Abstract
We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.
Footnotes
Many novel datasets used for deconvolution. Novel cell type-specific deconvolution analysis with some striking results. Many figures and results overhauled.
List of abbreviations
- RNA-seq
- Next Generation RNA Sequencing
- GEP
- gene expression profile matrix
- SVR
- Support Vector Regression
- DNN
- Deep Neural Network
- scRNA-seq
- single cell RNA-seq
- simulated tissue
- training data generated by mixing proportions of scRNA-seq data
- PBMC
- peripheral blood mononuclear cells
- CCC
- concordance correlation coefficient
- r
- Pearson’s correlation coefficient
- CS
- CIBERSORT
- CSx
- CIBERSORTx
- CPM
- Cell Population Mapping
