## The Pasilla dataset

This dataset is available from the Pasilla Bioconductor library and is derived from the work from Brooks et al. (Conservation of an RNA regulatory map between Drosophila and mammals. Genome Research, 2010).

Alternative splicing is generally controlled by proteins that bind directly to regulatory sequence elements and either activate or repress splicing of adjacent splice sites in a target pre-mRNA. Here, the authors have combined RNAi and mRNA-seq to identify exons that are regulated by Pasilla (PS), the Drosophila melanogaster ortholog of the mammalian RNA-binding proteins NOVA1 and NOVA2.

To get the dataset, you need to install the pasilla library from Bioconductor then load this library.

## Install the library if needed then load it
if(!require("pasilla")){
source("http://bioconductor.org/biocLite.R")
biocLite("pasilla")
}
## Loading required package: pasilla
library("pasilla")

To get the path to the tabulated file containing count table use the command below. Here, the system.file() function is simply used to get the path to the directory containing the count table. The “pasilla_gene_counts.tsv” file contains counts for each gene (row) in each sample (column).

datafile <-  system.file( "extdata/pasilla_gene_counts.tsv", package="pasilla" )

## Read the data table
head(count.table)
##             untreated1 untreated2 untreated3 untreated4 treated1 treated2
## FBgn0000003          0          0          0          0        0        0
## FBgn0000008         92        161         76         70      140       88
## FBgn0000014          5          1          0          0        4        0
## FBgn0000015          0          2          1          2        1        0
## FBgn0000017       4664       8714       3564       3150     6205     3072
## FBgn0000018        583        761        245        310      722      299
##             treated3
## FBgn0000003        1
## FBgn0000008       70
## FBgn0000014        0
## FBgn0000015        0
## FBgn0000017     3334
## FBgn0000018      308
Some genes were not detected in any of the sample. Delete them from the count.table data.frame.

### Phenotypic data

The dataset contains RNA-Seq count data for RNAi treated or S2-DRSC untreated cells (late embryonic stage). Some results were obtained through single-end sequencing whereas others were obtained using paired-end sequencing. We will store these informations in two vectors (cond.type and lib.type).

cond.type <-  c( "untreated", "untreated", "untreated","untreated", "treated", "treated", "treated" )
lib.type   <-  c( "single-end", "single-end", "paired-end", "paired-end", "single-end", "paired-end", "paired-end" )    

Next, we will extract a subset of the data containing only paired-end samples.

## Select only Paired-end datasets
isPaired <-  lib.type == "paired-end"
show(isPaired) 
## [1] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE
count.table <-  count.table[ , isPaired ]  ## Select only the paired samples
head(count.table)
##             untreated3 untreated4 treated2 treated3
## FBgn0000003          0          0        0        1
## FBgn0000008         76         70       88       70
## FBgn0000014          0          0        0        0
## FBgn0000015          1          2        0        0
## FBgn0000017       3564       3150     3072     3334
## FBgn0000018        245        310      299      308
cond.type <-  cond.type[isPaired]
show(cond.type)
## [1] "untreated" "untreated" "treated"   "treated"

## Descriptive statistics

Before going further in the analysis, we will compute some descriptive statistics on the dataset.

• What are the dimensions of the count.table object.
• Use the summary fonction with count.table as argument. What kind of information are displayed ?
• Draw the distribution of the count.table data. Is that really informative ? Why ?
• Draw the distribution of the count.table data in logarithme base 2 (add a pseudo-count to avoid logarithmic transformation of zero values).
• Draw the boxplot of each column of count.table using the boxplot() function.
• Use the plotDensity() from the affy library (BioC) to plot density estimates of each column of the count.table data.frame.
• Use the pairs() to produce a matrix of scatterplot from the count.table object.
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## Creating a CountDataSet dataset

Now that we have all the required material, we will create a CountDataSet object (named cds) that will be used by DESeq to perform differential expression call. The CountDataSet has some important useful accessor methods (counts, conditions, estimateSizeFactors, sizeFactors, estimateDispersions and nbinomTest) that will be used later in this tutorial.

