Run scGNN

Program scGNN.py is the main entrance of scGNN to impute and clustering. There are quite a few parameters to define to meet users’ requirements.

Required

• datasetName defines the folder of scRNA-Seq
• LTMGDir defines folder of the preprocessed LTMG output
• outputDir Output folder of the results

Clustering related

• clustering-method Clustering method on identifying celltypes from the embedding. Default is LouvainK: use Louvain to determine the number of the clusters and then use K-means. Supporting clustering type: Louvain/KMeans/SpectralClustering/AffinityPropagation/AgglomerativeClustering/AgglomerativeClusteringK/Birch/BirchN/MeanShift/OPTICS/LouvainK/LouvainB
• n-clusters predefines the number of clusters, it only used for clustering methods need a number of clusters input as KNN
• maxClusterNumber defines the maximum number of cluster allowed, default is 30. This parameter prevents extreme cases that too many clusters identified by Louvian clustering
• minMemberinCluster defines the minimum number of cells in a cluster, default is 5. This parameter prevents extreme cases that too many clusters identified by Louvain clustering.
• resolution controls the number of clusters identified by Louvain clustering. This parameter can be set between 0.4 and 1.2 in most cases. According to results on benchmarks, we set default ‘auto’.

Optional: Hyperparameters

• EM-iteration defines the number of iteration, default is 10
• Regu-epochs defines epochs in Feature Autoencoder initially, default is 500
• EM-epochs defines epochs in Feature Autoencoder in the iteration, default is 200
• cluster-epochs defines epochs in the Cluster Autoencoder, default is 200
• k is k of the K-Nearest-Neighour Graph
• knn-distance distance type of building K-Nearest-Neighour Graph, supported type: euclidean/cosine/correlation (default: euclidean)
• GAEepochs Number of epochs to train in Graph Autoencoder

Optional: Performance

• quickmode whether or not to bypass the Cluster Autoencoder.
• useGAEembedding whether use Graph Autoencoder
• regulized-type is the regularized type: noregu/LTMG, default is to use LTMG
• alphaRegularizePara alpha in the manuscript, the intensity of the regularizer
• EMregulized-type defines the imputation regularizer type:noregu/Graph/Celltype, default: Celltype
• gammaImputePara defines the intensity of LTMG regularizer in Imputation
• graphImputePara defines the intensity of graph regularizer in Imputation
• celltypeImputePara defines the intensity of celltype regularizer in Imputation
• L1Para defines the intensity of L1 regularizer, default: 1.0
• L2Para defines the intensity of L2 regularizer, defualt: 0.0
• saveinternal whether output internal results for debug usage

Optional: Speed

• no-cuda defines devices in usage. Default is using GPU, add –no-cuda in command line if you only have CPU.
• coresUsage defines how many cores can be used. default: 1. Change this value if you want to use more.

Example:

CSV format

For CSV format, we need add –nonsparseMode

Without LTMG:

python -W ignore scGNN.py --datasetName GSE138852 --datasetDir ./  --outputDir outputdir/ --EM-iteration 2 --Regu-epochs 50 --EM-epochs 20 --quickmode --nonsparseMode

(Optional) Using LTMG:

python -W ignore scGNN.py --datasetName GSE138852 --datasetDir ./ --LTMGDir ./ --outputDir outputdir/ --EM-iteration 2 --Regu-epochs 50 --EM-epochs 20 --quickmode --nonsparseMode --regulized-type LTMG

10X format

Without LTMG:

python -W ignore scGNN.py --datasetName 481193cb-c021-4e04-b477-0b7cfef4614b.mtx --datasetDir liver/ --outputDir outputdir/ --EM-iteration 2 --Regu-epochs 50 --EM-epochs 20 --quickmode

(Optional) Using LTMG:

python -W ignore scGNN.py --datasetName 481193cb-c021-4e04-b477-0b7cfef4614b.mtx --LTMGDir liver/ --datasetDir liver/ --outputDir outputdir/ --EM-iteration 2 --Regu-epochs 50 --EM-epochs 20 --quickmode --regulized-type LTMG

On these demo dataset using single cpu, the running time of demo codes is ~33min/26min. User should get exact same results as paper shown with full running time on single cpu for ~6 hours. If user wants to use multiple CPUs, parameter –coresUsage can be set as all or any number of cores the machine has.