_scores = pd.Series(pca.components_[0], index=genes)
## now sort the loading scores based on their magnitude
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)

# get the names of the top 10 genes
top_10_genes = sorted_loading_scores[0:10].index.values

## print the gene names and their scores (and +/- sign)
print(loading_scores[top_10_genes])<span 				data-mce-type="bookmark" 				id="mce_SELREST_start" 				data-mce-style="overflow:hidden;line-height:0" 				style="overflow:hidden;line-height:0" 			></span>

StatQuest: MDS and PCoA in R

Here’s the code:

library(ggplot2)
## 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)

###################################################################
##
## 1) Just for reference, draw a PCA plot using this data...
##
###################################################################
pca <- prcomp(t(data.matrix), scale=TRUE, center=TRUE) 

## calculate the percentage of variation that each PC accounts for...
pca.var <- pca$sdev^2
pca.var.per <- round(pca.var/sum(pca.var)*100, 1)
pca.var.per

## now make a fancy looking plot that shows the PCs and the variation:
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("PCA Graph")

###################################################################
##
## 2) Now draw an MDS plot using the same data and the Euclidean
##    distance. This graph should look the same as the PCA plot
##
###################################################################

## first, calculate the distance matrix using the Euclidian distance.
## NOTE: We are transposing, scaling and centering the data just like PCA.
distance.matrix <- dist(scale(t(data.matrix), center=TRUE, scale=TRUE),
  method="euclidean")

## do the MDS math (this is basically eigen value decomposition)
mds.stuff <- cmdscale(distance.matrix, eig=TRUE, x.ret=TRUE)

## calculate the percentage of variation that each MDS axis accounts for...
mds.var.per <- round(mds.stuff$eig/sum(mds.stuff$eig)*100, 1)
mds.var.per

## now make a fancy looking plot that shows the MDS axes and the variation:
mds.values <- mds.stuff$points
mds.data <- data.frame(Sample=rownames(mds.values),
  X=mds.values[,1],
  Y=mds.values[,2])
mds.data

ggplot(data=mds.data, aes(x=X, y=Y, label=Sample)) +
  geom_text() +
  theme_bw() +
  xlab(paste("MDS1 - ", mds.var.per[1], "%", sep="")) +
  ylab(paste("MDS2 - ", mds.var.per[2], "%", sep="")) +
  ggtitle("MDS plot using Euclidean distance")

###################################################################
##
## 3) Now draw an MDS plot using the same data and the average log(fold change)
##    This graph should look different than the first two
##
###################################################################

## first, take the log2 of all the values in the data.matrix.
## This makes it easy to compute log2(Fold Change) between a gene in two
## samples since...
##
## log2(Fold Change) = log2(value for sample 1) - log2(value for sample 2)
##
log2.data.matrix <- log2(data.matrix)

## now create an empty distance matrix
log2.distance.matrix <- matrix(0,
  nrow=ncol(log2.data.matrix),
  ncol=ncol(log2.data.matrix),
  dimnames=list(colnames(log2.data.matrix),
    colnames(log2.data.matrix)))

log2.distance.matrix

## now compute the distance matrix using avg(absolute value(log2(FC)))
for(i in 1:ncol(log2.distance.matrix)) {
  for(j in 1:i) {
    log2.distance.matrix[i, j] <-
      mean(abs(log2.data.matrix[,i] - log2.data.matrix[,j]))
  }
}
log2.distance.matrix

## do the MDS math (this is basically eigen value decomposition)
## cmdscale() is the function for "Classical Multi-Dimensional Scalign"
mds.stuff <- cmdscale(as.dist(log2.distance.matrix),
  eig=TRUE,
  x.ret=TRUE)

## calculate the percentage of variation that each MDS axis accounts for...
mds.var.per <- round(mds.stuff$eig/sum(mds.stuff$eig)*100, 1)
mds.var.per

## now make a fancy looking plot that shows the MDS axes and the variation:
mds.values <- mds.stuff$points
mds.data <- data.frame(Sample=rownames(mds.values),
  X=mds.values[,1],
  Y=mds.values[,2])
mds.data

ggplot(data=mds.data, aes(x=X, y=Y, label=Sample)) +
  geom_text() +
  theme_bw() +
  xlab(paste("MDS1 - ", mds.var.per[1], "%", sep="")) +
  ylab(paste("MDS2 - ", mds.var.per[2], "%", sep="")) +
  ggtitle("MDS plot using avg(logFC) as the distance")