Mar 30, 2020

DAISM-DNN: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks

bioRxiv
Jérôme RibotJonathan D. Touboul

Abstract

Understanding the immune-cell abundances of cancer and other disease-related tissues has an important role in guiding cancer treatments. We propose data augmentation through in silico mixing with deep neural networks (DAISM-DNN), where highly accurate and unbiased immune-cell proportion estimation is achieved through DNN with dataset-specific training data created from partial samples from the same batch with ground truth cell proportions. We evaluated the performance of DAISM-DNN on three publicly available real-world datasets and results showed that DAISM-DNN is robust against platform-specific variations among different datasets and outperforms other existing methods by a significant margin on all the datasets evaluated.

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Mentioned in this Paper

Health Center
Visual Processing
Anatomic Structures
Neurons
Spatial Distribution
Vision
Visual Perception
Optical Imaging
Entire Brodmann Areas 17 (Striate Cortex),18 (Parastriate Cortex) and 19 (Peristriate Cortex) of Occipital Lobe
Cellular Component Organization

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