Mercurial > repos > ebi-gxa > celltypist_predict

annotate celltypist.xml @ 1:df005630100e draft default tip

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planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 4dd95e3e6c059cc9df47f5d08e4f3f8b618830f1-dirty
author ebi-gxa
date Wed, 19 Feb 2025 12:10:17 +0000
parents a7d6985ba791
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df005630100e planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 4dd95e3e6c059cc9df47f5d08e4f3f8b618830f1-dirty
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1 <tool id="celltypist_predict" name="CellTypist: Predict Cell Types" version="1.6.3+galaxy1" profile="20.05" license="MIT">
0
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2 <description>Predict cell types using an existing CellTypist model.</description>
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3 <requirements>
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4 <requirement type="package" version="1.6.3">celltypist</requirement>
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5 </requirements>
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6 <command detect_errors="exit_code">
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7 <![CDATA[
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8 #if $inp.format == 'h5ad':
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9 ln -s "$inp.data" input.h5ad &&
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10 #else:
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11 ln -s "$inp.data" input.mtx &&
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12 #end if
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13 ln -s "$model_file" model.pkl &&
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14 python $__tool_directory__/celltypist_CLI.py --action predict
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15 --output_path "$output_predictions"
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16 --model model.pkl
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17 #if $inp.format == 'h5ad':
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18 --input_path input.h5ad
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19 #if $inp.raw_counts_layer:
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20 --raw_counts_layer "$inp.raw_counts_layer"
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21 #end if
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22 #if $inp.gene_symbols_field:
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23 --gene_symbols_field "$inp.gene_symbols_field"
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24 #end if
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25 #else:
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26 --input_path input.mtx
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27 --gene_file "$inp.gene_file"
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28 --cell_file "$inp.cell_file"
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29 #end if
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30
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31 ${normalize}
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32 ${transpose_input}
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33 --mode "$mode"
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34 --p_thres "$p_thres"
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35 ${majority_voting}
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36 #if $over_clustering:
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37 --over_clustering "$over_clustering"
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38 #end if
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39 ]]>
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40 </command>
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41 <inputs>
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42 <conditional name="inp" label="Input format">
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43 <param name="format" type="select" label="Input format" help="Format for the input files, either AnnData or Matrix market format (10x)">
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44 <option value="h5ad">AnnData</option>
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45 <option value="mtx">Matrix market</option>
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46 </param>
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47 <when value="h5ad">
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48 <param format="h5ad" type="data" name="data" label="Input data (AnnData)" help="The input data for prediction."/>
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49 <param type="text" name="raw_counts_layer" label="Raw counts layer" help="The name of the layer that stores the raw counts. Uses default matrix if not present."/>
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50 <param type="text" name="gene_symbols_field" label="Gene symbols field" help="The field in AnnData where the gene symbols are stored, if not in index."/>
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51 </when>
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52 <when value="mtx">
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53 <param format="mtx,txt" type="data" name="data" label="Input matrix (mtx file)" help="The input data for prediction."/>
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54 <param format="txt,tabular" type="data" name="gene_file" label="Gene file" help="The file containing one gene per line corresponding to the genes in the input data (required for .mtx data)."/>
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55 <param format="txt,tabular" type="data" name="cell_file" label="Cell file" help="The file containing one cell per line corresponding to the cells in the input data (required for .mtx data)."/>
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56 </when>
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57 </conditional>
1
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58 <param format="data" type="data" name="model_file" optional="false" label="Model file" help="A CellTypist model file in .pkl format."/>
0
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59 <param type="boolean" argument="--normalize" label="Normalize" truevalue="--normalize" falsevalue="" help="If raw counts are provided in the AnnData object, they need to be normalized."/>
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60 <param type="boolean" argument="--transpose_input" label="Transpose input" truevalue="--transpose_input" falsevalue="" help="If the provided matrix is in the gene-by-cell format, please transpose the input to cell-by-gene format."/>
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61 <param type="select" name="mode" label="Mode" help="Mode for the prediction (e.g., best match, majority vote).">
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62 <option value="best_match" selected="true">Best match</option>
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63 <option value="prob_match">Probability match</option>
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64 </param>
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65 <param type="float" name="p_thres" value="0.5" label="Probability threshold" help="Probability threshold for assigning a cell type in a multiclass problem. Ignored if Mode set to Best match."/>
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66 <param type="boolean" argument="--majority_voting" label="Majority voting" truevalue="--majority_voting" falsevalue="" help="Refine the predicted labels by running the majority voting classifier after over-clustering."/>
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67 <param type="text" name="over_clustering" label="Over-clustering" help="If majority voting is set to True, specify the type of over-clustering that is to be performed. This can be specified in the AnnData or an input file specifying the over-clustering per cell. If not present, then the default heuristic over-clustering based on input data will be used."/>
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68 </inputs>
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69 <outputs>
1
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70 <data format="tabular" name="output_predictions" label="${tool.name} on ${on_string}: cell typing table"/>
0
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71 </outputs>
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72 <tests>
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73 <test expect_num_outputs="1">
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74 <param name="model_file" value="Healthy_COVID19_PBMC.pkl"/>
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75 <param name="normalize" value="true"/>
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76 <param name="mode" value="best_match"/>
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77 <param name="p_thres" value="0.5"/>
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78 <conditional name="inp">
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79 <param name="format" value="h5ad"/>
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80 <param name="data" value="raw_counts_pbmc3k.h5ad"/>
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81 <param name="gene_symbols_field" value="SYMBOL"/>
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82 </conditional>
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83 <output name="output_predictions">
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84 <assert_contents>
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85 <has_text text="MAIT"/>
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86 <has_n_lines n="2701"/>
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87 <has_n_columns n="2"/>
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88 </assert_contents>
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89 </output>
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90 </test>
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91 </tests>
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92 <help><![CDATA[
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93 .. _celltypist_predict_tool:
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94
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95 CellTypist: Predict Cell Types
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96 ------------------------------
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97
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98 CellTypist is a tool for automatic cell type annotation using single-cell RNA sequencing data. This Galaxy tool wrapper executes the CellTypist script in "predict" mode, allowing you to predict cell types using an existing CellTypist model.
