Predicting miRNA Expression Level in Single-Cell Clusters for hub-miRNA Discovery. Online application is deposited at https://awi.cuhk.edu.cn/~HubmiR/
The following packages are required for running HubmiR:
R
Seurat
python
sys, torch, torch.nn, numpy, pandas, sklearn.model_selection
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Step1: Process the input data
location/for/Rscript /location/for/package/HubmiR/script/input_processing.R args[1] args[2] args[3]
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ALL Parameters should be quoted by "" or ''.
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parameter
args[1]
select which type of pooling of data: "celltypeavg" for average of annotated cell type. "pooledavg" for random pooling of cells in the same cell type. -
parameter
args[2]
file directory for input profile. Format see theDemo_input.csv
in files folder. This directory should contain ONLY the input files. End with "/". -
parameter
args[3]
output directory. End with "/".
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Step2: Input the processed data into HubmiR for miRNA expression profile prediction
location/for/python /location/for/package/HubmiR/script/DNN.py args[4]
.- ALL Parameters should be quoted by "" or ''.
- parameter
args[4]
Same directory asargs[3]
where the processed file(s) was(were) stored. End with "/".