scVCMD (Variance-Driven Multitask Clustering of scRNAseq Data) is a multi-task learning method for clustering multiple scRNAseq data from a patient cohort.
- Citation: Zhang, Huanan, Catherine AA Lee, Zhuliu Li, John R. Garbe, Cindy R. Eide, Raphael Petegrosso, Rui Kuang, and Jakub Tolar. "A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa." PLoS computational biology 14, no. 4 (2018): e1006053.
** Lung Data:
nature13173-s4.txt: This file contains single cell RNA-seq expression data (log2(FPKM) values) for all 80 lung epithelial cells at E18.5 together with the putative cell type of each cell in a .txt file. The last two are bulk values.
** mESC data:
- cell_states_condition.txt: the label files for mESC data. (the data matrix is needed to be downloaed at below)
- nCountGeneBatchAdjusted.csv: Download from http://compbio.cs.umn.edu/wp-content/uploads/2018/07/nCountGenesBatchAdjusted.csv
** Code:
*** To test the lung data, run the following two scripts
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process_Lung_data.m: Process Lung data with Matlab to generate the processed Lung data in Lung_data.mat.
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Lung_data_test.m: Run scVDMC algorithm on Lung_data.mat.
*** To test the mESC data; run the following two scripts
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process_mESC_data.m: Process mESC data with Matlab to generate the processed mESC data in mESC_data.mat.
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mESC_data_test.m: Run scVDMC algorithm on mESC_data.mat.
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scVDMC.m: scVDMC function.