From 1db6c46e2c17813d8f5af57663b1e987bf76018e Mon Sep 17 00:00:00 2001
From: Amirhossein Naghsh Nilchi <amirnnilchi@gmail.com>
Date: Sun, 25 Aug 2024 00:46:50 +0200
Subject: [PATCH] fix lint remove.xml

---
 tools/scanpy/remove_confounders.xml | 12 ++----------
 1 file changed, 2 insertions(+), 10 deletions(-)

diff --git a/tools/scanpy/remove_confounders.xml b/tools/scanpy/remove_confounders.xml
index 0fc9b8e9a43..8644cdd0d1e 100644
--- a/tools/scanpy/remove_confounders.xml
+++ b/tools/scanpy/remove_confounders.xml
@@ -76,7 +76,6 @@ sc.pp.combat(
     adata,
     key='$method.key',
     inplace=True)
-<<<<<<< HEAD
 
 #else if str($method.method) == 'external.pp.bbknn':
 sc.external.pp.bbknn(
@@ -137,8 +136,6 @@ sc.external.pp.scanorama_integrate(
     approx=$method.approx,
     alpha=$method.alpha,
     batch_size=$method.batch_size)
-=======
->>>>>>> a567b2ac1 (add rank_genes_groups_tracksplot and update some other params)
 #end if
 
 @CMD_anndata_write_outputs@
@@ -151,12 +148,9 @@ sc.external.pp.scanorama_integrate(
                 <option value="pp.regress_out">Regress out unwanted sources of variation, using 'pp.regress_out'</option>
                 <!-- <option value="external.pp.mnn_correct">Correct batch effects by matching mutual nearest neighbors, using 'pp.mnn_correct'</option> -->
                 <option value="pp.combat">Correct batch effects with ComBat function, using 'pp.combat'</option>
-<<<<<<< HEAD
                 <option value="external.pp.bbknn">Batch effect removal with Batch balanced KNN (BBKNN), using 'external.pp.bbknn'</option>
                 <option value="external.pp.harmony_integrate">Integrate multiple single-cell experiments with Harmony, using 'external.pp.harmony_integrate'</option>
                 <option value="external.pp.scanorama_integrate">Integrate multiple single-cell experiments with Scanorama, using 'external.pp.scanorama_integrate'</option>
-=======
->>>>>>> a567b2ac1 (add rank_genes_groups_tracksplot and update some other params)
             </param>
             <when value="pp.regress_out">
                 <param argument="layer" type="text" value="" label="Which element of layers to regress on">
@@ -204,7 +198,6 @@ sc.external.pp.scanorama_integrate(
                     <expand macro="sanitize_query"/>
                 </param>
             </when>
-<<<<<<< HEAD
             <when value="external.pp.bbknn">
                 <param argument="batch_key" type="text" value="batch" label="Batch key for the concatenate">
                     <expand macro="sanitize_query"/>
@@ -273,8 +266,6 @@ sc.external.pp.scanorama_integrate(
                 <param argument="alpha" type="float" value="0.1" label="Alignment score minimum cutoff"/>
                 <param argument="batch_size" type="integer" value="5000" label="The batch size used in the alignment vector computation" help="Useful when integrating very large (>100k samples) datasets. Set to large value that runs within available memory."/>
             </when>
-=======
->>>>>>> a567b2ac1 (add rank_genes_groups_tracksplot and update some other params)
         </conditional>
         <expand macro="inputs_common_advanced"/>
     </inputs>
@@ -486,8 +477,9 @@ inspired by Seurat's `regressOut` function in R.
 More details on the `scanpy documentation
 <https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.regress_out.html>`__
 
+
 Correct batch effects by matching mutual nearest neighbors, using `external.pp.mnn_correct`
-==================================================================================
+===========================================================================================
 
 This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the
 original order containing corrected expression values or a concatenated matrix or AnnData object.