Mercurial > repos > iuc > scanpy_remove_confounders
comparison remove_confounders.xml @ 14:c65fce500915 draft default tip
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 9d49b2a98de059ae9a053dc1c5a23537cf0311de
| author | iuc |
|---|---|
| date | Sat, 18 May 2024 18:30:17 +0000 |
| parents | 458e8f43a775 |
| children |
comparison
equal
deleted
inserted
replaced
| 13:d18e7a6e5cd4 | 14:c65fce500915 |
|---|---|
| 178 | 178 |
| 179 Regress out unwanted sources of variation, using simple linear regression. This is | 179 Regress out unwanted sources of variation, using simple linear regression. This is |
| 180 inspired by Seurat's `regressOut` function in R. | 180 inspired by Seurat's `regressOut` function in R. |
| 181 | 181 |
| 182 More details on the `scanpy documentation | 182 More details on the `scanpy documentation |
| 183 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.regress_out.html>`__ | 183 <https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.regress_out.html>`__ |
| 184 | 184 |
| 185 Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct` | 185 Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct` |
| 186 ================================================================================== | 186 ================================================================================== |
| 187 | 187 |
| 188 This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the | 188 This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the |
| 198 ======================================================== | 198 ======================================================== |
| 199 | 199 |
| 200 Corrects for batch effects by fitting linear models, gains statistical power via an EB framework where information is borrowed across genes. This uses the implementation of ComBat | 200 Corrects for batch effects by fitting linear models, gains statistical power via an EB framework where information is borrowed across genes. This uses the implementation of ComBat |
| 201 | 201 |
| 202 More details on the `scanpy documentation | 202 More details on the `scanpy documentation |
| 203 <https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.combat.html>`__ | 203 <https://scanpy.readthedocs.io/en/stable/api/generated/scanpy.pp.combat.html>`__ |
| 204 | 204 |
| 205 | 205 |
| 206 ]]></help> | 206 ]]></help> |
| 207 <expand macro="citations"/> | 207 <expand macro="citations"/> |
| 208 </tool> | 208 </tool> |