In lamortenera/epicseg: Chromatin Segmentation in R

knitr::opts_chunk$set(error=FALSE)

Command line environment

In all examples we will assume that:

1. A UNIX system is used. Windows can also be used, but these examples need to be slightly adapted.
2. A launcher called epicseg.R has been created using the epicseg:::getLauncher function.

3. You have execution permissions for the launcher. If not, type at the command line:

   chmod 700 epicseg.R
   

4. The launcher is in a folder listed in your PATH environment variable, if this is not the case you can add it by typing at the command line:

   export PATH=/path/to/the/launcher/:$PATH
   

5. You have some input data to work with. You can reproduce these examples by setting the indir environment variable to the value returned by the R code system.file("extdata", package="epicseg")

6. You have an output folder ${outdir} where to store files created by EpiCSeg. This could be a new empty directory to make sure that you are not overwriting anything.

Typical workflow

Typically you just need to

1. create a count matrix with the getcounts subprogram
2. produce a segmentation with the segment subprogram
3. sometimes you might want to get a nicer output by using the report subprogram.

Creating the count matrix

The main input of EpiCSeg are read counts for a set of histone marks. More precisely, you need to select which experiments to use (in BAM format, already indexed), in which genomic regions to count the reads, and how large the bins should be.

Here we are going to use three BAM files, a bin size of 200 base pairs (the default) and the following BED file:

cat ${indir}/contigs.bed

This is done with the getcounts subprogram:

```{bash, results="hide"} epicseg.R getcounts --mark H3K4me3:${indir}/H3K4me3.bam \ --mark H3K36me3:${indir}/H3K36me3.bam \ --mark H3K9me3:${indir}/H3K9me3.bam \ --regions ${indir}/contigs.bed --binsize 200 \ --target ${outdir}/counts.txt

Which creates a count matrix in text format:

```{bash}
head -n 5 ${outdir}/counts.txt

Note that the count matrix and the genomic regions are linked and they should always be used together.

In this case the regions could be easily splitted into bins of size 200 because each region had a width multiple of 200. If this is not the case, the regions are automatically refined, and a new BED file will be created, like in this case:

```{bash, results="hide"} epicseg.R getcounts -m H3K4me3:${indir}/H3K4me3.bam \ -m H3K36me3:${indir}/H3K36me3.bam \ -m H3K9me3:${indir}/H3K9me3.bam \ -r ${indir}/contigs.bed --binsize 220 \ -t ${outdir}/counts.txt

This creates the files `${outdir}/counts.txt` (which, in this example, got
overwritten by the second call to `getcounts`), plus the BED file 
`${outdir}/counts_refined_regions.bed`:

```{bash}
cat ${outdir}/counts_refined_regions.bed

Note that:

1. the refined regions are always contained in the original ones
2. start and end coordinate of each region are multiples of the chosen binsize
3. the count matrix refers to the refined regions (if it was necessary to refine them).
4. if you computed the count matrix and your regions by means other than EpiCseg, that's totally fine, as long as the regions are a valid BED file, the count matrix is tab separated and each column is labelled.

Fitting the model and producing the segmentation

Training EpiCSeg's model, inferring the state for each bin and producing an HTML report is done all at once by the segment subprogram. You just need to specify your regions (the refined ones, if it was necessary to refine them), your counts and the number of states that you want.

```{bash, results="hide"} epicseg.R segment --counts ${outdir}/counts.txt \ --regions ${outdir}/counts_refined_regions.bed \ --nstates 5 --outdir ${outdir} --prefix myFirstSegmentation_

This creates a bunch of files with filenames starting with 
`${outdir}/myFirstSegmentation_`. Among these the most important are:

1. the file `${outdir}/myFirstSegmentation_lmeans.png` shows
    the average levels of histone marks for each state. 
    <center>
    ```r
    cat(paste0("![](",Sys.getenv("outdir"),"/myFirstSegmentation_lmeans.png)"))
    ```
    </center>

2. the file `${outdir}/myFirstSegmentation_model.txt` contains the parameter
    of the obtained model. This is useful if you want to use the learned model
    on other data or if you want to refine your output files with the 
    `report` subprogram. 

3. the file `${outdir}/myFirstSegmentation_segmentation.bed` is the segmentation
    in bed format.

4. the file `${outdir}/myFirstSegmentation_report.html` is a web page showing
    all plots and results.

#### Producing a better report

Suppose that you are not happy with the colors automatically assigned by 
EpiCSeg, or you want to assign labels to each state, or you want to see where 
the states tend to localize with respect to annotated genes. You can use the 
`report` subprogram  for that. 

We need to specify the model and the segmentation (the BED file) produced
with `segment`.
Here we specify colors and labels via simple text files and a gene annotation 
via a BED file.

