deepTools: tools for exploring deep sequencing data¶

QC and data processing¶

I have downloaded/received a BAM file - how do I generate a file I can look at in a genome browser?¶

• tool: bamCoverage
• input: your BAM file with aligned reads

Of course, you could also look at your BAM file in the genome browser. However, generating a bigWig file of read coverages will drastically reduce the size of the file, it also allows you to normalize the coverage to 1x sequencing depth, which makes a visual comparison of multiple files more feasible.

How can I assess the reproducibility of my sequencing replicates?¶

Typically, you’re going to be interested in the correlation of the read coverages for different replicates and different samples. What you want to see is that replicates should correlate better than non-replicates. The ENCODE consortium recommends that for messenger RNA, (...) biological replicates [should] display 0.9 correlation for transcripts/features. For more information about correlation calculations, see the background description for plotCorrelation.

• tools: multiBamSummary followed by plotCorrelation

• input: BAM files

  • you can compare as many samples as you want, though the more you use the longer the computation will take

Tip

If you would like to do a similar analysis based on bigWig files, use the tool multiBigwigSummary instead.

How do I know whether my sample is GC biased? And if it is, how do I correct for it?¶

• input: BAM file
• use the tool computeGCBias on that BAM file (default settings, just make sure your reference genome and genome size are matching)

• have a look at the image that is produced and compare it to the examples here

• if your sample shows an almost linear increase in exp/obs coverage (on the log scale of the lower plot), then you should consider correcting the GC bias - if you think that the biological interpretation of this data would otherwise be compromised (e.g. by comparing it to another sample that does not have an inherent GC bias)

  • the GC bias can be corrected with the tool correctGCBias using the second output of the computeGCbias tool that you had to run anyway

Warning

correctGCbias will add reads to otherwise depleted regions (typically GC-poor regions), that means that you should not remove duplicates in any downstream analyses based on the GC-corrected BAM file. We therefore recommend removing duplicates before doing the correction so that only those duplicate reads are kept that were produced by the GC correction procedure.

How do I get an input-normalized ChIP-seq coverage file?¶

• input: you need two BAM files, one for the input and one for the ChIP-seq experiment
• tool: bamCompare with ChIP = treatment, input = control sample

How can I compare the ChIP strength for different ChIP experiments?¶

• tool: plotFingerprint
• input: as many BAM files of ChIP-seq samples as you’d like to compare (it is helpful to include the input control to see what a hopefully non-enriched sample looks like)

Tip

For more details on the interpretation of the plot, see plotFingerprint or select the tool within the deepTools Galaxy and scroll down for more information.

Heatmaps and summary plots¶

How do I get a (clustered) heatmap of sequencing-depth-normalized read coverages around the transcription start site of all genes?¶

• tools: computeMatrix, then plotHeatmap

• inputs:

  • 1 bigWig file of normalized read coverages (e.g. the output of bamCoverage or bamCompare)
  • 1 BED or INTERVAL file of genes, e.g. obtained through Galaxy via “Get Data” –> “UCSC main table browser” –> group: “Genes and Gene Predictions” –> (e.g.) “RefSeqGenes” –> send to Galaxy (see screenshots below)

• use computeMatrix with the bigWig file and the BED file
• indicate reference-point (and whatever other option you would like to tune, see screenshot below)

• use the output from computeMatrix with plotHeatmap

  • if you would like to cluster the signals, choose k-means clustering (last option of “advanced options”) with a reasonable number of clusters (usually between 2 to 7)

How can I compare the average signal for X-specific and autosomal genes for 2 or more different sequencing experiments?¶

Make sure you’re familiar with computeMatrix and plotProfile before using this protocol.

• tools:

  • Filter data on any column using simple expressions
  • computeMatrix
  • plotProfile
  • (plotting the summary plots for multiple samples)

• inputs:

  • several bigWig files (one for each sequencing experiment you would like to compare)
  • two BED files, one with X-chromosomal and one with autosomal genes

How to obtain a BED file for X chromosomal and autosomal genes each¶

1. download a full list of genes via “Get Data” –> “UCSC main table browser” –> group:”Genes and Gene Predictions” –> tracks: (e.g.) “RefSeqGenes” –> send to Galaxy

2. filter the list twice using the tool “Filter data on any column using simple expressions”

   • first use the expression: c1==”chrX” to filter the list of all genes –> this will generate a list of X-linked genes
   • then re-run the filtering, now with c1!=”chrX”, which will generate a list of genes that do not belong to chromosome X (!= indicates “not matching”)

Compute the average values for X and autosomal genes¶

• use computeMatrix for all of the signal files (bigWig format) at once

  • supply both filtered BED files (click on “Add new regions to plot” once) and label them
  • indicate the corresponding signal files

• now use plotProfile on the resulting file

  • important: display the “advanced output options” and select “save the data underlying the average profile” –> this will generate a table in addition to the summary plot images

deepTools Galaxy.

code @ github.

Normalized ChIP-seq signals and peak regions¶

This image was published by Ibrahim et al., 2014 (NAR). They used deepTools to generate extended reads per kilobase per million reads at 10 base resolution and visualized the resulting coverage files in IGV.

DNase accessibility at enhancers in murine ES cells¶

The following image demonstrates that enhancer regions are typically small stretches of highly accessible chromatin (more information on enhancers can be found, for example, here). In the heatmap, yellow and blue tiles indicate a large numbers of reads that were sequenced (indicative of open chromatin) and black spots indicate missing data points. An appropriate labeling of the y-axis was neglected.

Fast Facts:

• computeMatrix mode: reference-point
• regions file: BED file with typical enhancer regions from Whyte et al., 2013 (download here)
• signal file: bigWig file with DNase signal from UCSC
• heatmap cosmetics: labels, titles, heatmap height

Command:

$ computeMatrix reference-point \
 -S DNase_mouse.bigwig \
 -R Whyte_TypicalEnhancers_ESC.bed \
 --referencePoint center \
 -a 2000 -b 2000 \ ## regions before and after the enhancer centers
 -out matrix_Enhancers_DNase_ESC.tab.gz

$ plotHeatmap \
 -m matrix_Enhancers_DNase_ESC.tab.gz\
 -out hm_DNase_ESC.png \
 --heatmapHeight 15  \
 --refPointLabel enh.center \
 --regionsLabel enhancers \
 --plotTitle 'DNase signal' \

TATA box enrichments around the TSS of mouse genes¶

Using the TRAP suite, we produced a bigWig file that contained TRAP scores for the well-known TATA box motif along the mouse genome. The TRAP score is a measure for the strength of a protein-DNA interaction at a given DNA sequence; the higher the score, the closer the motif is to the consensus motif sequence. The following heatmap demonstrates that:

• TATA-like motifs occur quite frequently
• there is an obvious clustering of TATA motifs slightly upstream of the TSS of many mouse genes
• there are many genes that do not contain TATA-like motifs at their promoter

Note that the heatmap shows all mouse RefSeq genes, so ca. 15,000 genes!

Fast Facts:

• computeMatrix mode: reference-point
• regions file: BED file with all mouse genes (from UCSC table browser)
• signal file: bigWig file of TATA psem scores
• heatmap cosmetics: color scheme, labels, titles, heatmap height, only showing heatmap + colorbar

Command:

$ computeMatrix reference-point \
 -S TATA_01_pssm.bw \
 -R RefSeq_genes.bed \
 --referencePoint TSS \
 -a 100 -b 100 \
 --binSize 5 \

$ plotHeatmap \
 -m matrix_Genes_TATA.tab.gz  \
 -out hm_allGenes_TATA.png \
 --colorMap hot_r \
 --missingDataColor .4 \
 --heatmapHeight 7 \
 --plotTitle 'TATA motif' \
 --whatToShow 'heatmap and colorbar' \
 --sortRegions ascend

Visualizing the GC content for mouse and fly genes¶

It is well known that different species have different genome GC contents. Here, we used two bigWig files where the GC content was calculated for 50 base windows along the genome of mice and flies and the resulting scores visualized for gene regions.

