Machine learning classifier of tumor cells
ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.
| System | Target | Derivation | Build status |
|---|---|---|---|
| x86_64-linux | /gnu/store/65axf0zxcjimx8glzrc0ygpgyq5y96pv-python-ikarus-0.0.2.drv | ||
| i686-linux | /gnu/store/jyywy1i43mhxhpiwm0lkn69nzg31imnl-python-ikarus-0.0.2.drv |
| Linter | Message | Location |
|---|---|---|
| description Validate package descriptions | description should start with an upper-case letter or digit | |
| profile-collisions Report collisions that would occur due to propagated inputs | propagated inputs python-tornado@5.1.1 and python-tornado@6.1 collide |