analyze_yours_mountain

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Analyze yours: Mountain plot

Mountain plot, created by Dr. Adi Gazdar and Dr. Kai Song, is a very useful scatter plot for visualizing and analyzing genome-wide variations of copy numbers [1, 2]. It gains its name from the similarity of such a plot to the ups and downs of mountains outlines seen from afar. In Mountain plot, each spot is the median/mean value of concern data types (i.e. copy number etc.) of each gene in a group which can be a batch or a cancer type. The genes are sorted according to their locations. The space between two arms of each chromosome is the location of the corresponding centromere.

Originally, Mountain plot was used to analyze genome-wide variations of copy numbers. Due to the wonderful visualizing effect, we provide Mountain plot for mRNA expression and methylation values.

Because it is used to visualize the variations of copy numbers or other data types, the values of corresponding non-malignant samples are plotted in default if they are available for the given cancer types. When more than one cancer types are specified, all available non-malignant samples of all these cancer types will be combined in one group which means only one spot for each gene in all non-malignant samples from different specified cancer types.

As we mentioned above, there are two ways to get into the Mountain plot page.

1) Through the Navigation bar at the Home page, select “Mountain plot” under “Analyze yours”;

2) Go to “Analyze yours” page, then select “Mountain plot”;

For “Mountain plot” page, there are six areas:

→ Navigation bar: You can switch to other pages through this navigation bar.

→ Setting area: You can specify highlighted genes, chromosome, data types, cutoff values and other parameter details here.

→ Plotting area: The Mountain plot will be plotted in this area.

→ Figure Downloading and DIY area: You can download Mountain plot in certain format and size. You can also customize line color, line shape, marker color, marker shapes through the option buttons in this area.

→ Example area: Examples of the upload files are given here. You can download them through the link.

→ Link area: Necessary links are available for you to switch to other pages or websites.

Note: quick help can be available through putting your mouse on the small question marks besides certain options in this pages.

1. It reminds you which kind of plot you are working on.

2. You can upload your data in a file through the dialog box here. Only 'xlsx' file is acceptable. Please make sure all data are in the first sheet of this file. If there are more than one group of samples, please put them into separate files.

3. You need to specify the datatype here to tell us how to do log transformation (for mRNA expression values, it's log2 transformation; for CNV (copy number variation) values, it's log2(CNV/2) transformation).

4. You need to specify the chromosome through this drop-down list to provide us the necessary information about the location of the centromere. Now only 1-22 chromosomes are available.

5. You can specify concern gene here by inputting gene symbols. Only symbols provided in your files are accepted. If you want to input more than one gene symbols, a common and a space should be used to separate two gene symbols.

Note: small case and big case are all acceptable. For example, kRAS, kras, KRas, KRAS are all treated as the same gene.

6. In Mountain plot, each spot can be median or mean values of each group. You can specific either one of them through this drop-down list.

7. Log2: after checking this option, log transformation will be applied to the data for Mountain plot (for mRNA expression values, it's log2 transformation; for CNV (copy number variation) values, it's log2(CNV/2) transformation).

8. You can input cutoff values here to see whether the median/mean values of each group are above or below them.

After setting all these necessary options, click “GO” button at the bottom of this area, the Mountain plot will be created in the plotting area. There are no limits on how many groups of samples you want to plot. But due to the configuration of your computer, the internet transfering speed and the data sizes need to be transmitted for plotting (several times of the sample sizes), it may take a while to transmit and load the data for plotting. The more cancer types you want to plot, the longer the response time will it need.

Mountain plot figures will be shown in this area.

A toolbar will show up at the top right of this plotting area when a Mountain plot is created.

1. Zoom in: Rectangular zoom in tool. This tool allows you to select a region to display at full application size. After selecting this botton, your mouse will turn into a small cross. Then click and hold the left mouse button and drag a rectangle around a portion of the screen and have it zoomed in.

2. Zoom out: Zoom back to the status it was a step before by cliking it.

3. Restore: Show the plots in the original portion.

4. Save as Image: You can click it to swich into a image saving webpage then click right mouse button to save this image. You also can specify the image format and size by selecting the options in the Figure downloading and DIY area.

→ Vertically zoom in: There is a zoom bar at the right edge of the plotting area. Click and hold on either one of the two buttons on it, you can zoom in or zoom out vertically.

→ Horizontally zoom in: Slide the mouse wheel (for apple magic mouse, slide up or down) up or down, you can zoom in or zoom out horizontally.

For your convinence, the sample ID and other details of each individual gene will show up when you put your mouse on the corresponding marker.

For example: in the above figure, aftering putting the mouse on a marker, a catalog showed up is:

♦ First row: the group message of this sample;

♦ Second row: x-axis, y-axis, gene symbol

Δ x-axis: start location of the concern gene in the corresponding chromosome;

Δ y-axis: median/mean mRNA expression, copy number variation values depends on which data type you are working on;

Δ gene symbol: a symbol provided in your uploaded file.

Therefore, in this example: the gene symbol is LCE3C, its start location in chromosome 7 is 152573137 and the mean value of its copy number variations in group 1 is 2.793.

You can specify image format (png or jpg) and size/dimensions for the image to download .

You can modify color, shapes and other details of this figure for you own style.

Y-Limits: To make it easier for comparison by using the same Y-axis limits in the Mountain plots for different chromosomes, you can specify Y-axis limits here. Only the limits broader than the default ones are acceptable. (You can use the zoom bar mentioned above to make the limits narrower.)

Select the group whose style you want to custermize, then modify the color, shape of the markers here.

Only 'xlsx' file type is acceptable! The default sheet is the first sheet of each '*.xlsx' file. This area shows the table format for you to arrange your data. You also can download the example files and use them to try the functions provided in this page.

→ Please make sure that per group per '*.xlsx' file.

→ Please put gene symbols of each group in the first column. If you don't have them, please use numbers or something as symbols. In Mountain plot, the corresponding gene symbol will show up when you put your mouse on any markers. If there are more than one groups, only genes that are common in all groups will be plotted. Therefore, they are necessary information for creating the plot.

→ Please put the start location for the genes in the second column. They will be used as the x-axis values in the Mountain plot.

→ Please put the values in the rest of the table.

Plotting Mountain plot of mean values of copy numbers in the samples of two groups. The data are in 'mountain1.xlsx' and 'mountian2.xlsx'. Then customize the marker color of group2 samples into blue.

You may not like the colors and sizes of the markers plotted in default. Therefore, you can use the Figure DIY options to change the color, size and shapes of markers.

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  • Last modified: 2019/07/06 13:06
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