---
title: "ChIPSeqSpike: ChIP-seq data scaling with spike-in control"
author: 
- name: Nicolas Descostes
  affiliation: Howard Hughes Medical Institute - New York University
  email: nicolas.descostes@gmail.com
package: ChIPSeqSpike
abstract: Vignette update - 2018-01-26
output: 
  BiocStyle::pdf_document
vignette: >
  %\VignetteIndexEntry{ChIPSeqSpike}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


# Introduction

Chromatin Immuno-Precipitation followed by Sequencing (ChIP-Seq) is used to 
determine the binding sites of any protein of interest, such as transcription 
factors or histones with or without a specific modification, at a genome scale 
[@barski2007; @park2009]. ChIP-Seq entails treating cells with a cross-linking 
reagent such as formaldehyde; isolating the chromatin and fragmenting it by 
 sonication; immuno-precipitating with antibodies directed against the protein 
 of interest; reversing crosslink; DNA purification and amplification before 
 submission to sequencing. These many steps can introduce biases that make 
 ChIP-Seq more qualitative than quantitative. Different efficiencies in nuclear
extraction, DNA sonication, DNA amplification or antibody recognition make it 
challenging to distinguish between true differential binding events and 
technical variability.

This problem was addressed by using an external spike-in control to keep track 
of technical biases between conditions [@orlando2014; @bonhoure2014]. 
Exogenous DNA from a different non-closely related species is inserted during 
the protocol to infer scaling factors. This modification was shown to be 
especially important for revealing global histone modification differences, 
that are not caught by traditional downstream data normalization techniques, 
such as Histone H3 lysine-27 trimethyl (H3K27me3) upon Ezh2 inhibitor treatment
 [@trojer2016].

ChIPSeqSpike provides tools for ChIP-Seq spike-in normalization, assessment and
 analysis. Conversely to a majority of ChIP-Seq related tools, ChIPSeqSpike 
does not rely on peak detection. However, if one wants to focus on peaks, 
ChIPSeqSpike is flexible enough to do so. ChIPSeqSpike provides ready to use 
scaled bigwig files as output and scaling factors values. We believe that 
ChIPSeqSpike will be of great value to the genomics community.


# Standard workflow
## Note for Windows users
On Windows operating system, reading of BigWig files fail due to the 
Bioconductor package [rtracklayer >= 1.37.6](https://bioconductor.org/packages/release/bioc/html/rtracklayer.html) 
that does not support this file format. Therefore, the boost mode, the input 
subtraction method, and scaling method do not work on this operating system.

## Quick start
A case study reported Chromatin Immuno-Precipitation followed by Sequencing 
(ChIP-Seq) experiments that did not show differences upon inhibitor treatment 
with traditional normalization procedures [@orlando2014].
These experiments looked at the effect of DOT1L inhibitor EPZ5676 [@daigle2013]
 treatment on the Histone H3 lysine-79 dimethyl (H3K79me2) modification in 
Jurkat cells. DOT1L is involved in the RNA Polymerase II pause release and 
licensing of transcriptional elongation. H3K79me2 ChIP-Seq were performed on 
cells treated with 0%, 50% and 100% EPZ5676 inhibitor (see next section for 
details on data).

The following code performs spike-in normalization with a wrapper function on 
data sub-samples. A 'test_chipseq' temporary results folder is also created.  
\newline
```{r message = FALSE, warning = FALSE}
library("ChIPSeqSpike")

## Preparing testing data
info_file_csv <- system.file("extdata/info.csv", package="ChIPSeqSpike")
bam_path <- system.file("extdata/bam_files", package="ChIPSeqSpike")
bigwig_path <- system.file("extdata/bigwig_files", package="ChIPSeqSpike")
gff_vec <- system.file("extdata/test_coord.gff", package="ChIPSeqSpike")
genome_name <- "hg19"
output_folder <- "test_chipseqspike"
bigwig_files <- system.file("extdata/bigwig_files", 
                            c("H3K79me2_0-filtered.bw",
                              "H3K79me2_100-filtered.bw",
                              "H3K79me2_50-filtered.bw",
                              "input_0-filtered.bw",
                              "input_100-filtered.bw",
                              "input_50-filtered.bw"), package="ChIPSeqSpike")

## Copying example files
dir.create("./test_chipseqspike")
mock <- file.copy(bigwig_files, "test_chipseqspike")

## Performing spike-in normalization
if (.Platform$OS.type != "windows") {
    csds_test <- spikePipe(info_file_csv, bam_path, bigwig_path, gff_vec, 
                           genome_name, outputFolder = output_folder)
}
```



