ProteinCenter User Manual
Table of Contents

Chapter 18. Heat Maps view

Table of Contents

18.1. Heat maps
18.1.1. The heat map view
18.2. How to generate a heat map
18.2.1. Generating a heat map for a comparison or dataset

18.1. Heat maps

ProteinCenter can generate heat maps based on the supplementary data contained in datasets, in fact any set of numeric types of supplementary data are applicable to this approach.

Heat map generation is not limited to the protein, peptide, cluster, gene or chromosome level, but can also be aggregated on the annotations of the proteins (e.g. KEGG pathways), instead of the proteins themselves. The supported types of annotation include: GO Slim and GO terms (with and without more general terms), KEGG and UniProt pathways, enzyme codes (with and without more general terms), PFAM and InterPro domains, as well as diseases and keywords.

When a comparison is selected, the heat map spans datasets as well as supplementary data, enabling a comparison of the same supplementary value types across datasets.

18.1.1. The heat map view

Heat map generation consists of selecting a suitable set of supplementary data and setting the data processing and transformation parameters. The heat map result will be one image containing all the selected data, but filtered by the missing values criteria.

Figure 18.1. The heat map view

The heat map view

  1. Result Summary - An overview of the aggregation result

    • The total number of rows. This depends on the type of data aggregation selected(e.g. proteins, peptides, keywords etc.)

    • The resulting number of rows. The number of rows (e.g. proteins, peptides, keywords etc.) that passes the "missing values" criteria.

  2. The selected supplementary metadata.

  3. Identifiers for the selected aggregation level, in this example protein accession keys.

  4. A legend displaying the used color scale.

  5. A legend showing the colors assigned for special values (missing, infinite & below zero).

  6. A magnifying glass icon, which will display a higher resolution version of the heat map.

  7. A CSV disc icon, which will export all heat map data to a comma-separated text file.

18.1. Quantitation coloring of KEGG pathway maps

When heat mapping KEGG pathways, quantitative coloring of each KEGG pathway map can be obtained by clicking individual cells of the heat map. The supplementary data values for the selected column will be transformed and colored according to the current settings, and each element of the selected KEGG pathway will display a color proportional to the aggregated values for all proteins of that KEGG element.

Figure 18.2. Example of quantitation coloring on a KEGG pathway map

Example of quantitation coloring on a KEGG pathway map

18.2. How to generate a heat map

This section gives a step-by-step introduction to heat map generation for datasets and comparisons.

18.2.1. Generating a heat map for a comparison or dataset

Select a comparison or single dataset for analysis, and go to the heat map pane:

Then follow these steps:

Figure 18.3. The heat map menu

The heat map menu

  1. Select whether to heat map all proteins in a dataset, or only the selected ones.

  2. Select whether to use proteins, peptides, clusters, genes, chromosomes or a type of annotation. This determines the basic unit of analysis, which the heat map refers to.

  3. Pick a statistics type designating how data points will be treated to extract a single numerical value, which can then be converted into a color.

  4. If your data has values that lie far outside the general value range, pick a logarithmic function to keep the color scale from being distorted.

  5. Set the accepted number of missing values per row. E.g. some of your experimental data might have only two quantitative ratios, while others have three.

  6. Choose the scale axis to be used for mapping values to colors. If your data is centered around zero, use "min-0-max"; if unity is the center, use "0-1-max". If you do not wish to enforce a center on the data, and obtain a better dynamic range resolution on purely positive or purely negative values, use "min-max".

  7. Select the supplementary data to be used as dimensions for the heat map. Data types pertaining to various quantitative measures of protein expression are usually the best candidates (e.g. QR#, AQR#, GD#, emPAI, etc.). Numbers in square brackets following a data type indicate the number of missing values for that type. Data types that have values for all experimental data in the dataset(s) will not display missing value brackets. Selecting data types with a minimum number of missing values (or obtaining data with complete quantitation in general) will yield the most useful result. The ordering of the selected supplementary data only reflects the order in which they will be displayed in the horizontal axis on the heat map.

  8. Press to generate the heat map for the selected supplementary data.