Mercurial > repos > iuc > scanpy_remove_confounders

annotate remove_confounders.xml @ 8:80d4e2cac903 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 7b8af82cb8a3438d2a6535986e357527e5c6efec"
author iuc
date Tue, 16 Mar 2021 13:02:50 +0000
parents 215733a383f2
children bf2017df9837
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00305dc2dd6f "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 3b41d687ff30583540d055f6995de00530cca81d"
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1 <tool id="scanpy_remove_confounders" name="Remove confounders" version="@galaxy_version@" profile="@profile@">
1
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2 <description>with scanpy</description>
0
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements"/>
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7 <command detect_errors="exit_code"><![CDATA[
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8 @CMD@
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9 ]]></command>
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10 <configfiles>
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11 <configfile name="script_file"><![CDATA[
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12 @CMD_imports@
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13 @CMD_read_inputs@
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14
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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15 #if $method.method == "pp.regress_out"
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16 sc.pp.regress_out(
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17 adata=adata,
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18 #set $keys = [str(x.strip()) for x in str($method.keys).split(',')]
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19 keys=$keys,
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20 copy=False)
1
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22 #else if $method.method == "pp.mnn_correct"
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23 #for i, filepath in enumerate($methods.extra_adata)
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24 adata_$i = ad.read('$filepath')
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25 #end for
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26
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27 sc.pp.mnn_correct(
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28 adata,
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29 #for i, filepath in enumerate($methods.extra_adata)
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30 adata_$i,
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31 #end for
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32 #if str($methods.var_subset) != ''
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33 #set $var_subset=([x.strip() for x in str($method.var_subset).split(',')])
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34 var_subset=$var_subset,
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35 #end if
1
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36 batch_key='$method.batch_key',
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37 index_unique='$method.index_unique'
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38 #if str($methods.batch_categories) != ''
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39 #set $batch_categories=([x.strip() for x in str($method.batch_categories).split(',')])
a89ee42625ad "planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
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40 batch_categories=$batch_categories,
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41 #end if
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42 k=$method.k,
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43 sigma=$method.sigma,
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44 cos_norm_in=$method.cos_norm_in,
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45 cos_norm_out=$method.cos_norm_out,
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46 svd_dim=$method.svd_dim,
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47 var_adj=$method.var_adj,
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48 compute_angle=$method.compute_angle,
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49 mnn_order='$method.mnn_order',
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50 svd_mode='$method.svd_mode',
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51 do_concatenate=True,
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52 save_raw=True,
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53 n_jobs=\${GALAXY_SLOTS:-4})
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54
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55 #else if $method.method == "pp.combat"
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56 sc.pp.combat(
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57 adata,
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58 key='$method.key',
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59 inplace=True)
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60
0
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61 #end if
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62
9ca360dde8e3 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 92f85afaed0097d1879317a9f513093fce5481d6
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63 @CMD_anndata_write_outputs@
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64 ]]></configfile>
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65 </configfiles>
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66 <inputs>
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67 <expand macro="inputs_anndata"/>
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68 <conditional name="method">
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69 <param argument="method" type="select" label="Method used for plotting">
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70 <option value="pp.regress_out">Regress out unwanted sources of variation, using 'pp.regress_out'</option>
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71 <option value="pp.mnn_correct">Correct batch effects by matching mutual nearest neighbors, using 'pp.mnn_correct'</option>
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72 <option value="pp.combat">Correct batch effects with ComBat function, using 'pp.combat'</option>
0
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73 </param>
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74 <when value="pp.regress_out">
7
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75 <param argument="keys" type="text" value="" label="Keys for observation annotation on which to regress on" help="Keys separated by a comma">
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76 <expand macro="sanitize_query" />
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77 </param>
0
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78 </when>
1
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79 <when value="pp.mnn_correct">
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80 <param name="extra_adata" type="data" multiple="true" optional="true" format="h5ad" label="Extra annotated data matrix" help="They should have same number of variables."/>
7
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81 <param argument="var_subset" type="text" value="" optional="true" label="The subset of vars to be used when performing MNN correction" help="List of comma-separated key from '.var_names'. If not set, all vars are used">
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82 <expand macro="sanitize_query" />
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83 </param>
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84 <param argument="batch_key" type="text" value="batch" label="Batch key for the concatenate">
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85 <expand macro="sanitize_query" />
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86 </param>
1
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87 <param name="index_unique" type="select" label="Separator to join the existing index names with the batch category" help="Leave it empty to keep existing indices">
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88 <option value="-">-</option>
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89 <option value="_">_</option>
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90 <option value=" "> </option>
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91 <option value="/">/</option>
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92 </param>
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93 <param argument="batch_categories" type="text" value="" optional="true" label="Batch categories for the concatenate" help="List of comma-separated key">
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94 <expand macro="sanitize_query" />
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95 </param>
1
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96 <param argument="k" type="integer" value="20" label="Number of mutual nearest neighbors"/>
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97 <param argument="sigma" type="float" value="1" label="The bandwidth of the Gaussian smoothing kernel used to compute the correction vectors"/>
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98 <param argument="cos_norm_in" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Should cosine normalization be performed on the input data prior to calculating distances between cells?"/>
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99 <param argument="cos_norm_out" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Should cosine normalization be performed prior to computing corrected expression values?"/>
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100 <param argument="svd_dim" type="integer" value="" optional="true" label="Number of dimensions to use for summarizing biological substructure within each batch" help="If not set, biological components will not be removed from the correction vectors."/>