```{bash}
printf "red\ngold\ngreen4\ngray\nblue\n" > ${outdir}/myColors.txt
printf "promoter\nactive\ntranscribed\ninactive\nrepressed\n" > ${outdir}/myLabels.txt

```{bash, results="hide"} epicseg.R report --model ${outdir}/myFirstSegmentation_model.txt \ --segments ${outdir}/myFirstSegmentation_segmentation.bed \ --annot genes:${indir}/genes.bed \ --colors ${outdir}/myColors.txt \ --labels ${outdir}/myLabels.txt \ --outdir ${outdir} --prefix nicerReport_

Now our heatmap looks better:

<center>
```r
cat(paste0("![](",Sys.getenv("outdir"),"/nicerReport_lmeans.png)"))

And we can see how states relate to genes (this was also possible by setting the --annot option directly in the segment subprogram):

cat(paste0("![](",Sys.getenv("outdir"),"/nicerReport_annot_genes.png)"))

Non-basic usage

Learn states from different datasets

Sometimes you have two or more sets of experiments that you want to use for chromatin segmentation, where each set comes from a different individual or cell type, so that you can look at differences between chromatin states from one individual to another.

To do this in EpiCSeg, you need a slightly different workflow:

1. use the getcounts subprogram for each dataset/individuals/cell-types separately
2. make the datasets comparable with the normalizecounts subprogram
3. use all count matrices with segment

Here we make two datasets by changing the association between labels and file names. Of course, this doesn't make sense and it's only to get some data for our example.

```{bash, results="hide"} epicseg.R getcounts -m H3K4me3:${indir}/H3K4me3.bam \ -m H3K36me3:${indir}/H3K36me3.bam \ -m H3K9me3:${indir}/H3K9me3.bam \ -r ${indir}/contigs.bed \ -t ${outdir}/sample1.txt

epicseg.R getcounts -m H3K4me3:${indir}/H3K36me3.bam \ -m H3K36me3:${indir}/H3K9me3.bam \ -m H3K9me3:${indir}/H3K4me3.bam \ -r ${indir}/contigs.bed \ -t ${outdir}/sample2.txt

Here we make the two count matrices `sample1.txt` and `sample2.txt` comparable.

```{bash, results="hide"}
epicseg.R normalizecounts -c ${outdir}/sample1.txt -c ${outdir}/sample2.txt

This created the normalized count matrices sample1_norm.txt and sample2_norm.txt. Now we use them for segmentation:

```{bash, results="hide"} epicseg.R segment -c sample1:${outdir}/sample1_norm.txt \ -c sample2:${outdir}/sample2_norm.txt \ -r ${indir}/contigs.bed \ -n 5 --outdir ${outdir} --prefix comparison_

Now you can, for instance, open the two BED files `comparison_segmentation_sample1.bed`
and `comparison_segmentation_sample2.bed` in the genome browser and look at 
differences, or do more advanced analyses in R.

#### Aggregating replicate experiments

Sometimes you have replicates for a particular ChIP-seq experiment and you
want to take some sort of average in order to improve the quality of the experiment.
This is handled by the `getcounts` subprogram by providing the same label
more than once.

Here we are treating the H3K36me3 and H3K9me3 samples as replicates, which does
not make sense and it's only to get some data for our example.

```{bash, results="hide"}
epicseg.R getcounts -m H3K4me3:${indir}/H3K4me3.bam \
                    -m H3K36me3:${indir}/H3K36me3.bam \
                    -m H3K36me3:${indir}/H3K9me3.bam \
                    -r ${indir}/contigs.bed \
                    -t ${outdir}/aggregatedcounts.txt

By providing the label H3K36me3 twice we averaged two ChIP-seq experiments. The resulting count matrix looks like this:

head -n 5 ${outdir}/aggregatedcounts.txt

Known issues

Outliers

Bins with abnormally high read counts compared to the genome average, especially when they appear at the beginning of a chromosome, are known to cause underflow errors in the forward-backward algorithm. Unfortunately there is no obvious solution to that. These bins should be avoided, by filtering out problematic regions or by correcting the read counts by some other means.

This can happen, for instance, when the mithocondrial genome is included in the set of analyzed regions. Read counts in the mithocondrial genome tend to be orders of magnitude higher than in the rest of the genome, so it is not appropriate to include them in the input data (moreover, mithocondria don't have chromatin...)

Did you encounter any other issue? File an issue in the github repository!!!

lamortenera/epicseg documentation built on May 20, 2019, 7:34 p.m.