TCGA KIRC Cohort Cellular Morphometric Univariate Summarization
Submitted by Hang Chang on Fri, 04/14/2017 - 09:37
Background:
The Cancer Genome Atlas (TCGA) has been one of the clearing houses of genome-wide array data for the understanding of the molecular basis of cancer from large cohorts. These analyses are intrinsically from bulk measurements of mixed cell types, derived from frozen biopsy sections that include tissues with mixed histopathology and/or microanatomies (e.g., tumor, stroma). While bulk array profiling may provide insights into molecular aberrations, it provides only an average genome-wide measurement for a biopsy and fails to reveal inherent cellular composition and heterogeneity of a tumor. On the other hand, histology sections do not provide standardized measurements, but they are rich in content and continue to be the gold standard for the assessment of tissue neoplasm.
In this project, we aims to provide univariate cellular morphoetric summarization for TCGA Kidney Renal Clear Cell Carcinoma (KIRC) Cohort, which can be further utilized for various end points, such as,
- integrated analysis with OMIC data; and
- construction of predictive models of clinical outcome.
Examples can be found in our previous publication:
- Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.
Visualization Tool: KIRC Whole Slide Images (WSI) and the corresponding segmentation results.
Description:
The files in this directory are summarized as follows:
- README.txt provides the description of files.
- patient_equal_bin_widths_histograms.txt provides a datamatrix of equal bin widths histograms at the patient level, where each column represents a patient.
- patient_equal_probability_histograms.txt provides a datamatrix of equal probability histograms at the patient level, where each column represents a patient.
- patient_statistics.txt provides basis statistics per feature per patient.
The total number of morphometric features, involved in this summarization is 53, which are derived using the methdology described in:
- Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.
and are summarized as follows:
| Feature type | Feature name | Annotation |
| Cellular organization | cell_voronoi_area | measured at the log10 scale |
| Cellular organization | cellularity | cell density per unit area, inverse of cell_voronoi_area, at the log10 scale |
| Cellular organization | edge_length | |
| Cytoplasm feature | background_intensity_mean | |
| Cytoplasm feature | background_intensity_sd | |
| Cytoplasm feature | contrast_mean | |
| Cytoplasm feature | gradient_mean | |
| Cytoplasm feature | gradient_sd | |
| Cytoplasm feature | intensity_mean | |
| Cytoplasm feature | intensity_sd | |
| Cytoplasm feature | intensity_total | |
| Nuclear feature | area | |
| Nuclear feature | aspect ratio | |
| Nuclear feature | background_intensity_mean | |
| Nuclear feature | background_intensity_sd | |
| Nuclear feature | bending_energy_s1_mean | |
| Nuclear feature | contrast_mean | |
| Nuclear feature | curvature_s1_sd | |
| Nuclear feature | deviation_from_polygon_convexity | |
| Nuclear feature | gradient_mean | |
| Nuclear feature | gradient_sd | |
| Nuclear feature | intensity_mean | |
| Nuclear feature | intensity_sd | |
| Nuclear feature | intensity_total | |
| Nuclear feature | major_axis | |
| Nuclear feature | max_curvature_s1 | |
| Nuclear feature | minor_axis | |
| Nuclear feature | orientation | |
| Nuclear feature | perimeter | |
| Nuclear feature | texture_feature_0_mean | 1st order steerable filter at angle 0 |
| Nuclear feature | texture_feature_0_sd | 1st order steerable filter at angle 0 |
| Nuclear feature | texture_feature_1_mean | 2nd order steerable filter at angle 0 |
| Nuclear feature | texture_feature_1_sd | 2nd order steerable filter at angle 0 |
| Nuclear feature | texture_feature_2_mean | 3rd order steerable filter at angle 0 |
| Nuclear feature | texture_feature_2_sd | 3rd order steerable filter at angle 0 |
| Nuclear feature | texture_feature_3_mean | 1st order steerable filter at angle 0.78 |
| Nuclear feature | texture_feature_3_sd | 1st order steerable filter at angle 0.78 |
| Nuclear feature | texture_feature_4_mean | 2nd order steerable filter at angle 0.78 |
| Nuclear feature | texture_feature_4_sd | 2nd order steerable filter at angle 0.78 |
| Nuclear feature | texture_feature_5_mean | 3rd order steerable filter at angle 0.78 |
| Nuclear feature | texture_feature_5_sd | 3rd order steerable filter at angle 0.78 |
| Nuclear feature | texture_feature_6_mean | 1st order steerable filter at angle 1.56 |
| Nuclear feature | texture_feature_6_sd | 1st order steerable filter at angle 1.56 |
| Nuclear feature | texture_feature_7_mean | 2nd order steerable filter at angle 1.56 |
| Nuclear feature | texture_feature_7_sd | 2nd order steerable filter at angle 1.56 |
| Nuclear feature | texture_feature_8_mean | 3rd order steerable filter at angle 1.56 |
| Nuclear feature | texture_feature_8_sd | 3rd order steerable filter at angle 1.56 |
| Nuclear feature | texture_feature_9_mean | 1st order steerable filter at angle 2.34 |
| Nuclear feature | texture_feature_9_sd | 1st order steerable filter at angle 2.34 |
| Nuclear feature | texture_feature_10_mean | 2nd order steerable filter at angle 2.34 |
| Nuclear feature | texture_feature_10_sd | 2nd order steerable filter at angle 2.34 |
| Nuclear feature | texture_feature_11_mean | 3rd order steerable filter at angle 2.34 |
| Nuclear feature | texture_feature_11_sd | 3rd order steerable filter at angle 2.34 |
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