STrenD: Subspace Trend Discovery

Revision as of 18:55, 21 August 2014

Software Interface

Procedure

1. Load Tab-delimited txt file. If columns are features and rows are samples, File/Load Table; If columns are samples and rows are features, File/Load Rotated Table;

2. Calculate for feature clustering and pair-wise neighborhood similarity (NS);

3. Auto selection: push select for automatic thresholding on NS matrix to provide a list of non-overlapping feature subsets (size >=3). The largest subset, on top of the list, is selected by default;

4. Manual selection: push select to visualize co-clustered NS matrix and select a group of features that have high NS values by left clicking on the top-left starting square and releasing on the right-bottom ending square. The user can also input feature cluster index in the editor, separated by comma;

5. Visualize for dimension reduction on the selected features and provide a 2-D or 3-D visualization using t-SNE("dimension" higher than 3 would be visualized in 2D with a selected pair of dimensions);

6. MST-ordered Heatmap to visualize a heatmap with rows arranged by the depth-first order of MST on selected data and columns arranged by a hierarchical clustering of features. The selected ones are separated by a red line from the rest.

For test dataset "cellCycleMicroarray.txt" with default param settings:

File/Load Rotated Table -> Auto selection:select ->Visualize->MST-ordered Heatmap

Result:

Output files

For test dataset "cellCycleMicroarray.txt" with 17 samples of 3196 dimensions, clustering sigma = 0.8, k = 4:

1. 3196_17_0.8_clustering.txt: agglomerative clustering result, containing index and feature names;

2. 3196_17_0.8_4_NS.txt: pair-wise neighborhood similarity matrix of feature clusters;

3. Shanbhag.txt: intermediate outputs for Shanbhag thresholding;

4. 3196_17_0.8_4_AutoSelFeatures.txt: selected feature index and names;

5. data_selected_vis.txt: table of normalized data with selected features for visualization;

6. vis_coordinates.txt: output coordinates for visualization after dimension reduction by t-SNE.