• Install and load the DESeq library (it should be installed from BioC).
• Create an object of class CountDataSet using the newCountDataSet() function and get some help about this object.
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## Normalization

The normalization procedure (RLE) is implemented through the estimateSizeFactors function.

### How is it computed ?

From DESeq help files: Given a matrix or data frame of count data, this function estimates the size factors as follows: Each column is divided by the geometric means of the rows. The median (or, if requested, another location estimator) of these ratios (skipping the genes with a geometric mean of zero) is used as the size factor for this column.

• Create a new object cds.norm that will contain normalized data. The method associated with normalization for the CountDataSet class is estimateSizeFactors().
• Implement a function that will compute the size factors for each sample.
• Now, check that the results obtained with your function are the same as those produced by DESeq (you can get the size factors from a CountDataSet object using the sizeFactors() function.
• Check the distribution before and after normalization using the boxplot() and plotDensity() functions. Do you see some differences. Does the count look more balanced across samples ?
Solution| Hide solution

## Differential expression

In this section we will search for genes whose expression is affected by the si-RNA treatment.

### Some intuition about the problematic

Let say that we would produce a lot of RNA-Seq experiments from the same samples (technical replicates). For each gene $$g$$ the measured read counts would be expected to vary rather slighlty around the expected mean and would be probably well-modeled using a poisson distribution. However, when working with biological replicates more variations are intrinsically expected. Indeed, due to sample purity, cell-synchronization issues or reponses to environment (e.g. heat-shock) the measured expression values for each genes are expected to fluctuate more importantly. The poisson distribution has only one parameter $$\lambda$$ and the mean and variance of the distribution are both equal to $$\lambda$$. Thus in most cases, the poisson distribution is not expected to fit very well with the count distribution since some over-dispersion (greater variability) due to biological noise is expected. As a consequence, when working with RNA-Seq data, many of the current approaches for differential expression call rely on the negative binomial distribution (note that this hold true also for other -Seq approaches, e.g. ChIP-Seq with replicates).

### What is the negative binomial?

The negative binomial distribution is a discrete distribution that can be used to model over-dispersed data (in this case this overdispersion is relative to the poisson model). There are two ways to parametrize the negative binomial distribution. The negative binomial distribution is a discrete distribution that can be used to model over-dispersed data (in this case this overdispersion is relative to the poisson model). There are two ways to parametrize the negative binomial distribution.

#### The probability of $$x$$ failures before $$n$$ success

First, given a Bernouilli trial with a probability $$p$$ of success, the negative binomial distribution describes the probability of observing $$x$$ failures before a target number of successes $$n$$ is reached. In this case the parameters of the distribution will thus be $$p$$, $$n$$ (in dnbinom() function of R, $$n$$ and $$p$$ are denoted by arguments size and prob respectively).

$P_{NegBin}(x; n, p) = \binom{x+n-1}{x}\cdot p^n \cdot (1-p)^x = C^{x}_{x+n-1}\cdot p^n \cdot (1-p)^x$

In this formula, $$p^n$$ denotes the probability to observe $$n$$ successes, $$(1-p)^x$$ the probability of $$x$$ failures, and the binomial coefficient $$C^{x}_{x+n-1}$$ indicates the number of possible ways to dispose $$x$$ failures among the $$x+n-1$$ trials that precede the last one (the problem statement imposes for the last trial to be a success).

The negative binomial distribution has expected value $$n\frac{q}{p}$$ and variance $$n\frac{q}{p^2}$$. Some examples of using this distribution in R are provided below.

Particular case: when $$n=1$$ the negative binomial corresponds to the the geometric distribution, which models the probability distribution to observe the first success after $$x$$ failures: $$P_{NegBin}(x; 1, p) = P_{geom}(x; p) = p \cdot (1-p)^x$$.

par(mfrow=c(1,1))
## Some intuition about the negative binomial parametrized using n and p.
## The simple case, one success (see geometric distribution)
# Let's have a look at the density
p <- 1/6 # the probability of success
n <- 1   # target for number of successful trials

# The density function
plot(0:10, dnbinom(0:10, n, p), type="h", col="blue", lwd=2)