The 4-2-3-1 formation came to prominence in Spain in the 1990s and early 2000s and is now used by many teams throughout the world. The two players in front of the back-four, known as the 'doble pivot' (double pivot) in Spain, give support to the defense, with one player breaking up opposition attacks, and the other putting more emphasis on. Correlation calculation ¶. The result of the correlation computation is a table of correlation coefficients that indicates how “strong” the relationship between two samples is and it will consist of numbers between -1 and 1. While some tools, such as plotFingerprint, specifically address ChIP-seq-issues, the majority of tools is widely applicable to deep-sequencing data, including RNA-seq. As shown in the flow chart above, our work usually begins with one or more FASTQ file(s) of deeply-sequenced samples.

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Take a deep belly breath to begin. 5 - LOOK: Look around for 5 things that you can see, and say them out loud. For example, you could say, I see the computer, I see the cup, I see the picture frame. 4 - FEEL: Pay attention to your body and think of 4 things that you can feel, and say them out loud. For example, you could say, I feel my feet. 2.3.1 Deeper is a tool with which allows you to easily configure the hidden functions of your Mac operating system. Download Deeper free and make your adjustments.

Tool for the analysis and visualization of sample correlations based on the output of multiBamSummary ormultiBigwigSummary. Pearson or Spearman methods are available to compute correlationcoefficients. Results can be saved as multiplescatter plots depicting the pairwise correlations or as a clustered heatmap,where the colors represent the correlation coefficients and the clusters are constructed using complete linkage.Optionally, the values can be saved as tables, too.

detailed help:

example usages:plotCorrelation -in results_file –whatToPlot heatmap –corMethod pearson -o heatmap.png

plotCorrelation computes the overall similarity between two or more files based on read coverage (or other scores) within genomic regions, which must be calculated using either multiBamSummary or multiBigwigSummary.

The result of the correlation computation is a table of correlation coefficients that indicates how “strong” the relationship between two samples is and it will consist of numbers between -1 and 1. (-1 indicates perfect anti-correlation, 1 perfect correlation.)

We offer two different functions for the correlation computation: Pearson or Spearman.

The Pearson method measures the metric differences between samples and is therefore influenced by outliers. More precisely, it is defined as the covariance of two variables divided by the product of their standard deviation.

The Spearman method is based on rankings.If you imagine a race with 3 participants where the winner and runner-up are very close together while the third person broke her leg and comes in way, way after the first two, then Pearson would be strongly influenced by the fact that the third person had a great distance to the first ones while Spearman would only care about the fact that person 1 came in first, person 2 came in second and person 3 got the third rank, the distances between them are ignored.

Tip

Pearson is an appropriate measure for data that follows a normal distribution, while Spearman does not make this assumption and is generally less driven by outliers, but with the caveat of also being less sensitive.

If you use the heatmap output of plotCorrelation, this will automatically lead to a clustering of the samples based on the correlation coefficients. This helps to determine whether the different sample types can be separated, i.e., samples of different conditions are expected to be more dissimilar to each other than replicates within the same condition.

The distances of the sample pairs are based on the correlation coefficients, r, where distance = 1 - r. The similarity of the samples is assessed using the nearest point algorithm, i.e., the shortest distance between any 2 members of the tree is considered to decide whether to join a cluster or not. For more details of the algorithm, go here.

Here’s an example of RNA-seq data from different human cell lines that we had downloaded from https://genome.ucsc.edu/ENCODE/dataMatrix/encodeDataMatrixHuman.html.

As you can see, both correlation calculations more or less agree on which samples are nearly identical (the replicates, indicated by 1 or 2 at the end of the label). The Spearman correlation, however, seems to be more robust and meets our expectations more closely as the two different cell types (HUVEC and IMR90) are clearly separated.

In the following example, a correlation analysis is performed based on the coverage file computed by multiBamSummary or multiBigwigSummary for our test ENCODE ChIP-Seq datasets.

Scatterplot

Here we make pairwose scatterplots of the average scores per transcript that we calculated using multiBigwigSummary and include the Pearson correlation coefficients for each comparison.

Heatmap

In addition to scatterplots, heatmaps can be generated where the pairwise correlation coefficients are depicted by varying color intensities and are clustered using hierarchical clustering.

The example here calculates the Spearman correlation coefficients of read counts.The dendrogram indicates which samples’ read counts are most similar to each other.

Released:

Useful tools for exploring deep sequencing data

Project description

deepTools addresses the challenge of handling the large amounts of datathat are now routinely generated from DNA sequencing centers. deepToolscontains useful modules to process the mapped reads data for multiplequality checks, creating normalized coverage files in standardbedGraph and bigWig file formats, that allow comparison betweendifferent files (for example, treatment and control). Finally, usingsuch normalized and standardized files, deepTools can create manypublication-ready visualizations to identify enrichments and forfunctional annotations of the genome.

For support or questions please make a post on Biostars. For feature requests, please open an issue on github.

For further documentation, please see our read the docs page.

Citation:

Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, HeyneS, Dündar F, Manke T. deepTools2: a next generation web server fordeep-sequencing dataanalysis.Nucleic Acids Research. 2016 Apr 13:gkw257.

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