scDFC is a deep fusion clustering method for single-cell RNA-seq data. Existing methods either consider the attribute information of each cell or the structure information between different cells. In other words, they cannot sufficiently make use of all of this information simultaneously. To this end, we propose a novel single-cell deep fusion clustering model, which contains two modules, i.e., an attributed feature clustering module and a structure-attention feature clustering module. More concretely, two elegantly designed autoencoders are built to handle both features regardless of their data types.
Python --- 3.6.2
Pandas --- 1.1.5
Tensorflow --- 1.12.0
Keras --- 2.1.0
Numpy --- 1.19.5
Scipy --- 1.5.4
Pandas --- 1.1.5
Scikit-learn --- 0.19.0
Biase:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57249
Darmanis:https://pubmed.ncbi.nlm.nih.gov/26060301/
Enge:https://pubmed.ncbi.nlm.nih.gov/28965763/
Bjorklund:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70580
Sun.1:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128066
Fink:https://www.sciencedirect.com/science/article/abs/pii/S1534580722004932
Sun.2:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128066
Sun.3:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128066
Brown:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137710
The example expression matrix data.tsv of dataset Biase is put into data/Biase. To change datasets, you should type the iuput of code:
parser.add_argument('--dataset_str', default='Biase', type=str, help='name of dataset')
parser.add_argument('--n_clusters', default=3, type=int, help='expected number of clusters')
parser.add_argument('--label_path', default='data/Biase/label.ann', type=str, help='true labels')
# ... other arguments ...python scDFC.pyIf you find this work useful, please consider citing:
Hu, D., Liang, K., Zhou, S., Tu, W., Liu, M., & Liu, X. (2023). scDFC: A deep fusion clustering method for single-cell RNA-seq data. Briefings in Bioinformatics, bbad216. Oxford University Press.