The images nicely illustrate the completely opposite GC distributions in flies and mice: while the gene starts of mammalian genomes are enriched for Gs and Cs, fly promoters show depletion of GC content.

Fly and mouse genes were scaled to different sizes due to the different median sizes of the two species’ genes (genes of D.melanogaster contain many fewer introns and are considerably shorter than mammalian genes). Thus, computeMatrix had to be run with slightly different parameters while the plotHeatmap commands were virtually identical (except for the labels).

$ computeMatrix scale-regions \
 -S GCcontent_Mm9_50_5.bw \
 -R RefSeq_genes_uniqNM.bed \
 -bs 50
 -m 10000 -b 3000 -a 3000 \
 -out matrix_GCcont_Mm9_scaledGenes.tab.gz \
 --skipZeros \
 --missingDataAsZero

$ computeMatrix scale-regions \
 -S GCcontent_Dm3_50_5.bw \
 -R Dm530.genes.bed \
 -bs 50
 -m 3000 -b 1000 -a 1000 \
 -out matrix_GCcont_Dm3_scaledGenes.tab.gz \
 --skipZeros --missingDataAsZero

$ plotHeatmap \
 -m matrix_GCcont_Dm3_scaledGenes.tab.gz \
 -out hm_GCcont_Dm3_scaledGenes.png \
 --colorMap YlGnBu \
 --regionsLabel 'fly genes' \
 --heatmapHeight 15 \
 --plotTitle 'GC content fly' &

$ plotHeatmap \
 -m matrix_GCcont_Mm9_scaledGenes.tab.gz \
 -out hm_GCcont_Mm9_scaledGenes.png \
 --colorMap YlGnBu \
 --regionsLabel 'mouse genes' \
 --heatmapHeight 15 \
 --plotTitle 'GC content mouse' &

CpG methylation around murine transcription start sites in two different cell types¶

In addition to the methylation of histone tails, the cytosines can also be methylated (for more information on CpG methylation, read here). In mammalian genomes, most CpGs are methylated unless they are in gene promoters that need to be kept unmethylated to allow full transcriptional activity. In the following heatmaps, we used genes expressed primarily in ES cells and checked the percentages of methylated cytosines around their transcription start sites. The blue signal indicates that very few methylated cytosines are found. When you compare the CpG methylation signal between ES cells and neuronal progenitor (NP) cells, you can see that the majority of genes remain unmethylated, but the general amount of CpG methylation around the TSS increases, as indicated by the stronger red signal and the slight elevation of the CpG methylation signal in the summary plot. This supports the notion that genes stored in the BED file indeed tend to be more expressed in ES than in NP cells.

This image was taken from Chelmicki & Dündar et al. (2014), eLife.

The commands for the bigWig files from the ES and NP cells were the same:

$ computeMatrix reference-point \
 -S GSE30202_ES_CpGmeth.bw \
 -R activeGenes_ESConly.bed \
 --referencePoint TSS \
 -a 2000 -b 2000 \
 -out matrix_Genes_ES_CpGmeth.tab.gz

$ plotHeatmap \
 -m matrix_Genes_ES_CpGmeth.tab.gz \
 -out hm_activeESCGenes_CpG_ES_indSort.png \
 --colorMap jet \
 --missingDataColor "#FFF6EB" \
 --heatmapHeight 15 \
 --yMin 0 --yMax 100 \
 --plotTitle 'ES cells' \
 --regionsLabel 'genes active in ESC'

Histone marks for genes of the mosquito Anopheles gambiae¶

This figure was taken from Gómez-Díaz et al. (2014): Insights into the epigenomic landscape of the human malaria vector *Anopheles gambiae*. From Genet Aug15;5:277. It shows the distribution of H3K27Me3 (left) and H3K27Ac (right) over gene features in A. gambiae midguts. The enrichment or depletion is shown relative to chromatin input. The regions in the map comprise gene bodies flanked by a segment of 200 bases at the 5′ end of TSSs and TTSs. Average profile across gene regions ±200 bases for each histone modification are shown on top.

Signals of repressive chromatin marks, their enzymes and repeat element conservation scores¶

This image is from Bulut-Karsliogu and De La Rosa-Velázquez et al. (2014), Mol Cell. The heatmaps depict various signal types for unscaled peak regions of proteins and histone marks associated with repressed chromatin. The peaks were separated into those containing long interspersed elements (LINEs) on the forward and reverse strand. The signals include normalized ChIP-seq signals for H3K9Me3, Suv39h1, Suv39h2, Eset, and HP1alpha-EGFP, followed by LINE and ERV content and repeat conservation scores.

Upload files from your computer¶

The data upload of files smaller than 2 GB that lie on your computer is fairly straight-forward: click on the category “Get data” and choose the tool “Upload file”. Then select the file via the “Browse” button.

For files greater than 2GB, there’s the option to upload via an FTP server. If your data is available via an URL that links to an FTP server, you can simply paste the URL in the empty text box.

If you do not have access to an FTP server, you can directly upload to our Galaxy’s FTP.

1. Register with deeptools.ie-freiburg.mpg.de (via “User” ⟶ “register”; registration requires an email address and is free of charge)
2. You will also need an FTP client, e.g. filezilla.
3. Then login to the FTP client using your deepTools Galaxy user name and password (host: deeptools.ie-freiburg.mpg.de). Down below you see a screenshot of what that looks like with filezilla.
4. Copy the file you wish to upload to the remote site (in filezilla, you can simply drag the file to the window on the right hand side)
5. Go back to deepTools Galaxy.
6. Click on the tool “Upload file” (⟶ “Files uploaded via FTP”) - here, the files you just copied over via filezilla should appear. Select the files you want and hit “execute”. They will be moved from the FTP server to your history (i.e. they will be deleted from the FTP once the upload was successful).

Import data sets from the Galaxy data library¶

If you would like to play around with sample data, you can import files that we have saved within the general data storage of the deepTools Galaxy server. Everyone can import them into his or her own history, they will not contribute to the user’s disk quota.

You can reach the data library via “Shared Data” in the top menu, then select “Data Libraries”.

Within the Data Library you will find a folder called “Sample Data” that contains data that we downloaded from the Roadmap project and UCSC More precisely, we donwloaded the [FASTQ][] files of various ChIP-seq samples and the corresponding input and mapped the reads to the human reference genome (version hg19) to obtain the [BAM][] files you see. In addition, you will find bigWig files created using bamCoverage and some annotation files in BED format as well as RNA-seq data.

Download annotation files from public data bases¶

In many cases you will want to query your sequencing data results for known genome annotation, such as genes, exons, transcription start sites etc. These information can be obtained via the two main sources of genome annotation, UCSC and BioMart.

You can access the data stored at UCSC or BioMart conveniently through our Galaxy instance which will import the resulting files into your history. Just go to “Get data” ⟶ “UCSC” or “BioMart”.

The majority of annotation files will probably be in [BED][] format, however, you can also find other data sets. UCSC, for example, offers a wide range of data that you can browse via the “group” and “track” menus (for example, you could download the GC content of the genome as a signal file from UCSC via the “group” menu (“Mapping and Sequencing Tracks”).

Here’s a screenshot from downloading a BED-file of all RefSeq genes defined for the human genome (version hg19):

And here’s how you would do it for the BioMart approach:

Copy data sets between histories¶

If you have registered with deepTools Galaxy you can have more than one history.

In order to minimize the disk space you’re occupying we strongly suggest to copy data sets between histories when you’re using the same data set in different histories.

Copying can easily be done via the History panel’s option button ⟶ “Copy dataset”. In the main frame, you should now be able to select the history you would like to copy from on the left hand side and the target history on the right hand side.

More help

deepTools¶

The most important category is called “deepTools” that contains all the main tools we have developed.

Working with text files and tables¶

In addition to deepTools that were specifically developed for the handling of NGS data, we have incorporated several standard Galaxy tools that enable you to manipulate tab-separated files such as gene lists, peak lists, data matrices etc.

There are 3 main categories;

Filter and Sort¶

In addition to the common sorting and filtering, there’s the very useful tool to select lines that match an expression. For example, using the expression c1=='chrM' will select all rows from a BED file with regions located on the mitochondrial chromosome.