## Data

The data used in this documentation represent a gold-standard example of the 
importance of using spike-in controls with ChIP-Seq experiments. It uses 
chromatin from Drosophila Melanogaster as exogenous spike-in control to correct
 experimental biases. Without a spike-in control and using only RPM 
normalization, proper differences in the H3K79me2 histone modification in human
 Jurkat cells upon EPZ5676 inhibitor treatment were not observed 
[@orlando2014]. 

This dataset is made of bigwig and bam files of H3K79me2 ChIP-Seq data and 
corresponding input DNA controls 
(see [input subtraction section](#inputSubtraction)).
Bam files contain data aligned to the Human reference genome Hg19 or to the 
Drosophila reference genome dm3. The latest is used to compute external 
spike-in scaling factors. All above mentioned data are available at 0%, 50% and
 100% EPZ5676 inhibitor treatment.

### Testing data

For the sake of memory and computation time efficiency, bigwig files used in 
this vignette are limited to chromosome 1. Reads falling in the top 10% mostly 
bound genes (at 0% treatment) with length between 700-800 bp were kept in bam 
files. For efficient plotting functions testing, only binding values of the top
 100 mostly bound genes are used and can be accessed with 
`data(result_extractBinding)`. Scores for factors and read counts were computed 
on the whole dataset and are available through 
`data(result_estimateScalingFactors)` (see below).

### Complete data

The whole dataset is accessible at 
[GSE60104](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60104). 
Specifically, the data used are H3K79me2 0% (GSM1465004), H3K79me2 50% 
(GSM1465006), H3K79me2 100% (GSM1465008), input DNA 0% (GSM1511465), input DNA 
50% (GSM1511467) and input DNA 100% (GSM1511469).

The data were treated as follows: Quality of sequencing was assessed with 
[FastQC v0.11.4](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). 
Reads having less than 80% of quality scores above 25 were removed with 
NGSQCToolkit v2.3.3 [@ngsqctoolkit]. Homo Sapiens Hg19 and Drosophila 
Melanogaster dm3 from Illumina igenomes UCSC Collection were used. ChIP-Seq 
data were aligned with Bowtie2 v2.1.0 [@bowtie2] with default parameters. Sam 
outputs were converted to Bam with Samtools v1.0.6 [@samtools] and sorted with 
[Picard tools v1.88](http://broadinstitute.github.io/picard). Data were further
 processed with Pasha v0.99.21 [@pasha]. Fixed steps wiggle files were 
converted to bigwigs with 
[wigToBigWig](http://hgdownload.soe.ucsc.edu/admin/exe/).

Results with the complete dataset are also provided in this documentation.


## Detailed operations

The spike-in normalization procedure consists of 4 steps: RPM scaling, input 
DNA subtraction, RPM scaling reversal and exogenous spike-in DNA scaling. Below
 is detailed the different steps included in the above mentioned wrapper 
function 'spikePipe'.

### Dataset generation

The different data necessary for proper spike-in scaling are provided in a csv 
or a tab separated txt file. The columns must contain proper names and are 
organized as follows: Experiment name (expName); bam file name of data aligned 
to the endogenous reference genome (endogenousBam); bam file name of data 
aligned to the exogenous reference genome (exogenousBam); the corresponding 
input DNA bam file aligned to the endogenous reference genome (inputBam); the 
fixed steps bigwig file name of data aligned to the endogenous reference genome
 (bigWigEndogenous) and the fixed steps bigwig file names of the corresponding 
input DNA experiment aligned to the endogenous reference genome (bigWigInput).
\newline
```{r message = FALSE, warning = FALSE}
info_file <- read.csv(info_file_csv)
head(info_file)
````

