Create a Map

You can create a map with your own data by selecting Create map from the File menu, then supplying at least a layout feature file in one of the four following formats.

See Technical Overview section below for an explanation of the pipeline used to create a map.

Features to Lay Out the Map —————————attributes

Features are properties of samples used to lay out the map. The feature file must be in TSV (tab-separated values) format in one of the following forms.

Feature data : AKA clustering data. This is the most basic of the layout input formats where similarities and XY locations will be calculated for you. This contains a full matrix with sample IDs across the top and feature IDs in the first column:

feature   sample_1  sample_2   sample_3  ...
TP53      0.6423    0.7654     0.2345
NAS1      0.2345    0.6423     0.7654
BRCA1     0.7654    0.2345     0.6423

Full similarity : This contains similarity scores between all sample pairs as a full matrix which will be used to calculate xy positions. This has sample IDs across the top and in the first column with similarity scores as the values:

samples     sample_1  sample_2  sample_3  ...
sample_1    0.7654    0.6423    0.9524
sample_2    0.9524    0.7654    0.6423
sample_3    0.6423    0.9524    0.7654

Sparse similarity :This contains similarity scores between the top neighbor samples of each sample as a sparse matrix which will be used to calculate xy positions. This has sample IDs in the first two columns with the the similarity scores in the third column:

sample_1    sample_2    0.9524
sample_1    sample_3    0.76543
sample_2    sample_4    0.6423

XY positions : This is the most processed of the layout input formats, containing the x and y coordinates in two-dimensional space of each sample, as the the example where the header line is optional:

#ID         x       y
sample_1    73.6    63.6
sample_2    63.6    23.8
sample_3    23.8    73.6

Attributes to Color the Map

Note that attributes are optional.

Attributes are properties of samples used to color the map. The attribute file must be in TSV (tab-separated values) format with the attributes IDs across the top and sample IDs in the first column, like:

sample      age   disease stage  ...
sample_1    81    BRCA    IV
sample_2    96    COAD    III
sample_3    52    GBM     II

Missing values: Replace with zero

Check this checkbox to replace missing values with zero in the layout input file of format feature data or full similarity. More on this in the Technical Overview below.


Help in resolving issues is at Create a Map: Troubleshooting.

Technical Overview

The layout input formats described in the `Features to Lay Out the Map`_ section represent different stages of the pipeline used to create a map. Feature data is the beginning of the pipeline, any nxm matrix can be used. Spearman correlations are calculated representing the similarity between all columns in the Feature data matrix. The resulting nxn matrix of Spearman correlations is the Full similarity matrix. The Full similarity matrix is then sparsified by taking the 6 highest Spearman correlations for each sample, this sparsification is the Sparse similarity input format. XY positions are then produced by applying the openOrd layout algorithm to the Sparse similarity representation.

The XY positions are further modified by the hexagonal binning process. The hexagonal binning process first lays a hexagonal tiling over the x-y plane, then assigns each point in the xy space to the nearest hexagon. If a point is assigned to a hexagon that is already occupied, then a breadth-first search on the hexagon tilling is used to find the nearest empty hexagon. If the OpenOrd clustering algorithm is used the size of the hexagons is set to 1. This has been shown to be reasonable with the scaling of the algorithm. If XY positions are input, a hexagon size is set such that hexagons cover 5% of the open space in the plane. The open space is determined by (max x - min x) * (max y - min y), and the area of a hexagon is is sqrt(3)*3/2 *S^2, where S is the side length.

Missing values in Feature data

We strongly encourage users to choose and execute an appropriate method for dealing with missing values before using our pipeline. In general there is not a single method that is best for all types of data. There is an option on the Create Map window to replace any missing values with zeroes. This applies to feature data whose missing values are converted to zero before calculating Spearman similarities. Depending on the distribution of the data our technique of filling with zeroes may be problematic.