Join, Subtract, Group¶

The tools of this category are very useful if you have several data sets that you would like to work with, e.g. by comparing them.

Basic arithmetics for tables¶

We offer some very basic mathematical operations on values stored with tables. The Summary Statistics can be used to calculate the sum, mean, standard deviation and percentiles for a set of numbers, e.g. for values stored in a specific column.

More help

Finding read coverage over a region¶

With deepTools, the read coverage over multiple genomic regions and multiple files can be computed quite quickly using multiple processors. First, we start with a simple example that is later expanded upon to demonstrate the use of multipe processors. In this example we compute the coverage of reads over a small region for bins of 50bp. For this we need the deeptools.countReadsPerBin class.

import deeptools.countReadsPerBin

We also need a BAM file containing the aligned reads. The BAM file must be indexed to allow quick access to reads falling into the regions of interest.

bam_file = "file.bam"

Now, the countReadsPerBin object can be initialized. The first argument to the constructor is a list of BAM files, which in this case is just one file. We are going to use a binLength of 50 bases, with subsequent bins adjacent (i.e., the stepSize between bins is also 50 bases). Overlapping bin coverages can be used by setting a stepSize smaller than binLength.

cr = countReadsPerBin.CountReadsPerBin([bam_file], binLength=50, stepSize=50)

Now, we can compute the coverage over a region in chromosome 2 from position 0 to 1000.

cr.count_reads_in_region('chr2L', 0, 1000)

array([[ 2.],
       [ 3.],
       [ 1.],
       [ 2.],
       [ 3.],
       [ 2.],
       [ 4.],
       [ 3.],
       [ 2.],
       [ 3.],
       [ 4.],
       [ 6.],
       [ 4.],
       [ 2.],
       [ 2.],
       [ 1.]])

The result is a numpy array with one row per bin and one column per bam file. Since only one BAM file was used, there is only one column.

Filtering reads¶

If reads should be filtered, the relevant options simply need to be passed to the constructor. In the following code, the reads are filtered such that only those with a mapping quality of at least 20 and not aligned to the reverse strand are kept (samFlag_exclude=16, where 16 is the value for reverse reads, see the [SAM Flag Calculator](http://broadinstitute.github.io/picard/explain-flags.html) for more info). Furthermore, duplicated reads are ignored.

cr = countReadsPerBin.CountReadsPerBin([bam_file], binLength=50, stepSize=50,
                                        minMappingQuality=20,
                                        samFlag_exclude=16,
                                        ignoreDuplicates=True
                                        )
cr.count_reads_in_region('chr2L', 1000000, 1001000)

array([[ 1.],
       [ 1.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 2.],
       [ 3.],
       [ 1.],
       [ 0.],
       [ 1.],
       [ 2.],
       [ 0.],
       [ 0.],
       [ 1.],
       [ 2.],
       [ 1.],
       [ 0.],
       [ 0.],
       [ 0.]])

Sampling the genome¶

Instead of adjacent bins, as in the previous cases, a genome can simply be sampled. This is useful to estimate some values, like depth of sequencing, without having to look at the complete genome. In the following example, 10,000 positions of size 1 base are going to be queried from three bam files to compute the average depth of sequencing. For this, we set the numberOfSamples parameter in the object constructor. The skipZeros parameter is added to exclude regions lacking reads in all BAM files. The run() method is used instead of count_reads_in_region.

cr = countReadsPerBin.CountReadsPerBin([bam_file1, bam_file2, bam_file3],
                                        binLength=1, numberOfSamples=10000,
                                        numberOfProcessors=10,
                                        skipZeros=True)
sequencing_depth = cr.run()
print sequencing_depth.mean(axis=0)

[  1.98923924   2.43743744  22.90102603]

The run() method splits the computation over 10 processors and collates the results. When the parameter numberOfSamples is used, the regions selected for the computation of the coverage are not random. Instead, the genome is split into ‘number-of-samples’ equal parts and the start of each part is queried for its coverage. You can also compute coverage over selected regions by inputting a BED file.

Now it is possible to make some diagnostic plots from the results:

fig, axs = plt.subplots(1, 2, figsize=(15,5))
# plot coverage
for col in res.T:
    axs[0].plot(np.bincount(col.astype(int)).astype(float)/total_sites)
    csum = np.bincount(col.astype(int))[::-1].cumsum()
    axs[1].plot(csum.astype(float)[::-1] / csum.max())
axs[0].set_xlabel('coverage')
axs[0].set_ylabel('fraction of bases sampled')
# plot cumulative coverage

axs[1].set_xlabel('coverage')
axs[1].set_ylabel('fraction of bases sampled >= coverage')

Computing the FRiP score¶

The FRiP score is defined as the fraction of reads that fall into a peak and is often used as a measure of ChIP-seq quality. For this example, we need a BED file containing the peak regions. Such files are usually computed using a peak caller. Also, two bam files are going to be used, corresponding to two biological replicates.

bed_file = open("peaks.bed", 'r')
cr = countReadsPerBin.CountReadsPerBin([bam_file1, bam_file2],
                                        bedFile=bed_file,
                                        numberOfProcessors=10)
reads_at_peaks = cr.run()
print reads_at_peaks

array([[ 322.,  248.],
       [ 231.,  182.],
       [ 112.,  422.],
       ...,
       [ 120.,   76.],
       [ 235.,  341.],
       [ 246.,  265.]])

The result is a numpy array with a row for each peak region and a column for each BAM file.

reads_at_peaks.shape

(6295, 2)

Now, the total number of reads per peaks per bam file is computed:

total = reads_at_peaks.sum(axis=0)

Next, we need to find the total number of mapped reads in each of the bam files. For this we use the pysam module.

import pysam
bam1 = pysam.AlignmentFile(bam_file1)
bam2 = pysam.AlignmentFile(bam_file2)

Now, bam1.mapped and bam2.mapped contain the total number of mapped reads in each of the bam files, respectively.

Finally, we can compute the FRiP score:

frip1 = float(total[0]) / bam1.mapped
frip2 = float(total[1]) / bam2.mapped
print frip1, frip2

0.170030741997, 0.216740390353

Using mapReduce to sample paired-end fragment lengths¶

deepTools internally uses a map-reduce strategy, in which a computation is split into smaller parts that are sent to different processors. The output from the different processors is subsequently collated. The following example is based on the code available for bamPEFragmentSize.py

Here, we retrieve the reads from a BAM file and collect the fragment length. Reads are retrieved using pysam, and the read object returned contains the template_length attribute, which is the number of bases from the leftmost to the rightmost mapped base in the read pair.

First, we will create a function that can collect fragment lengths over a genomic position from a BAM file. As we will later call this function using mapReduce, the function accepts only one argument, namely a tuple with the parameters: chromosome name, start position, end position, and BAM file name.

import pysam
import numpy as np
def get_fragment_length(args):
    chrom, start, end, bam_file_name = args
    bam = pysam.Aligmementfile(bam_file_name)
    f_lens_list = []
    for fetch_start in range(start, end, 1e6):
        # simply get the reads over a region of 10000 bases
        fetch_end = min(end, start + 10000)

        f_lens_list.append(np.array([abs(read.template_length)
                              for read in bam.fetch(chrom, fetch_start, fetch_end)
                              if read.is_proper_pair and read.is_read1]))

    # concatenate all results
    return np.concatenate(fragment_lengths)

Now, we can use mapReduce to call this function and compute fragment lengths over the whole genome. mapReduce needs to know the chromosome sizes, which can be easily retrieved from the BAM file. Furthermore, it needs to know the size of the region(s) sent to each processor. For this example, a region of 10 million bases is sent to each processor using the genomeChunkLength parameter. In other words, each processor executes the same get_fragment_length function to collect data over different 10 million base regions. The arguments to mapReduce are the list of arguments sent to the function, besides the first obligatory three (chrom start, end). In this case only one extra argument is passed to the function, the BAM file name. The next two positional arguments are the name of the function to call (get_fragment_length) and the chromosome sizes.

import deeptools.mapReduce
bam = pysam.Aligmentfile(bamFile)
chroms_sizes = zip(bam.references, bam.lengths)

result = mapReduce.mapReduce((bam_file_name, ),
                              get_fragment_length
                              chrom_sizes,
                              genomeChunkLength=10000000,
                              numberOfProcessors=20,
                              verbose=True)

fragment_lengths =  np.concatenate(result)

print "mean fragment length {}".format(fragment_lengths.mean()"
print "median fragment length {}".format(np.median(fragment_lengths)"

0.170030741997, 0.216740390353

Indices and tables¶

• Index
• Module Index
• Search Page

deeptools.SES_scaleFactor module¶

class deeptools.SES_scaleFactor.Tester[source]¶

Bases: object

deeptools.SES_scaleFactor.estimateScaleFactor(bamFilesList, binLength, numberOfSamples, normalizationLength, avg_method='median', blackListFileName=None, numberOfProcessors=1, verbose=False, chrsToSkip=[])[source]¶

Subdivides the genome into chunks to be analyzed in parallel using several processors. The code handles the creation of workers that compute fragment counts (coverage) for different regions and then collect and integrates the results.

