The MFmap is a semi-supervised generative model integrating omics data, matching cell lines to cancer subtypes. Publication based on MFmap:
- [MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes] (https://www.biorxiv.org/content/10.1101/2021.07.15.452446v1.full)
MFmap shiny app is publically available at (http://h2926513.stratoserver.net:3838/MFmap_shiny/)
The original mutation and CNV data are represented as a binary matrix indicating the presence/absence of a DNA alteration in a cell line or a bulk tumour sample. This sparse binary matrix is projected onto a cancer reference network (Huang et al.,Bioinformatics 2018) and network diffusion algorithm is used to propagate the signals of single somatic events onto their network neighbours, resulting in continuous matrix with the same dimensions of the original binary matrix.
MFmap has three components: an encoder, a classifier and a decoder, encoded by different colours in the above figure. Input layer of the encoder contains two views, network smoothed mutation and CNV data are concatenated and input into DNA view, RNA view inputs gene expression data. The encoder maps the input data of each sample into a latent representation vector <img src="https://render.githubusercontent.com/render/math?math=\vec{z}". The decoder uses <img src="https://render.githubusercontent.com/render/math?math=\vec{z}" to reconstruct the DNA and RNA views in its output layer. Subtypes of bulk tumours are used to train the classifier. During training, the classifier predicts subtypes of cell lines.
To organise and summarise sample associations we used the visualisation concept of OncoGPS(Kim et al.,Cell Syst. 2017). The biological meanings of latent representations are annotated and MFmap reference map is generated. Both steps are based on bulk tumour latent representations learnt by MFmap. Samples can then be projected onto the MFmap reference map to visualise sample properties such as drug sensitivity and subtypes, complemented with the information of sample relationship.
Clone this repository to use MFmap
git clone https://github.com/mcmzxx/mfMap.py.git
cd mfMap.py
# run the example using simulated data
bash run-example.sh
# run the example using realistic colon cancer data
mkdir data_bak
cd data_bak
# download the data from data repository (https://cloud.hs-koblenz.de/s/WFWjMq9pJ8i29WD)
cd ..
bash run_example_real_data.shMFmap takes inputs of datasets containing both cell lines bulk tumours: (1) gene expression profile which is a gene by sample matrix, (2) DNA profile which is a gene by sample matrix, and (3) cancer subtype labels of bulk tumours.
In gene expression profile or DNA profile, values should be separated by tabs.
| barcode | TCGA.A6.2678.01 | TCGA.AA.3950.01 | TCGA.DM.A1HB.01 | CL40_LARGE_INTESTINE | SW403_LARGE_INTESTINE | SNUC4_LARGE_INTESTINE |
|---|---|---|---|---|---|---|
| HSPA2 | 0.178085 | 0.573315 | 0.503795 | 0.547310 | 0.243164 | 0.495841 |
| HSPA6 | 0.337385 | 0.556164 | 0.487331 | 0.531813 | 0.550296 | 0.784094 |
| HIST1H4I | 0.108904 | 0.511286 | 0.450405 | 0.488413 | 0.400680 | 0.446989 |
| HSPA8 | 0.331599 | 0.600488 | 0.503966 | 0.562086 | 0.411304 | 0.495323 |
| B2M | 0.399159 | 0.561251 | 0.518686 | 0.608198 | 0.240596 | 0.479772 |
Cancer subtype label list has the following three columns, separated by tabs.
| barcode | subtype | type |
|---|---|---|
| TCGA.A6.2678.01 | NOLBL | tumor |
| TCGA.AA.3950.01 | CMS1 | tumor |
| TCGA.DM.A1HB.01 | CMS1 | tumor |
| SNUC4_LARGE_INTESTINE | NOLBL | cell |
| SW403_LARGE_INTESTINE | NOLBL | cell |
| CL40_LARGE_INTESTINE | NOLBL | cell |
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