View source: R/filter_autoencoder.R
autoFilterCV | R Documentation |
Cross-validation is done to determine which genes can not be predicted well, by comparing the autoencoder predicted loss with the loss estimating the gene expression as a constant across cells
autoFilterCV(x, python.module, main, pretrain_file = "", nonmissing_indicator = 1, n_human = 21183L, n_mouse = 21122L, shared_size = 15494L, model.species = NULL, out_dir = ".", batch_size = 32L, write_output_to_tsv = F, fold = 6, samp = 3, epsilon = 1e-10, seed = 1, ...)
x |
Target sparse data matrix of gene by cell. When pretraining is used, the genes should be the same as the nodes used in the pretrained model. If a node gene is missing is the target dataset, set all values of that gene as 0 in |
python.module |
The python module for the Python package |
main |
A Python main module |
pretrain_file |
The pretrained weights file ended with '.hdf5' |
nonmissing_indicator |
A single value 1 or a vector of 0 and 1s to indicate which nodes are missing in the target dataset. Set to 1 for no pretraining. |
model.species |
Should be either 'Human' or 'Mouse' when pretraining is used |
write_output_to_tsv |
If True, then the result of Python is written as .tsv files instead of passing back to R. Default is False. |
fold |
Number of total CV folds |
samp |
Number of sampled folds taken to reduce CV time cost |
... |
Extra parameters passed to Python module |
a list of the filtered predicted data matrix and the CV error
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.