Parameters:

bamFilesList : list list of bam files to normalize binLength : int the window size in bp, where reads are going to be counted. numberOfSamples : int number of sites to sample from the genome. For more info see the documentation of the CountReadsPerBin class normalizationLength : int length, in bp, to normalize the data. For a value of 1, on average 1 read per base pair is found avg_method : str defines how the different values are to be summarized. The options are ‘mean’ and ‘median’ chrsToSkip : list name of the chromosomes to be excluded from the scale estimation. Usually the chrX is included. blackListFileName : str BED file containing blacklisted regions

Returns:

dict Dictionary with the following keys:: ‘size_factors’ ‘size_factors_based_on_mapped_reads’ ‘size_factors_SES’ ‘size_factors_based_on_mean’ ‘size_factors_based_on_median’ ‘mean’ ‘meanSES’ ‘median’ ‘reads_per_bin’ ‘std’ ‘sites_sampled’

Examples

>>> test = Tester()
>>> bin_length = 50
>>> num_samples = 4
>>> _dict = estimateScaleFactor([test.bamFile1, test.bamFile2], bin_length, num_samples,  1)
>>> _dict['size_factors']
array([ 1. ,  0.5])
>>> _dict['size_factors_based_on_mean']
array([ 1. ,  0.5])

deeptools.bamHandler module¶

deeptools.bamHandler.openBam(bamFile)[source]¶

deeptools.correctReadCounts module¶

deeptools.correctReadCounts.computeCorrectedReadcounts(tileCoverage, args)[source]¶

This function is called by the writeBedGraph workers for every tile in the genome that is considered

It computes a pvalue based on an expected lambda comming from the correction of treatment when the input is considered.

deeptools.correctReadCounts.computeLambda(tileCoverage, args)[source]¶

This function is called by the writeBedGraph workers for every tile in the genome that is considered

deeptools.correctReadCounts.computePvalue(tileCoverage, args)[source]¶

This function is called by the writeBedGraph workers for every tile in the genome that is considered

It computes a pvalue based on an expected lambda comming from the correction of treatment when the input is considered.

deeptools.correctReadCounts.controlLambda(tileCoverage, args)[source]¶

deeptools.correctReadCounts.correctReadCounts(bamFilesList, binLength, numberOfSamples, defaultFragmentLength, outFileName, outFileFormat, outFileNameCorr=None, region=None, extendPairedEnds=True, numberOfProcessors=1, Nsigmas=2, maxSignalRatio=10, blackListFileName=None, verbose=False)[source]¶

deeptools.correlation module¶

class deeptools.correlation.Correlation(matrix_file, corr_method=None, labels=None, remove_outliers=False, skip_zeros=False, log1p=False)[source]¶

class to work with matrices having sample data to compute correlations, plot them and make scatter plots

compute_correlation()[source]¶

computes spearman or pearson correlation for the samples in the matrix

The matrix should contain the values of each sample per column that’s why the transpose is used.

>>> matrix = np.array([[1, 2, 3, np.nan],
...                    [1, 2, 3, 4],
...                    [6, 4, 3, 1]]).T
>>> np.savez_compressed("/tmp/test_matrix.npz", matrix=matrix, labels=['a', 'b', 'c'])

>>> c = Correlation("/tmp/test_matrix.npz", corr_method='pearson')

the results should be as in R

>>> c.compute_correlation().filled(np.nan)
array([[ 1.        ,  1.        , -0.98198051],
       [ 1.        ,  1.        , -0.98198051],
       [-0.98198051, -0.98198051,  1.        ]])
>>> c.corr_method = 'spearman'
>>> c.corr_matrix = None
>>> c.compute_correlation()
array([[ 1.,  1., -1.],
       [ 1.,  1., -1.],
       [-1., -1.,  1.]])

static get_outlier_indices(data, max_deviation=200)[source]¶

The method is based on the median absolute deviation. See Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.

returns the list, without the outliers

The max_deviation=200 is like selecting a z-score larger than 200, just that it is based on the median and the median absolute deviation instead of the mean and the standard deviation.

load_matrix(matrix_file)[source]¶

loads a matrix file saved using the numpy savez method. Two keys are expected: ‘matrix’ and ‘labels’. The matrix should contain one sample per row

plot_correlation(plot_fiilename, plot_title='', vmax=None, vmin=None, colormap='jet', image_format=None, plot_numbers=False)[source]¶

plots a correlation using a symmetric heatmap

plot_pca(plot_filename, plot_title='', image_format=None, log1p=False)[source]¶

Plot the PCA of a matrix

plot_scatter(plot_fiilename, plot_title='', image_format=None, log1p=False)[source]¶

Plot the scatter plots of a matrix in which each row is a sample

remove_outliers(verbose=True)[source]¶

get the outliers per column using the median absolute deviation method

Returns the filtered matrix

remove_rows_of_zeros()[source]¶

save_corr_matrix(file_handle)[source]¶

saves the correlation matrix

deeptools.correlation_heatmap module¶

deeptools.correlation_heatmap.plot_correlation(corr_matrix, labels, plotFileName, vmax=None, vmin=None, colormap='jet', image_format=None, plot_numbers=False, plot_title='')[source]¶

deeptools.countReadsPerBin module¶

class deeptools.countReadsPerBin.CountReadsPerBin(bamFilesList, binLength=50, numberOfSamples=None, numberOfProcessors=1, verbose=False, region=None, bedFile=None, extendReads=False, blackListFileName=None, minMappingQuality=None, ignoreDuplicates=False, chrsToSkip=[], stepSize=None, center_read=False, samFlag_include=None, samFlag_exclude=None, zerosToNans=False, smoothLength=0, minFragmentLength=0, maxFragmentLength=0, out_file_for_raw_data=None)[source]¶

Bases: object

Collects coverage over multiple bam files using multiprocessing

This function collects read counts (coverage) from several bam files and returns an numpy array with the results. This class uses multiprocessing to compute the coverage.