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101 <param argument="var_adj" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Adjust variance of the correction vectors?" help="This step takes most computing time."/>
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102 <param argument="compute_angle" type="boolean" truevalue="True" falsevalue="False" checked="false" label="compute the angle between each cell’s correction vector and the biological subspace of the reference batch?"/>
7
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103 <param argument="mnn_order" type="text" value="" optional="true" label="The order in which batches are to be corrected" help="List of comma-separated key. If not set, datas are corrected sequentially">
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104 <expand macro="sanitize_query" />
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105 </param>
1
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106 <param name="svd_mode" type="select" label="SVD mode">
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107 <option value="svd">svd: SVD using a non-randomized SVD-via-ID algorithm</option>
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108 <option value="rsvd" selected="true">rsvd: SVD using a randomized SVD-via-ID algorithm</option>
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109 <option value="irlb">irlb: truncated SVD by implicitly restarted Lanczos bidiagonalization</option>
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110 </param>
0
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111 </when>
1
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112 <when value="pp.combat">
7
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113 <param argument="key" type="text" value="batch" label="Key to a categorical annotation from adata.obs that will be used for batch effect removal">
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114 <expand macro="sanitize_query" />
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115 </param>
0
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116 </when>
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117 </conditional>
6
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118 <expand macro="inputs_common_advanced"/>
0
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119 </inputs>
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120 <outputs>
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121 <expand macro="anndata_outputs"/>
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122 </outputs>
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123 <tests>
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124 <test>
6
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125 <!-- test 0 -->
1
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126 <param name="adata" value="krumsiek11.h5ad" />
0
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127 <conditional name="method">
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128 <param name="method" value="pp.regress_out"/>
1
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129 <param name="keys" value="cell_type"/>
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130 </conditional>
6
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131 <section name="advanced_common">
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132 <param name="show_log" value="true" />
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133 </section>
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134 <output name="hidden_output">
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135 <assert_contents>
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136 <has_text_matching expression="sc.pp.regress_out"/>
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137 <has_text_matching expression="keys=\['cell_type'\]"/>
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138 </assert_contents>
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139 </output>
1
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140 <output name="anndata_out" file="pp.regress_out.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/>
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141 </test>
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142 <!--<test>
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143 < test 2 >
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144 <param name="adata" value="krumsiek11.h5ad" />
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145 <conditional name="method">
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146 <param name="method" value="pp.mnn_correct"/>
0
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147 <param name="reg_keys" value="cell_type"/>
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148 </conditional>
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149 <assert_stdout>
1
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150 <has_text_matching expression="sc.pp.mnn_correct"/>
0
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151 <has_text_matching expression="keys='cell_type'"/>
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152 </assert_stdout>
1
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153 <output name="anndata_out" file="pp.mnn_correct.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/>
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154 </test>-->
0
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155 <test>
6
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156 <!-- test 1 -->
1
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157 <param name="adata" value="blobs.h5ad" />
0
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158 <conditional name="method">
1
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159 <param name="method" value="pp.combat"/>
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160 <param name="key" value="blobs"/>
0
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161 </conditional>
6
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162 <section name="advanced_common">
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163 <param name="show_log" value="true" />
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164 </section>
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165 <output name="hidden_output">
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166 <assert_contents>
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167 <has_text_matching expression="sc.pp.combat"/>
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168 <has_text_matching expression="key='blobs'"/>
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169 </assert_contents>
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170 </output>
1
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171 <output name="anndata_out" file="pp.combat.blobs.h5ad" ftype="h5ad" compare="sim_size"/>
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172 </test>
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173 </tests>
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174 <help><![CDATA[
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175 Regress out unwanted sources of variation, using `pp.regress_out`
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176 =================================================================
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177
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178 Regress out unwanted sources of variation, using simple linear regression. This is
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179 inspired by Seurat's `regressOut` function in R.
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180
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181 More details on the `scanpy documentation
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182 <https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.pp.regress_out.html>`__
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183
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184 Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct`
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185 ==================================================================================
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186
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187 This uses the implementation of mnnpy. Depending on do_concatenate, it returns AnnData objects in the
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188 original order containing corrected expression values or a concatenated matrix or AnnData object.
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189
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190 Be reminded that it is not advised to use the corrected data matrices for differential expression testing.
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191
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192 More details on the `scanpy documentation
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193 <https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.api.pp.mnn_correct.html>`__
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194
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195
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196 Correct batch effects with ComBat function (`pp.combat`)
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197 ========================================================
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198
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199 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
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200
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201 More details on the `scanpy documentation
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202 <https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.pp.combat.html>`__
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203
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204
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205 ]]></help>
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206 <expand macro="citations"/>
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207 </tool>