scHPF train¶
Basic usage¶
A typical command to train an scHPF model (using data prepared by the
scHPF prep
command):
scHPF train -i TRAIN_FILE -o OUTDIR -p PREFIX -k 7 -t 5
This command performs approximate Bayesian inference on scHPF with, in this instance, seven factors and five different random initializations. scHPF will automatically select the trial with the lowest negative log-likelihood, and save the model in the OUTDIR in a serialized joblib file.
Input file format¶
scHPF’s train command accepts two formats:
Matrix Market (.mtx) files, where rows are cells, columns are genes, and values are nonzero molecular counts. Matrix market files are output by the current
scHPF prep
command.Tab-delimited COO matrix coordinates, output by a previous version of the preprocessing command. These files are essentially the same as .mtx files, except they do not have a header and are zero indexed.
Debugging¶
Hint
If you get an error like “Inconsistency detected by ld.so: dl-version.c: 224: _dl_check_map_versions” and are running numba 0.40.0, try downgrading to 0.39.0.
Hint
If you get an error like “Segmentation fault (core dumped)” and are running Python 3.7.4, try upgrading numba to version 0.45 or downgrading Python to 3.7.3 python [More details]
Complete options¶
For complete options, see the complete CLI reference or use the
-h
option on the command line:
scHPF train -h