Parameters:

bamFilesList : list List containing the names of indexed bam files. E.g. [‘file1.bam’, ‘file2.bam’] binLength : int Length of the window/bin. This value is overruled by bedFile if present. numberOfSamples : int Total number of samples. The genome is divided into numberOfSamples, each with a window/bin length equal to binLength. This value is overruled by stepSize in case such value is present and by bedFile in which case the number of samples and bins are defined in the bed file numberOfProcessors : int Number of processors to use. Default is 4 verbose : bool Output messages. Default: False region : str Region to limit the computation in the form chrom:start:end. bedFile : file_handle File handle of a bed file containing the regions for which to compute the coverage. This option overrules binLength, numberOfSamples and stepSize. blackListFileName : str A string containing a BED file with blacklist regions. extendReads : bool, int Whether coverage should be computed for the extended read length (i.e. the region covered by the two mates or the regions expected to be covered by single-reads). If the value is ‘int’, then then this is interpreted as the fragment length to extend reads that are not paired. For Illumina reads, usual values are around 300. This value can be determined using the peak caller MACS2 or can be approximated by the fragment lengths computed when preparing the library for sequencing. If the value is of the variable is true and not value is given, the fragment size is sampled from the library but only if the library is paired-end. Default: False minMappingQuality : int Reads of a mapping quality less than the give value are not considered. Default: None ignoreDuplicates : bool Whether read duplicates (same start, end position. If paired-end, same start-end for mates) are to be excluded. Default: false chrToSkip: list List with names of chromosomes that do not want to be included in the coverage computation. This is useful to remove unwanted chromosomes (e.g. ‘random’ or ‘Het’). stepSize : int the positions for which the coverage is computed are defined as follows: range(start, end, stepSize). Thus, a stepSize of 1, will compute the coverage at each base pair. If the stepSize is equal to the binLength then the coverage is computed for consecutive bins. If seepSize is smaller than the binLength, then teh bins will overlap. center_read : bool Determines if reads should be centered with respect to the fragment length. samFlag_include : int Extracts only those reads having the SAM flag. For example, to get only reads that are the first mates a samFlag of 64 could be used. Similarly, the samFlag_include can be used to select only reads mapping on the reverse strand or to get only properly paired reads. samFlag_exclude : int Removes reads that match the SAM flag. For example to get all reads that map to the forward strand a samFlag_exlude 16 should be used. Which translates into exclude all reads that map to the reverse strand. zerosToNans : bool If true, zero values encountered are transformed to Nans. Default false. minFragmentLength : int If greater than 0, fragments below this size are excluded. maxFragmentLength : int If greater than 0, fragments above this size are excluded. out_file_for_raw_data : str File name to save the raw counts computed

Returns:

numpy array Each row correspond to each bin/bed region and each column correspond to each of the bamFiles.

Examples

The test data contains reads for 200 bp.

>>> test = Tester()

The transpose function is used to get a nicer looking output. The first line corresponds to the number of reads per bin in bam file 1

>>> c = CountReadsPerBin([test.bamFile1, test.bamFile2], 50, 4)
>>> np.transpose(c.run())
array([[ 0.,  0.,  1.,  1.],
       [ 0.,  1.,  1.,  2.]])

count_reads_in_region(chrom, start, end, bed_regions_list=None)[source]¶

Counts the reads in each bam file at each ‘stepSize’ position within the interval (start, end) for a window or bin of size binLength.

The stepSize controls the distance between bins. For example, a step size of 20 and a bin size of 20 will create bins next to each other. If the step size is smaller than the bin size the bins will overlap.

If a list of bedRegions is given, then the number of reads that overlaps with each region is counted.

Parameters:	chrom : str Chrom name start : int start coordinate end : int end coordinate bed_regions_list: list List of list of tuples of the form (start, end) corresponding to bed regions to be processed. If not bed file was passed to the object constructor then this list is empty.
Returns:	numpy array The result is a numpy array that as rows each bin and as columns each bam file.

Examples

Initialize some useful values

>>> test = Tester()
>>> c = CountReadsPerBin([test.bamFile1, test.bamFile2], 25, 0, stepSize=50)

The transpose is used to get better looking numbers. The first line corresponds to the number of reads per bin in the first bamfile.

>>> _array, __ = c.count_reads_in_region(test.chrom, 0, 200)
>>> _array
array([[ 0.,  0.],
       [ 0.,  1.],
       [ 1.,  1.],
       [ 1.,  2.]])

getReadLength(read)[source]¶

getSmoothRange(tileIndex, tileSize, smoothRange, maxPosition)[source]¶

Given a tile index position and a tile size (length), return the a new indices over a larger range, called the smoothRange. This region is centered in the tileIndex an spans on both sizes to cover the smoothRange. The smoothRange is trimmed in case it is less than zero or greater than maxPosition

---------------|==================|------------------
           tileStart
      |--------------------------------------|
      |    <--      smoothRange     -->      |
      |
tileStart - (smoothRange-tileSize)/2

Test for a smooth range that spans 3 tiles.

Examples

>>> c = CountReadsPerBin([], 1, 1, 1, 0)
>>> c.getSmoothRange(5, 1, 3, 10)
(4, 7)

Test smooth range truncated on start.

>>> c.getSmoothRange(0, 10, 30, 200)
(0, 2)

Test smooth range truncated on start.

>>> c.getSmoothRange(1, 10, 30, 4)
(0, 3)

Test smooth range truncated on end.

>>> c.getSmoothRange(5, 1, 3, 5)
(4, 5)

Test smooth range not multiple of tileSize.

>>> c.getSmoothRange(5, 10, 24, 10)
(4, 6)

get_coverage_of_region(bamHandle, chrom, regions, fragmentFromRead_func=None)[source]¶

Returns a numpy array that corresponds to the number of reads that overlap with each tile.

>>> test = Tester()
>>> import pysam
>>> c = CountReadsPerBin([], stepSize=1, extendReads=300)

For this case the reads are length 36. The number of overlapping read fragments is 4 and 5 for the positions tested.

>>> c.get_coverage_of_region(pysam.AlignmentFile(test.bamFile_PE), 'chr2',
... [(5000833, 5000834), (5000834, 5000835)])
array([ 4.,  5.])

In the following example a paired read is extended to the fragment length which is 100 The first mate starts at 5000000 and the second at 5000064. Each mate is extended to the fragment length independently At position 500090-500100 one fragment of length 100 overlap, and after position 5000101 there should be zero reads.

>>> c.zerosToNans = True
>>> c.get_coverage_of_region(pysam.AlignmentFile(test.bamFile_PE), 'chr2',
... [(5000090, 5000100), (5000100, 5000110)])
array([  1.,  nan])

In the following case the reads length is 50. Reads are not extended.

>>> c.extendReads=False
>>> c.get_coverage_of_region(pysam.AlignmentFile(test.bamFile2), '3R', [(148, 150), (150, 152), (152, 154)])
array([ 1.,  2.,  2.])

get_fragment_from_read(read)[source]¶

Get read start and end position of a read. If given, the reads are extended as follows: If reads are paired end, each read mate is extended to match the fragment length, otherwise, a default fragment length is used. If reads are split (give by the CIGAR string) then the multiple positions of the read are returned. When reads are extended the cigar information is skipped.

Parameters:

read : pysam read object

Returns:

list of tuples [(fragment start, fragment end)] >>> test = Tester() >>> c = CountReadsPerBin([], 1, 1, 200, extendReads=True) >>> c.defaultFragmentLength=100 >>> c.get_fragment_from_read(test.getRead("paired-forward")) [(5000000, 5000100)] >>> c.get_fragment_from_read(test.getRead("paired-reverse")) [(5000000, 5000100)] >>> c.defaultFragmentLength = 200 >>> c.get_fragment_from_read(test.getRead("single-forward")) [(5001491, 5001691)] >>> c.get_fragment_from_read(test.getRead("single-reverse")) [(5001536, 5001736)] >>> c.defaultFragmentLength = 'read length' >>> c.get_fragment_from_read(test.getRead("single-forward")) [(5001491, 5001527)] >>> c.defaultFragmentLength = 'read length' >>> c.extendReads = False >>> c.get_fragment_from_read(test.getRead("paired-forward")) [(5000000, 5000036)] Tests for read centering. >>> c = CountReadsPerBin([], 1, 1, 200, extendReads=True, center_read=True) >>> c.defaultFragmentLength = 100 >>> assert(c.get_fragment_from_read(test.getRead("paired-forward")) == [(5000032, 5000068)]) >>> c.defaultFragmentLength = 200 >>> assert(c.get_fragment_from_read(test.getRead("single-reverse")) == [(5001618, 5001654)])

run(allArgs=None)[source]¶

class deeptools.countReadsPerBin.Tester[source]¶

Bases: object

getRead(readType)[source]¶

prepare arguments for test

deeptools.countReadsPerBin.countReadsInRegions_wrapper(args)[source]¶

Passes the arguments to countReadsInRegions_worker. This is a step required given the constrains from the multiprocessing module. The args var, contains as first element the ‘self’ value from the countReadsPerBin object

deeptools.countReadsPerBin.remove_row_of_zeros(matrix)[source]¶

deeptools.getFragmentAndReadSize module¶

deeptools.getFragmentAndReadSize.getFragmentLength_worker(chrom, start, end, bamFile, distanceBetweenBins)[source]¶

