2.1 Inputs

You can work with an existing metagene object previously generated by metagene (>= 2.1.31) or through Imetagene. The file must be a RData file (with extention .RData, .Rda, .RDATA or .RDA)

You also create a new metagene object. To do so, you have to provide the BAM files you want to use and the regions you want to study in BED format.

There is no hard limit in the number of BAM files that can be included in an analysis (but with too many BAM files, memory may become an issue). BAM files must be indexed. For instance, if you use a file names file.bam, a file named file.bam.bai must be present in the same directory.

To compare custom regions of interest, it is possible to use a list of one or more BED files. BED, narrowPeak and broadPeak format are supported (extention .bed, .BED, .narrowPeak and .broadPeak).

You can then save the metagene object for a future usage. Once the metagene is loaded or freshly created, you can go to the next step : Design creation.

2.2 Design

A design file is a tab-delimited file (.tsv) that describes one or more experiments. An experiment can contain one or more replicates and controls. The first column of a design file contains the list of bam names available in the current metagene object. The following columns correspond to the experiments. They must have a unique name and the possible values are 0 (ignore), 1 (chip) or 2 (control).

If you are using a loaded metagene object which already contains a design, it will be displayed in the Current design tab. If you load or create a new design it will override the existing one.

You can load an existing design file in the Load existing design file tab. If the file is well formated, it will be loaded and displayed the Current design tab.

To create a new design, you can go in the Create new design tab. You will have create one experiment at a time by :

• defining an experiment name (must be unique in the design, or it will override the homonym experiment)
• selecting the chip among the BAM files
• selecting the corresponding control among the remaining BAM files (optional)

Once it is done, you can add the experiment. The updated design will be displayed the Current design tab.

You can then save the design in csv file for a further use.

2.3 Matrix

To produce the metagene plot, the coverages must be converted in a matrix where the columns represent the positions and the rows the regions. Furthermore, to reduce the computation time during the following steps, the positions are also binned.

If you are using a loaded metagene object which already contains processed matrices, they will be displayable in the Current matrix subset tab. If you produce new matrices, they will override the existing ones.

We can control the size of the bin either with the bin_size argument. By default, a bin_count of 100 will be used during this step.

We can also use the design we produced earlier to remove background signal and combine replicates.

The Current matrix subset tab give you an overview of the generated matrices as heatmaps. Thanks to the package d3heatmap, you can hover the mouse pointer over a cell to show details, drag a rectangle to zoom, and click row/column ticks to highlight.

You can then save the updated metagene object (which now contains a design and matrices) for a future usage.

2.4 Plot

The metagene plot requires a data.frame as input. The data.frame is computed in background and the values of the ribbon are calculated. By default, metagene uses “bootstrap” to obtain a better estimation of the mean of enrichment for every positions in each group of regions/BAM files.

The data.frame will be then used to plot the calculated values using ggplot2. We show a subset of the regions by using the Regions to display and Experiments to display parameter. The Regions to display correspond to the names of the regions used during the initialization. The Experiments to display will vary depending if a design was added. If no design was added, this param correspond to the BAM name or BAM filenames. Otherwise, we have to use the names of the columns from the design.

Here is the plot with the usage of a design

And the same data without a design