Irrationone/scvis

R wrapper for scvis library

scvis

scvis is a wrapper for the scvis python package for dimension reduction of high-dimensional biological data, especially single-cell RNA-sequencing (scRNA-seq) data. It is adapted from https://bitbucket.org/jerry00/scvis-dev/.

Dependencies:

• tensorflow >= 1.1
• PyYAML >= 3.11
• matplotlib >= 1.5.1
• numpy >= 1.11.1
• pandas >= 0.19.1

Running scvis

1, the train function

The train function can be used to learn a probabilistic parametric mapping (the exact directories of the input files should change based on their actual positions in the computer system):

scvis_train(sce,
			output_dir,
			sce_assay = "logcounts",
			use_reducedDim = FALSE,
			reducedDim_name = NULL,
			config_file = NULL,
			data_label_file = NULL,
			normalize = NULL)

A trained model is saved in the folder <output_dir>/model/

In addition to the model file, the low-dimensional embedding and the log-likelihoods are also written to two files in <output_dir>, (the log-likelihood file is named as *_log_likelihood.tsv).

The different components of the objective function are also saved to a file (*_obj.tsv).

By default, the desired data from sce is normalized by the maximum absolute value. If you want to provide a positive float number for normalization, you can set normalize = your_number.

Another important parameter is config_file, which allows you to set various parameters. If you want to use your own config file, you can pass it as a parameter. The default config file is in scvis/inst/python/config/model_config.yaml, and you can use this file as a template to set parameters.

2, the map function

After learning a probabilistic parametric mapping, the map function can be used to add new data to an existing embedding:

scvis_map(sce,
			output_dir,
			sce_assay = "logcounts",
			use_reducedDim = FALSE,
			reducedDim_name = NULL,
			config_file = NULL,
			data_label_file = NULL,
			normalize = NULL)

As for calling the scvis_train() command, this command will also output the likelihood files and the low-dimensional embedding files, but without the model files and the objective function trace file and plots.

The data matrix files for calling both train and map should be normalized similarly, i.e., the parameters used to normalize the training data should be used to normalize the test data. This is the default setting. You can also pass a positive float number to normalize your data: normalize = your_number.

For map, you can also pass the config file as a parameter: config_file. Notice that the config_file for scvis_train() and scvis_map() should be the same.