README.md

scDHA

The scDHA software package can perform cell segregation through unsupervised learning, dimension reduction and visualization, cell classification, and time-trajectory inference on single-cell RNA sequencing data.

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How to install:

To run the Goolam example:

How to use the package for new data

To use our package for new data, the package includes these functions: - scDHA: main function, doing dimension reuction and clustering. The input is a matrix with rows as samples and columns as genes. - scDHA.w: plot the normalized weight variances to select suitable cutoff for gene filtering (optional). - scDHA.vis: visualization. The input is demension reduction output. - scDHA.pt: generating pseudotime. The input is demension reduction output. - scDHA.class: classification new data using available one. The inputs consist of train data matrix, train data label and new data matrix. - The result is reproducible by setting seed for these functions. - More detail about parameters for each function could be found in the manual.

Citation:

Duc Tran, Hung Nguyen, Bang Tran, Carlo La Vecchia, Hung N. Luu, Tin Nguyen (2021). Fast and precise single-cell data analysis using a hierarchical autoencoder. Nature Communications, 12, 1029. doi: 10.1038/s41467-021-21312-2 (link)



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scDHA documentation built on Sept. 16, 2021, 1:07 a.m.