Queries the reads at the given region for the distance between reads and the read length

Parameters:	chrom : str chromosome name start : int region start end : int region end bamFile : str BAM file name distanceBetweenBins : int the number of bases at the end of each bin to ignore
Returns:	np.array an np.array, where first column is fragment length, the second is for read length

deeptools.getFragmentAndReadSize.getFragmentLength_wrapper(args)[source]¶

deeptools.getFragmentAndReadSize.get_read_and_fragment_length(bamFile, return_lengths=False, blackListFileName=None, binSize=50000, distanceBetweenBins=1000000, numberOfProcessors=None, verbose=False)[source]¶

Estimates the fragment length and read length through sampling

Parameters:	bamFile : str BAM file name return_lengths : bool numberOfProcessors : int verbose : bool binSize : int distanceBetweenBins : int
Returns:	d : dict tuple of two dictionaries, one for the fragment length and the other for the read length. The dictionaries summarise the mean, median etc. values

deeptools.getRatio module¶

deeptools.getRatio.compute_ratio(value1, value2, args)[source]¶

deeptools.getRatio.getRatio(tileCoverage, args)[source]¶

The mapreduce method calls this function for each tile. The parameters (args) are fixed in the main method.

>>> funcArgs= {'valueType': 'ratio', 'scaleFactors': (1,1), 'pseudocount': 1}
>>> getRatio([9, 19], funcArgs)
0.5
>>> getRatio([0, 0], funcArgs)
1.0
>>> getRatio([np.nan, np.nan], funcArgs)
nan
>>> getRatio([np.nan, 1.0], funcArgs)
nan
>>> funcArgs['valueType'] ='subtract'
>>> getRatio([20, 10], funcArgs)
10
>>> funcArgs['scaleFactors'] = (1, 0.5)
>>> getRatio([10, 20], funcArgs)
0.0

The reciprocal ratio is of a and b is: is a/b if a/b > 1 else -1* b/a >>> funcArgs[‘valueType’] =’reciprocal_ratio’ >>> funcArgs[‘scaleFactors’] = (1, 1) >>> funcArgs[‘pseudocount’] = 0 >>> getRatio([2, 1], funcArgs) 2.0 >>> getRatio([1, 2], funcArgs) -2.0 >>> getRatio([1, 1], funcArgs) 1.0

deeptools.getScorePerBigWigBin module¶

class deeptools.getScorePerBigWigBin.Tester[source]¶

Bases: object

deeptools.getScorePerBigWigBin.countFragmentsInRegions_worker(chrom, start, end, bigWigFiles, stepSize, binLength, save_data, bedRegions=None)[source]¶

returns the average score in each bigwig file at each ‘stepSize’ position within the interval start, end for a ‘binLength’ window. Because the idea is to get counts for window positions at different positions for sampling the bins are equally spaced and not adjacent.

If a list of bedRegions is given, then the number of reads that overlaps with each region is counted.

Test dataset with two samples covering 200 bp. >>> test = Tester()

Fragment coverage. >>> np.transpose(countFragmentsInRegions_worker(test.chrom, 0, 200, [test.bwFile1, test.bwFile2], 50, 25, False)[0]) array([[ 1., 1., 2., 2.],

[ 1., 1., 1., 3.]])

>>> np.transpose(countFragmentsInRegions_worker(test.chrom, 0, 200, [test.bwFile1, test.bwFile2], 200, 200, False)[0])
array([[ 1.5],
       [ 1.5]])

BED regions: >>> bedRegions = [[test.chrom, [(45, 55)]], [test.chrom, [(95, 105)]], [test.chrom, [(145, 155)]]] >>> np.transpose(countFragmentsInRegions_worker(test.chrom, 0, 200,[test.bwFile1, test.bwFile2], 200, 200, False, ... bedRegions=bedRegions)[0]) array([[ 1. , 1.5, 2. ],

[ 1. , 1. , 2. ]])

deeptools.getScorePerBigWigBin.countReadsInRegions_wrapper(args)[source]¶

deeptools.getScorePerBigWigBin.getChromSizes(bigwigFilesList)[source]¶

Get chromosome sizes from bigWig file with pyBigWig

Test dataset with two samples covering 200 bp. >>> test = Tester()

Chromosome name(s) and size(s). >>> assert(getChromSizes([test.bwFile1, test.bwFile2]) == ([(‘3R’, 200)], set([])))

deeptools.getScorePerBigWigBin.getScorePerBin(bigWigFiles, binLength, numberOfProcessors=1, verbose=False, region=None, bedFile=None, blackListFileName=None, stepSize=None, chrsToSkip=[], out_file_for_raw_data=None, allArgs=None)[source]¶

This function returns a matrix containing scores (median) for the coverage of fragments within a region. Each row corresponds to a sampled region. Likewise, each column corresponds to a bigwig file.

Test dataset with two samples covering 200 bp. >>> test = Tester() >>> np.transpose(getScorePerBin([test.bwFile1, test.bwFile2], 50, 3)) array([[ 1., 1., 2., 2.],

[ 1., 1., 1., 3.]])

deeptools.heatmapper module¶

deeptools.heatmapper.chopRegions(exonsInput, left=0, right=0)[source]¶

exons is a list of (start, end) tuples. The goal is to chop these into separate lists of tuples, to take care or unscaled regions. “left” and “right” denote regions of a given size to exclude from the normal binning process (unscaled regions).

This outputs three lists of (start, end) tuples:

leftBins: 5’ unscaled regions bodyBins: body bins for scaling rightBins: 3’ unscaled regions

In addition are two integers padLeft: Number of bases of padding on the left (due to not being able to fulfill “left”) padRight: As above, but on the right side

deeptools.heatmapper.chopRegionsFromMiddle(exonsInput, left=0, right=0)[source]¶

Like chopRegions(), above, but returns two lists of tuples on each side of the center point of the exons.

The steps are as follow:

1. Find the center point of the set of exons (e.g., [(0, 200), (300, 400), (800, 900)] would be centered at 200)

• If a given exon spans the center point then the exon is split

2. The given number of bases at the end of the left-of-center list are extracted

• If the set of exons don’t contain enough bases, then padLeft is incremented accordingly

3. As above but for the right-of-center list
4. A tuple of (#2, #3, pading on the left, and padding on the right) is returned

deeptools.heatmapper.compute_sub_matrix_wrapper(args)[source]¶

class deeptools.heatmapper.heatmapper[source]¶

Bases: object

Class to handle the reading and plotting of matrices.

static change_chrom_names(chrom)[source]¶

Changes UCSC chromosome names to ensembl chromosome names and vice versa.

computeMatrix(score_file_list, regions_file, parameters, blackListFileName=None, verbose=False, allArgs=None)[source]¶

Splits into multiple cores the computation of the scores per bin for each region (defined by a hash ‘#’ in the regions (BED/GFF) file.

static compute_sub_matrix_worker(chrom, start, end, score_file_list, parameters, regions)[source]¶

Returns:	numpy matrix A numpy matrix that contains per each row the values found per each of the regions given

static coverage_from_array(valuesArray, zones, binSize, avgType)[source]¶

static coverage_from_big_wig(bigwig, chrom, zones, binSize, avgType, nansAsZeros=False, verbose=True)[source]¶

uses pyBigWig to query a region define by chrom and zones. The output is an array that contains the bigwig value per base pair. The summary over bins is done in a later step when coverage_from_array is called. This method is more reliable than querying the bins directly from the bigwig, which should be more efficient.

