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Version 1.2.0
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ChuanXu1 committed Aug 22, 2022
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7 changes: 7 additions & 0 deletions CHANGELOG.md
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# Changelog
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## CellTypist 1.2.0 (August 22, 2022)
- Report all model url request errors (including timeout error) [#28](https://github.com/Teichlab/celltypist/issues/28)
- Add `with_mean` parameter in [celltypist.train](https://celltypist.readthedocs.io/en/latest/celltypist.train.html) to optimize RAM usage by not subtracting the mean [#29](https://github.com/Teichlab/celltypist/issues/29)
- Raise warning instead of error during prediction when the input expression matrix contains only a subset of genes
- Reformat [docs](https://celltypist.readthedocs.io/en/latest/?badge=latest#) with independent pages for main functions
- Support dot plot when the prediction comes from the 'prob match' mode [#33](https://github.com/Teichlab/celltypist/issues/33)
- Disable hard-coded `max_iter` in [celltypist.train](https://celltypist.readthedocs.io/en/latest/celltypist.train.html) with default values varying according to dataset sizes
## CellTypist 1.1.0 (June 09, 2022)
- Add citation (PMID: 35549406)
- Add Docker/Singularity container
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2 changes: 1 addition & 1 deletion README.md
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The downstream workflow is the same as that from one-pass data training.

### General parameters relating to runtime and RAM usage
`max_iter`: when `celltypist.train` does not converge for a long time, setting `max_iter` to a lower number (default to 1000) can reduce runtime at a possible cost of a suboptimal model.
`max_iter`: when `celltypist.train` does not converge for a long time, setting `max_iter` to a lower number can reduce runtime at a possible cost of a suboptimal model.

`with_mean`: when the training data is a sparse matrix, setting `with_mean = False` will preserve sparsity by skipping the step of subtraction by the mean during scaling, and thus lower the RAM usage at the cost of a suboptimal model.

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2 changes: 1 addition & 1 deletion celltypist/__init__.py
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from .train import train
from .plot import dotplot

__version__ = "1.1.0"
__version__ = "1.2.0"

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