From the info file, two kinds of objects can be generated: either a 
ChIPSeqSpikeDataset or a ChIPSeqSpikeDatasetList depending upon the number of 
input DNA experiments. A ChIPSeqSpikeDatasetList object is a list of 
ChIPSeqSpikeDataset object that is created if several input DNA experiments are
 used. In this latter case, ChIP-Seq experiments are grouped by their 
corresponding input DNA. The function spikeDataset creates automatically the 
suitable object. The folder path to the bam and fixed steps bigwig files must 
be provided.
\newline
```{r message = FALSE, warning = FALSE}
csds_test <- spikeDataset(info_file_csv, bam_path, bigwig_path)
is(csds_test)
````


#### Boost mode

Reading and processing bigwig and bam files can be memory-greedy. By default, 
files are read, processed and written at each step of the normalization 
procedure to enable data treatment on a regular desktop computer. One could 
wish to reduce the time of computation especially when processing a lot of 
data. ChIPSeqSpike reduces such time by providing a boost mode.
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    csds_testBoost <- spikeDataset(info_file_csv, bam_path, bigwig_path, 
    boost = TRUE)
    is(csds_testBoost)
}
````
Binding scores for each experiment are stored in a GRanges object and are 
directly accessible by functions.
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    getLoadedData(csds_testBoost[[1]])
}
````
Even if optimizing greatly the time of computing, one should know that loading 
binding scores of all experiments is greedy in memory and should be used with 
caution. The boost mode is ignored in the rest of the vignette, but all code 
provided in the following sections, with the exception of section 3.1 (
plotTransform), can be run with csds_testBoost.

### Summary and control

A ChIPSeqSpikeDataset object, at this point, is made of slots storing paths to 
files. In order to compute scaling factors, bam counts are first computed. A 
scaling factor is defined as 1000000/bam_count. The method 
estimateScalingFactors returns bam counts and endogenous/exogenous scaling 
factors for all experiments. In the following example, scores are computed 
using chromosome 1 only. Scores on the whole dataset are also indicated below.
\newline
```{r message = FALSE, warning = FALSE}
csds_test <- estimateScalingFactors(csds_test, verbose = FALSE)
````
The different scores can be visualized:
\newline
```{r message = FALSE, warning = FALSE}
## Scores on testing sub-samples
spikeSummary(csds_test)

##Scores on whole dataset
data(result_estimateScalingFactors)
spikeSummary(csds)
````

An important parameter to keep in mind when performing spike-in with ChIP-seq 
is the percentage of exogenous DNA relative to that of endogenous DNA. The 
amount of exogenous DNA should be between 2-25% of endogenous DNA. The method 
getRatio returns the percentage of exogenous DNA and throws a warning if this 
percentage is not within the 2-25% range. In theory, having more than 25% 
exogenous DNA should not affect the normalization, whereas having less than 2% 
is usually not sufficient to perform a reliable normalization.
\newline
```{r message = FALSE}
getRatio(csds_test)

## Result on the whole dataset
data(ratio)
ratio
````

### RPM scaling

The first normalization applied to the data is the 'Reads Per Million' (RPM) 
mapped reads. The method 'scaling' is used to achieve such normalization using 
default parameters. It is also used to reverse the RPM normalization and apply 
exogenous scaling factors (see sections [2.3.5](#RPMreversal) and 
[2.3.6](#exoscaling)).
\newline
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    csds_test <- scaling(csds_test, outputFolder = output_folder)
}
````
 In the context of this vignette, output_folder is precised since the testing 
files were copied to a temporary 'test_chipseqspike' folder. The slots 
containing paths to files will be updated to this folder. If not precised, 
the RPM scaled bigwig files are written to the same folder containing bigwigs. 
This statement is also applicable for the next operations below.

### Input Subtraction {#inputSubtraction}

When Immuno-Precipitating (IP) DNA bound by a given protein, a control is 
needed to distinguish background noise from true signal. This is typically 
achieved by performing a mock IP, omitting the use of antibody. After mock IP 
sequencing, one can notice peaks of signal above background. These peaks have 
to be removed from the experiment since they represent false positives. The 
inputSubtraction method simply subtracts scores of the input DNA experiment 
from the corresponding ones. If in boost mode, the input subtracted values are 
stored in the dataset object and no files are written. For this latter case, 
the method exportBigWigs can be used to output the transformed files.
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    csds_test <- inputSubtraction(csds_test)
}
````