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99
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100 Input
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101 ^^^^^
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102
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103 - **Input format**: Choose the format for the input files, either AnnData or Matrix market format (10x).
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104
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105 - If AnnData is selected, provide the input data (AnnData file).
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106 - If Matrix market is selected, provide the input matrix (mtx file), gene file, and cell file.
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107
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108 - **Input data**: The input data for prediction.
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109
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110 - **Gene file**: The file containing one gene per line corresponding to the genes in the input data (required for .mtx data).
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111
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112 - **Cell file**: The file containing one cell per line corresponding to the cells in the input data (required for .mtx data).
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113
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114 - **Model file**: The existing CellTypist model file in .pkl format.
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115
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116 - **Raw counts layer**: The name of the layer that stores the raw counts. Uses the default matrix if not provided.
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117
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118 - **Gene symbols field**: The field in AnnData where the gene symbols are stored, if not in index.
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119
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120 - **Normalize**: If raw counts are provided in the AnnData object, they need to be normalized.
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121
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122 - **Transpose input**: If the provided matrix is in the gene-by-cell format, transpose the input to cell-by-gene format.
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123
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124 - **Mode**: Mode for the prediction (e.g., best match, majority vote).
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125
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126 - **Probability threshold**: Probability threshold for assigning a cell type in a multiclass problem. Ignored if Mode set to Best match.
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127
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128 - **Majority voting**: Refine the predicted labels by running the majority voting classifier after over-clustering.
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129
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130 - **Over-clustering**: If majority voting is set to True, specify the type of over-clustering that is to be performed. This can be specified in the AnnData or an input file specifying the over-clustering per cell. If not provided, the default heuristic over-clustering based on input data will be used.
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131
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132 Output
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133 ^^^^^^
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134
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135 - **Output predictions**: The predicted cell types for the input data.
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136
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137 Citation
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138 ^^^^^^^^
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139
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140 - CellTypist: a tool for automatic cell type annotation using single-cell RNA sequencing data doi/10.1126/science.abl5197
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141
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142 History
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143 ^^^^^^^
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144
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145 Version 1.6.3+galaxy0: Initial wrapper, Pablo Moreno. CLI written by Alex Proutski.
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146 ]]>
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147 </help>
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148 <citations>
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149 <citation type="bibtex">@article{dominguez_conde2022,
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150 title={Cross-tissue immune cell analysis reveals tissue-specific features in humans},
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151 author={Domínguez Conde, C and Xu, C and Jarvis, LB and Rainbow, DB and Wells, SB and Gomes, T and Howlett, SK and Suchanek, O and Polanski, K and King, HW and Mamanova, L and Huang, N and Szabo, PA and Richardson, L and Bolt, L and Fasouli, ES and Mahbubani, KT and Prete, M and Tuck, L and Richoz, N and Tuong, ZK and Campos, L and Mousa, HS and Needham, EJ and Pritchard, S and Li, T and Elmentaite, R and Park, J and Rahmani, E and Chen, D and Menon, DK and Bayraktar, OA and James, LK and Meyer, KB and Yosef, N and Clatworthy, MR and Sims, PA and Farber, DL and Saeb-Parsy, K and Jones, JL and Teichmann, SA},
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152 journal={Science},
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153 volume={376},
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154 number={6594},
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155 pages={eabl5197},
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156 year={2022},
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157 publisher={American Association for the Advancement of Science},
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158 pmid={35549406},
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159 pmcid={PMC7612735}
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160 }</citation>
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161 </citations>
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162 </tool>