The following table describes the features of the Heat Map.

Heat Map parameters

Parameters and Settings

Description

Parameters in the left pane

 

Color By

Displays the selected color bars for the selected variables above the heat map.

Filter By

Filter results by:

  • Stimulation
  • Quan Channel
  • Sample

By default, all the checkboxes are selected. Clearing a checkbox removes the corresponding item from the analysis.

Column Settings

  • Show Clusters—Displays the dendogram above the heat map.
  • Show Labels—Displays the column labels below the heat map.

Row Settings

  • Show Clusters—Displays the dendogram to the left of the heat map.
  • Show Labels—Displays the labels across the bottom of the heat map.

Color Scheme

Specifies the color scheme

Distance Function

Specifies the distance function to use for calculating the distance between data points.

  • Euclidean—Computes the Euclidean distance between two data vectors, which is the geometric distance in the multidimensional space.
  • Manhattan—Computes the city-block (Manhattan) distance between two vectors. The Manhattan distance between two data items is the sum of the differences of their corresponding components. (In most cases, the Manhattan distance measure yields results similar to the simple Euclidean distance, but the effect of outliers is dampened, because they are not squared.
  • Maximum–Computes the maximum distance on any one of the dimensions between two vectors. Use this function to define two objects as different if they differ in any one of the dimensions.
  • Pearson—Computes the Pearson product-moment correlation, which is a measure for the shape similarity between two clusters.
  • Squared Euclidean—Computes the squared Euclidean distance between two data vectors. The Euclidean Squared distance metric uses the same equation as the Euclidean distance metric, but does not take the square root.

Linkage Method

  • Average–Computes the distance between two clusters as the average distance between all pairs of objects in the two different clusters.
  • Centroid–Computes the distance between two clusters as the difference between centroids. The centroid of a cluster is the average point in the multidimensional space.
  • Complete–Computes the distance between two clusters as the greatest distance between any two objects in the different clusters (furthest neighbors).
  • Median–Computes the distance between two clusters as the difference between centroids, using the size of each cluster as a weighting factor.
  • Single–Computes the distance between two clusters as the distance of the two closest objects (nearest neighbors) in the clusters.
  • Ward–Computes the distance between two clusters using Ward's method. Ward's method uses an analysis of variance approach to evaluate the distances between clusters. The smaller the increase in the total within-group sum of squares as a result of joining two clusters, the closer they are. The within-group sum of squares of a cluster is defined as the sum of the squares of the distance between all objects in the cluster and the centroid of the cluster. Ward's method tends to produce compact groups of well-distributed size.
  • Weighted Average–Computes the distance between two clusters as the average distance between all pairs of objects in the two different clusters, using the size of each cluster as a weighting factor.

X-Axis Label

  • Specifies the type of label: All Factors, Selected Factors, File ID, Full File Name, Sample Name

Parameters in right pane

 

Data Source

Specifies the result table for the source data.

  • Proteins: Abundances
  • Proteins: Abundances (Normalized)
  • Proteins: Abundances (Groups)
  • Protein: Abundances (Scaled)
  • Peptide Groups: Abundances
  • Peptide Groups: Abundances (Normalized)
  • Peptide Groups: Abundances (Groups)
  • Peptide Groups: Abundances (Scaled)

Scale Values

Specifies if and when to perform a z-score transformation on the data points:

  • None—The application does not scale the data. The heat map legend displays a scale in area counts, and the dendogram nodes display the distance in area counts.
  • Scale After Clustering—Applies a z-score transformation after performing the hierarchical clustering. The heat map legend displays the range of the scaled values, and the dendogram nodes display the distance in area counts.
  • Scale Before Clustering—Applies a z-score transformation before performing the hierarchical clustering. The heat map legend displays the range of the scaled values, and the dendogram nodes display the scaled distance values.

Refresh

Runs the hierarchical clustering analysis on the display abundances report.