### RPM scaling reversal {#RPMreversal}

After RPM and input subtraction normalization, the RPM normalization is 
reversed in order for the data to be normalized by the exogenous scaling 
factors.
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    csds_test <- scaling(csds_test, reverse = TRUE)
}
````
\newpage
### Exogenous scaling {#exoscaling}

Finally, exogenous scaling factors are applied to the data.
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    csds_test <- scaling(csds_test, type = "exo")
}
````

### Extract binding values

The last step of data processing is to extract and format binding scores in 
order to use plotting methods. The 'extractBinding' method extracts binding 
scores at different locations and stores these values in the form of 
PlotSetArray objects and matrices (see ?extractBinding for more details). The 
scores are retrieved on annotations provided in a gff file. If one wishes to 
focus on peaks, their coordinates should be submitted at this step. The genome 
name must also be provided. For details about installing the required BSgenome 
package corresponding to the endogenous organism, see the 
[BSgenome](https://bioconductor.org/packages/release/bioc/html/BSgenome.html) 
package documentation.
\newline
```{r message = FALSE, warning = FALSE}
if (.Platform$OS.type != "windows") {
    csds_test <- extractBinding(csds_test, gff_vec, genome_name)
}
````

# Plotting data

ChIPSeqSpike offers several graphical methods for normalization diagnosis and 
data exploration. These choices enable one to visualize each step of the 
normalization through exploring inter-samples differences using profiles, 
heatmaps, boxplots and correlation plots.

In the following sections, the testing data are restricted to the 100 mostly 
bound genes. Results on the complete set of hg19 genes are also indicated.

## Meta-profiles and transformations

The first step of spike-in normalized ChIP-Seq data analysis is an inter-sample
 comparison by meta-gene or meta-annotation profiling. The method 'plotProfile'
 automatically plots all experiments at the start, midpoint, end and composite 
locations of the annotations provided to the method extractBinding in gff 
format. Here is the result of profiling H3K79me2 on the 100 mostly bound genes 
at 0% inhibitor treatment (figure \ref{figure1}).
\newline
```{r message = FALSE, warning = FALSE, fig.cap="Spiked experiment upon different percentages concentrations of inhibitor treatment \\label{figure1}", fig.height = 6}
data(result_extractBinding)
plotProfile(csds, legend = TRUE)
````

The unspiked data (however RPM scaled and input subtracted) can be added 
to the plot (figure \ref{figure2}).
\newline
```{r message = FALSE, warning = FALSE, fig.cap="Same as figure 1 including unspiked data \\label{figure2}", fig.height = 7}
plotProfile(csds, legend = TRUE, notScaled = TRUE)
````     

\newpage
The effect of the individual processing steps for each experiment can also be 
plotted.
\newline
```{r message = FALSE, warning = FALSE}
plotTransform(csds, legend = TRUE, separateWindows = TRUE)
````


## Heatmaps

plotHeatmaps is a versatile method based on the plotHeatmap method of the 
seqplots package [@seqplots]. This method enables one to represent data at 
different locations (start, end, midpoint, composite) and at different stages 
of the normalization process. Different scaling (log, zscore, etc) and 
different clustering approaches (k-means, hierarchical, etc) can be used (see 
documentation for more details).

Figure \ref{figure3} shows a k-means clustering of spiked data, each group 
being sub-sorted by decreasing values.
\newline
\newline
```{r message = FALSE, warning = FALSE, fig.cap="kmeans clustering of spiked data \\label{figure3}", fig.height = 5}
plotHeatmaps(csds, nb_of_groups = 2, clustering_method = "kmeans")
````

\newpage
Figure \ref{figure4} illustrates a clustering by decreasing values on the whole
 dataset.

