Embedding (TCGA RNASeq)
Source code of applying embedding on TCGA RNASeqV2 RSEM normalized data.
Link
Web Interactive Embedding Projector (powered by TensorFlow)
Gene Embedding Matrix from:
Source Code
Handy python scripts to load data (load_data.py) and functions for handling embeddings (util.py) are included.
Dependencies
- numpy
- pandas
- matplotlib
- seaborn
- networkx
- scipy
- sklearn
- fastai
Usage
- Clone the repo locally.
- Change directory to the local directory.
- Run
python train.py --data $YOUR_INPUT_DATA --out-prefix $OUT --out-dir $OUTPUT_PATH
.
Note.train.py
can only be run on CUDA enabled machine.
Input data must be .csv with oberservation per row and must have an ID column.
Folder Structure
tcga-embedding
| LICENSE
| README.rst
| load_data.py
| train.py
| util.py
└───emb
| gemb_bias_CN.csv
| gemb_bias_normal.csv
| gemb_CN.csv
| gemb_normal.csv
| semb_bias_CN.csv
| semb_bias_normal.csv
| semb_CN.csv
| semb_normal.csv
└───geneSCF
| gemb_d17_top_GO_BP.tsv
| gemb_d22_top_GO_BP.tsv
| gemb_d25_top_GO_BP.tsv
| gemb_d35_bottom_GO_BP.tsv
| gemb_d43_bottom_GO_BP.tsv
| gemb_d46_bottom_GO_BP.tsv
└───ipynb
| tcga_emb_dist.ipynb
| tcga_emb_pca.ipynb
| tcga_emb_subtyping.ipynb
| tcga_ioresponse.ipynb
| tcga_plot_emb_som_pca_heatmap.ipynb
| tcga_plot_gsea_compare.ipynb
| tcga_som.ipynb
| tcga_training_CN.ipynb
| tcga_training_normal.ipynb
└───ref
| genes_gids.tsv
| sid_ca.csv