By default, any region, even if no chromosome match is found on the bigwig file, produces a result. In other words no regions are skipped.

zones: array as follows zone0: region before the region start,

zone1: 5’ unscaled region (if present) zone2: the body of the region (not always present) zone3: 3’ unscaled region (if present) zone4: the region from the end of the region downstream

each zone is a tuple containing start, end, and number of bins

This is useful if several matrices wants to be merged or if the sorted BED output of one computeMatrix operation needs to be used for other cases

get_individual_matrices(matrix)[source]¶

In case multiple matrices are saved one after the other this method splits them appart. Returns a list containing the matrices

get_num_individual_matrix_cols()[source]¶

returns the number of columns that each matrix should have. This is done because the final matrix that is plotted can be composed of smaller matrices that are merged one after the other.

static matrix_avg(matrix, avgType='mean')[source]¶

matrix_from_dict(matrixDict, regionsDict, parameters)[source]¶

static my_average(valuesArray, avgType='mean')[source]¶

computes the mean, median, etc but only for those values that are not Nan

read_matrix_file(matrix_file)[source]¶

save_BED(file_handle)[source]¶

save_matrix(file_name)[source]¶

saves the data required to reconstruct the matrix the format is: A header containing the parameters used to create the matrix encoded as: @key:value key2:value2 etc... The rest of the file has the same first 5 columns of a BED file: chromosome name, start, end, name, score and strand, all separated by tabs. After the fifth column the matrix values are appended separated by tabs. Groups are separated by adding a line starting with a hash (#) and followed by the group name.

The file is gzipped.

save_matrix_values(file_name)[source]¶

save_tabulated_values(file_handle, reference_point_label='TSS', start_label='TSS', end_label='TES', averagetype='mean')[source]¶

Saves the values averaged by col using the avg_type given

Args:

file_handle: file name to save the file reference_point_label: Name of the reference point label start_label: Name of the star label end_label: Name of the end label averagetype: average type (e.g. mean, median, std)

deeptools.heatmapper.trimZones(zones, maxLength, binSize, padRight)[source]¶

Given a (variable length) list of lists of (start, end) tuples, trim/remove and tuple that extends past maxLength (e.g., the end of a chromosome)

Returns the trimmed zones and padding

deeptools.heatmapper_utilities module¶

deeptools.heatmapper_utilities.getProfileTicks(hm, referencePointLabel, startLabel, endLabel)[source]¶

returns the position and labelling of the xticks that correspond to the heatmap

deeptools.heatmapper_utilities.plot_single(ax, ma, average_type, color, label, plot_type='simple')[source]¶

Adds a line to the plot in the given ax using the specified method

Parameters:

ax : matplotlib axis matplotlib axis ma : numpy array numpy array The data on this matrix is summarized according to the average_type argument. average_type : str string values are sum mean median min max std color : str a valid color: either a html color name, hex (e.g #002233), RGB + alpha tuple or list or RGB tuple or list label : str label plot_type: str type of plot. Either ‘se’ for standard error, ‘std’ for standard deviation, ‘overlapped_lines’ to plot each line of the matrix, fill to plot the area between the x axis and the value or None, just to plot the average line.

Returns:

ax matplotlib axis

Examples

>>> import matplotlib.pyplot as plt
>>> import os
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> matrix = np.array([[1,2,3],
...                    [4,5,6],
...                    [7,8,9]])
>>> ax = plot_single(ax, matrix -2, 'mean', color=[0.6, 0.8, 0.9], label='fill light blue', plot_type='fill')
>>> ax = plot_single(ax, matrix, 'mean', color='blue', label='red')
>>> ax = plot_single(ax, matrix + 5, 'mean', color='red', label='red', plot_type='std')
>>> ax = plot_single(ax, matrix + 10, 'mean', color='#cccccc', label='gray se', plot_type='se')
>>> ax = plot_single(ax, matrix + 20, 'mean', color=(0.9, 0.5, 0.9), label='violet', plot_type='std')
>>> ax = plot_single(ax, matrix + 30, 'mean', color=(0.9, 0.5, 0.9, 0.5), label='violet with alpha', plot_type='std')
>>> leg = ax.legend()
>>> plt.savefig("/tmp/test.pdf")
>>> plt.close()
>>> fig = plt.figure()
>>> os.remove("/tmp/test.pdf")

deeptools.mapReduce module¶

deeptools.mapReduce.blSubtract(t, chrom, chunk)[source]¶

If a genomic region overlaps with a blacklisted region, then subtract that region out

returns a list of lists

deeptools.mapReduce.getUserRegion(chrom_sizes, region_string, max_chunk_size=1000000.0)[source]¶

Verifies if a given region argument, given by the user is valid. The format of the region_string is chrom:start:end:tileSize where start, end and tileSize are optional.

Parameters:	chrom_sizes – dictionary of chromosome/scaffold size. Key=chromosome name region_string – a string of the form chr:start:end max_chunk_size – upper limit for the chunk size
Returns:	tuple chrom_size for the region start, region end, chunk size

#>>> data = getUserRegion({‘chr2’: 1000}, “chr1:10:10”) #Traceback (most recent call last): # ... #NameError: Unknown chromosome: chr1 #Known chromosomes are: [‘chr2’]

If the region end is biger than the chromosome size, this value is used instead >>> getUserRegion({‘chr2’: 1000}, “chr2:10:1001”) ([(‘chr2’, 1000)], 10, 1000, 990)

Test chunk and regions size reduction to match tile size >>> getUserRegion({‘chr2’: 200000}, “chr2:10:123344:3”) ([(‘chr2’, 123344)], 9, 123345, 123336)

Test chromosome name mismatch >>> getUserRegion({‘2’: 200000}, “chr2:10:123344:3”) ([(‘2’, 123344)], 9, 123345, 123336) >>> getUserRegion({‘chrM’: 200000}, “MT:10:123344:3”) ([(‘chrM’, 123344)], 9, 123345, 123336)

deeptools.mapReduce.mapReduce(staticArgs, func, chromSize, genomeChunkLength=None, region=None, bedFile=None, blackListFileName=None, numberOfProcessors=4, verbose=False, includeLabels=False, keepExons=False, transcriptID='transcriptID', exonID='exonID', transcript_id_designator='transcript_id', self_=None)[source]¶

Split the genome into parts that are sent to workers using a defined number of procesors. Results are collected and returned.

For each genomic region the given ‘func’ is called using the following parameters:

chrom, start, end, staticArgs

The arg are static, pickable variables that need to be sent to workers.

The genome chunk length corresponds to a fraction of the genome, in bp, that is send to each of the workers for processing.

Depending on the type of process a larger or shorter regions may be preferred

Parameters:

chromSize – A list of duples containing the chromosome name and its length region – The format is chr:start:end:tileSize (see function getUserRegion) staticArgs – tuple of arguments that are sent to the given ‘func’ func – function to call. The function is called using the following parameters (chrom, start, end, staticArgs) bedFile – Is a bed file is given, the args to the func to be called are extended to include a list of bed defined regions. blackListFileName – A list of regions to exclude from all computations. Note that this has genomeChunkLength resolution... self – In case mapreduce should make a call to an object the self variable has to be passed. includeLabels – Pass group and transcript labels into the calling function. These are added to the static args (groupLabel and transcriptName).

If “includeLabels” is true, a tuple of (results, labels) is returned

deeptools.utilities module¶

deeptools.utilities.bam_blacklisted_reads(bam_handle, chroms_to_ignore, blackListFileName=None, numberOfProcessors=1)[source]¶

deeptools.utilities.bam_blacklisted_worker(args)[source]¶

deeptools.utilities.bam_total_reads(bam_handle, chroms_to_ignore)[source]¶

Count the total number of mapped reads in a BAM file, filtering the chromosome given in chroms_to_ignore list

deeptools.utilities.copyFileInMemory(filePath, suffix='')[source]¶

copies a file into the special /dev/shm device which moves the file into memory. This process speeds ups the multiprocessor access to such files

deeptools.utilities.getCommonChrNames(bamFileHandlers, verbose=True)[source]¶

Compares the names and lengths of a list of bam file handlers. The input is list of pysam file handlers.

The function returns a duple containing the common chromosome names and the common chromome lengths.