![Spiked data organized by decreasing values at start position of all refseq Hg19 genes \label{figure4}](heatmaps_start-2.png)


## Boxplots

boxplotSpike plots boxplots of the mean values of ChIP-seq experiments on the
annotations given to the extractBinding method. It offers a wide range of 
graphical representations that includes violin plots (see documentation for 
details). Figure \ref{figure5} illustrates all transformations of all dataset 
indicating confidence intervals.
\newpage
```{r message = FALSE, warning = FALSE, fig.cap="Complete representation of the whole procedure using boxplots (without outliers)\\label{figure5}", fig.height = 6}
par(cex.axis=0.5)
boxplotSpike(csds, rawFile = TRUE, rpmFile = TRUE, bgsubFile = TRUE, 
revFile = TRUE, spiked = TRUE, outline = FALSE)
````      

\newpage
Figure \ref{figure6} shows spiked experiments indicating each mean value, mean
 and standard deviation with a violin plot representation.
\newline
```{r message = FALSE, warning = FALSE, fig.cap="Spiked data with mean and standard deviation - Each point represents a mean binding value on a given gene \\label{figure6}", fig.height = 6}
boxplotSpike(csds, outline = FALSE, violin=TRUE, mean_with_sd = TRUE,
 jitter = TRUE)
````


## Correlation plots

The plotCor method plots the correlation between ChIP-seq experiments using 
heatscatter plot or, if heatscatterplot = FALSE, correlation tables. For 
heatscatter plots, ChIPSeqSpike makes use of the heatscatter function of the 
package [LSD](https://CRAN.R-project.org/package=LSD) and the corrplot function
 of the package [corrplot](https://CRAN.R-project.org/package=corrplot) is used
 to generate correlation tables. This offers a wide range of graphical 
possibilities for assessing the correlation between experiments and 
transformation steps (see documentation for more details).

Figure \ref{figure7} shows two correlation table representations between spiked
 experiments.
\newline
```{r message = FALSE, warning = FALSE, fig.cap="Correlation table of spiked data with circle (left) or numbers (right)\\label{figure7}", fig.height = 6, fig.width=10}
par(mfrow=c(1,2))
plotCor(csds, heatscatterplot = FALSE)
plotCor(csds, heatscatterplot = FALSE, method_corrplot = "number")
````

Figure \ref{figure8} illustrates a heatscatter plot of spiked data after log 
transformation (only positive mean binding values are kept) and figure 
\ref{figure9} is the result of running the same code on the whole refseq Hg19 
gene set.
\newline
```{r message = FALSE, warning =  FALSE, fig.cap="Heatscatter of spiked data after log transformation \\label{figure8}", fig.height=6}
plotCor(csds, method_scale = "log")
````        
        
        
![Heatscatter of spiked data after log transformation on all Hg19 refseq genes \label{figure9}](cor_log.pdf)
          
          
          
```{r message = FALSE, warning = FALSE, include = FALSE}
unlink("test_chipseqspike/", recursive = TRUE)
````

# Session info

```{r}
sessionInfo(package="ChIPSeqSpike")
````        
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  - family: Fenouil
    given: Romain
  - family: Descostes
    given: Nicolas
  - family: Spinelli
    given: Lionel
  - family: Koch
    given: Frederic
  - family: Maqbool
    given: Muhammad A
  - family:  Benoukraf
    given: Touati
  - family: Cauchy
    given: Pierre
  - family: Innocenti
    given: Charlene
  - family: Ferrier
    given: Pierre
  - family: Andrau
    given: Jean-Christophe
  container-title: Bioinformatics
  volume: 25
  URL: 'http://dx.doi.org/10.1093/bioinformatics/btp352'
  DOI: 10.1093/bioinformatics/btp352
  issue: 16
  publisher: Oxford Academic
  type: article-journal
  issued:
    year: 2009
    month: 8

- id: seqplots
  title: 'SeqPlots - Interactive software for exploratory data analyses, pattern discovery and visualization in genomics'
  author:
  - family: Stempor
    given: Przemyslaw
  - family: Ahringer
    given: Julie
  container-title: Wellcome Open Research
  volume: 1
  URL: 'http://dx.doi.org/10.12688/wellcomeopenres.10004.1'
  DOI: 10.12688/wellcomeopenres.10004.1
  issue: 
  publisher: 
  type: article-journal
  issued:
    year: 2016
    month: 11

---
