DAISM-DNN^XMBD: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks
Authors/Creators
- 1. School of Informatics, Xiamen University, China.
- 2. School of Life Science, Xiamen University, China.
- 3. School of Medicine, Xiamen University, China.
- 4. Amoy Diagnostics, Xiamen, China.
- 5. Aginome Scientific, Xiamen, China.
- 6. Research Unit of Cellular Stress of CAMS, Cancer Research Center of Xiamen University, School of Medicine, Xiamen University, China.
Description
Understanding the immune cell abundance of cancer and other disease-related tissues has an important role in guiding disease treatments. Computational cell type proportion estimation methods have been previously developed to derive such information from bulk RNA sequencing (RNA-seq) data. Unfortunately, our results show that the performance of these methods can be seriously plagued by the mismatch between training data and real-world data. To tackle this issue, we propose the DAISM-DNN^XMBD (denoted as DAISM-DNN) pipeline that trains a deep neural network (DNN) with dataset-specific training data populated from a certain amount of calibrated samples using DAISM, a novel Data Augmentation method with an In Silico Mixing strategy. The evaluation results demonstrate that the DAISM-DNN pipeline
outperforms other existing methods consistently and substantially for all the cell types under evaluation on real-world datasets.
Notes
Files
DAISM-XMBD-2.0.5.zip
Files
(5.1 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/xmuyulab/DAISM-XMBD/tree/v2.0.1 (URL)