Hopefully, only _random and chrM are not common.

deeptools.utilities.getGC_content(dnaString, as_fraction=True)[source]¶

deeptools.utilities.getTempFileName(suffix='')[source]¶

returns a temporary file name. If the special /dev/shm device is available, the temporary file would be located in that folder. /dv/shm is a folder that resides in memory and which has much faster accession.

deeptools.utilities.gtfOptions(allArgs=None)[source]¶

This is used a couple places to setup arguments to mapReduce

deeptools.utilities.mungeChromosome(chrom, chromList)[source]¶

A generic chromosome munging function. “chrom” is munged by adding/removing “chr” such that it appears in chromList

On error, None is returned, but a common chromosome list should be used beforehand to avoid this possibility

deeptools.utilities.tbitToBamChrName(tbitNames, bamNames)[source]¶

checks if the chromosome names from the two-bit and bam file coincide. In case they do not coincide, a fix is tried. If successful, then a mapping table is returned. tbitNames and bamNames should be lists

deeptools.utilities.toBytes(s)[source]¶

Like toString, but for functions requiring bytes in python3

deeptools.utilities.toString(s)[source]¶

This takes care of python2/3 differences

deeptools.utilities.which(program)[source]¶

method to identify if a program is on the user PATH variable. From: http://stackoverflow.com/questions/377017/test-if-executable-exists-in-python

deeptools.writeBedGraph module¶

class deeptools.writeBedGraph.WriteBedGraph(bamFilesList, binLength=50, numberOfSamples=None, numberOfProcessors=1, verbose=False, region=None, bedFile=None, extendReads=False, blackListFileName=None, minMappingQuality=None, ignoreDuplicates=False, chrsToSkip=[], stepSize=None, center_read=False, samFlag_include=None, samFlag_exclude=None, zerosToNans=False, smoothLength=0, minFragmentLength=0, maxFragmentLength=0, out_file_for_raw_data=None)[source]¶

Bases: deeptools.countReadsPerBin.CountReadsPerBin

Reads bam files coverages and writes a bedgraph or bigwig file

Extends the CountReadsPerBin object such that the coverage of bam files is writen to multiple bedgraph files at once.

The bedgraph files are later merge into one and converted into a bigwig file if necessary.

The constructor arguments are the same as for CountReadsPerBin. However, when calling the run method, the following parameters have to be passed

Examples

Given the following distribution of reads that cover 200 on a chromosome named ‘3R’:

  0                              100                           200
  |------------------------------------------------------------|
A                                ===============
                                                ===============

B                 ===============               ===============
                                 ===============
                                                ===============

>>> import tempfile
>>> test_path = os.path.dirname(os.path.abspath(__file__)) + "/test/test_data/"

>>> outFile = tempfile.NamedTemporaryFile()
>>> bam_file = test_path +  "testA.bam"

For the example a simple scaling function is going to be used. This function takes the coverage found at each region and multiplies it to the scaling factor. In this case the scaling factor is 1.5

>>> function_to_call = scaleCoverage
>>> funcArgs = {'scaleFactor': 1.5}

Restrict process to a region between positions 0 and 200 of chromosome 3R

>>> region = '3R:0:200'

Set up such that coverage is computed for consecutive bins of length 25 bp >>> bin_length = 25 >>> step_size = 25

>>> num_sample_sites = 0 #overruled by step_size
>>> c = WriteBedGraph([bam_file], binLength=bin_length, region=region, stepSize=step_size)
>>> c.run(function_to_call, funcArgs, outFile.name)
>>> open(outFile.name, 'r').readlines()
['3R\t0\t100\t0.00\n', '3R\t100\t200\t1.50\n']
>>> outFile.close()

run(func_to_call, func_args, out_file_name, blackListFileName=None, format='bedgraph', smoothLength=0)[source]¶

Given a list of bamfiles, a function and a function arguments, this method writes a bedgraph file (or bigwig) file for a partition of the genome into tiles of given size and a value for each tile that corresponds to the given function and that is related to the coverage underlying the tile.

Parameters:

func_to_call : str function name to be called to convert the list of coverages computed for each bam file at each position into a single value. An example is a function that takes the ratio between the coverage of two bam files. func_args : dict dict of arguments to pass to func. E.g. {‘scaleFactor’:1.0} out_file_name : str name of the file to save the resulting data. smoothLength : int Distance in bp for smoothing the coverage per tile.

writeBedGraph_worker(chrom, start, end, func_to_call, func_args, bed_regions_list=None)[source]¶

Writes a bedgraph based on the read coverage found on bamFiles

The given func is called to compute the desired bedgraph value using the funcArgs

Parameters:

chrom : str Chrom name start : int start coordinate end : int end coordinate func_to_call : str function name to be called to convert the list of coverages computed for each bam file at each position into a single value. An example is a function that takes the ratio between the coverage of two bam files. func_args : dict dict of arguments to pass to func. smoothLength : int Distance in bp for smoothing the coverage per tile. bed_regions_list: list List of tuples of the form (chrom, start, end) corresponding to bed regions to be processed. If not bed file was passed to the object constructor then this list is empty.

Returns:

temporary file with the bedgraph results for the region queried.

Examples

>>> test_path = os.path.dirname(os.path.abspath(__file__)) + "/test/test_data/"
>>> bamFile1 = test_path +  "testA.bam"
>>> bin_length = 50
>>> number_of_samples = 0 # overruled by step_size
>>> func_to_call = scaleCoverage
>>> funcArgs = {'scaleFactor': 1.0}

>>> c = WriteBedGraph([bamFile1], bin_length, number_of_samples, stepSize=50)
>>> tempFile = c.writeBedGraph_worker( '3R', 0, 200, func_to_call, funcArgs)
>>> open(tempFile, 'r').readlines()
['3R\t0\t100\t0.00\n', '3R\t100\t200\t1.00\n']
>>> os.remove(tempFile)

deeptools.writeBedGraph.bedGraphToBigWig(chromSizes, bedGraphPath, bigWigPath, sort=True)[source]¶

takes a bedgraph file, orders it and converts it to a bigwig file using pyBigWig.

deeptools.writeBedGraph.getGenomeChunkLength(bamHandlers, tile_size)[source]¶

Tries to estimate the length of the genome sent to the workers based on the density of reads per bam file and the number of bam files.

The chunk length should be a multiple of the tileSize

deeptools.writeBedGraph.ratio(tile_coverage, args)[source]¶

tileCoverage should be an list of two elements

deeptools.writeBedGraph.scaleCoverage(tile_coverage, args)[source]¶

tileCoverage should be an list with only one element

deeptools.writeBedGraph.writeBedGraph_wrapper(args)[source]¶

Passes the arguments to writeBedGraph_worker. This is a step required given the constrains from the multiprocessing module. The args var, contains as first element the ‘self’ value from the WriteBedGraph object

deeptools.writeBedGraph_bam_and_bw module¶

deeptools.writeBedGraph_bam_and_bw.getCoverageFromBigwig(bigwigHandle, chrom, start, end, tileSize, missingDataAsZero=False)[source]¶

deeptools.writeBedGraph_bam_and_bw.writeBedGraph(bamOrBwFileList, outputFileName, fragmentLength, func, funcArgs, tileSize=25, region=None, blackListFileName=None, numberOfProcessors=None, format='bedgraph', extendPairedEnds=True, missingDataAsZero=False, smoothLength=0, fixed_step=False)[source]¶

Given a list of bamfiles, a function and a function arguments, this method writes a bedgraph file (or bigwig) file for a partition of the genome into tiles of given size and a value for each tile that corresponds to the given function and that is related to the coverage underlying the tile.

deeptools.writeBedGraph_bam_and_bw.writeBedGraph_worker(chrom, start, end, tileSize, defaultFragmentLength, bamOrBwFileList, func, funcArgs, extendPairedEnds=True, smoothLength=0, missingDataAsZero=False, fixed_step=False)[source]¶

Writes a bedgraph having as base a number of bam files.

The given func is called to compute the desired bedgraph value using the funcArgs

tileSize

deeptools.writeBedGraph_bam_and_bw.writeBedGraph_wrapper(args)[source]¶

Module contents¶