Mercurial > repos > ebi-gxa > scanpy_normalise_data

comparison scanpy-normalise-data.xml @ 1:e541f264fad2 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 4846776f55931e176f7e77af7c185ec6fec7d142"
author ebi-gxa
date Mon, 16 Sep 2019 08:11:56 -0400
parents 1dda36e73482
children 059f8d2e8be1
comparison
equal deleted inserted replaced
0:1dda36e73482 1:e541f264fad2
1 <?xml version="1.0" encoding="utf-8"?> 1 <?xml version="1.0" encoding="utf-8"?>
2 <tool id="scanpy_normalise_data" name="Scanpy NormaliseData" version="@TOOL_VERSION@+galaxy1"> 2 <tool id="scanpy_normalise_data" name="Scanpy NormaliseData" version="@TOOL_VERSION@+galaxy1">
3 <description>to make all cells having the same total expression</description> 3 <description>to make all cells having the same total expression</description>
4 <macros> 4 <macros>
5 <import>scanpy_macros.xml</import> 5 <import>scanpy_macros2.xml</import>
6 </macros> 6 </macros>
7 <expand macro="requirements"/> 7 <expand macro="requirements"/>
8 <command detect_errors="exit_code"><![CDATA[ 8 <command detect_errors="exit_code"><![CDATA[
9 ln -s '${input_obj_file}' input.h5 && 9 ln -s '${input_obj_file}' input.h5 &&
10 PYTHONIOENCODING=utf-8 scanpy-normalise-data.py 10 PYTHONIOENCODING=utf-8 scanpy-normalise-data
11 -i input.h5 11 --normalize-to ${scale_factor}
12 -f '${input_format}' 12 --fraction ${fraction}
13 -o output.h5 13 --save-raw ${save_raw}
14 -F '${output_format}' 14 @INPUT_OPTS@
15 -s '${scale_factor}' 15 @OUTPUT_OPTS@
16 #if $save_raw
17 '${save_raw}'
18 #end if
19 @EXPORT_MTX_OPTS@
20 ]]></command> 16 ]]></command>
21 17
22 <inputs> 18 <inputs>
23 <expand macro="input_object_params"/> 19 <expand macro="input_object_params"/>
24 <expand macro="output_object_params"/> 20 <expand macro="output_object_params"/>
25 <param name="scale_factor" argument="--scale-factor" type="float" value="1e4" label="Target number to normalise to" help="Aimed counts per cell after normalisation, default: 1e4"/> 21 <param name="scale_factor" argument="--normalize-to" type="float" value="1e4" min="0"
26 <param name="save_raw" argument="--save-raw" type="boolean" truevalue="--save-raw" falsevalue="" checked="true" label="Save pre-normalised data" help="Save raw quantification in log scale before normalisation."/> 22 label="Target number to normalise to" help="Aimed counts per cell after normalisation."/>
23 <param name="fraction" argument="--fraction" type="float" value="1" min="0" max="1"
24 label="Exclude top expressed genes until the remaining account for no greater than specified fraction of total counts"
25 help="Only non-excluded genes will sum up the target number."/>
26 <param name="save_raw" argument="--save-raw" type="boolean" truevalue="yes" falsevalue="no" checked="true"
27 label="Save normalised data in `.raw`" help="The saved normalised data are log1p transformed."/>
27 <expand macro="export_mtx_params"/> 28 <expand macro="export_mtx_params"/>
28 </inputs> 29 </inputs>
29 30
30 <outputs> 31 <outputs>
31 <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: Normalized data" /> 32 <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: Normalised data"/>
32 <expand macro="export_mtx_outputs"/> 33 <expand macro="export_mtx_outputs"/>
33 </outputs> 34 </outputs>
34 35
35 <tests> 36 <tests>
36 <test> 37 <test>
42 <output name="output_h5" file="normalise_data.h5" ftype="h5" compare="sim_size"/> 43 <output name="output_h5" file="normalise_data.h5" ftype="h5" compare="sim_size"/>
43 </test> 44 </test>
44 </tests> 45 </tests>
45 46
46 <help><![CDATA[ 47 <help><![CDATA[
47 ========================================================= 48 =============================================================
48 Normalize total counts per cell (`pp.normalize_per_cell`) 49 Normalise total counts per cell (`scanpy.pp.normalize_total`)
49 ========================================================= 50 =============================================================
50 51
51 Normalize each cell by total counts over all genes, so that every cell has 52 Normalise each cell by total counts over all genes (excluding top expressed
52 the same total count after normalization. 53 genes if so required), so that every cell has the same total count after
54 normalisation.
53 55
54 Similar functions are used, for example, by Seurat, Cell Ranger or SPRING. 56 Similar functions are used, for example, by Seurat, Cell Ranger or SPRING.
55 57
56 @HELP@ 58 @HELP@
57 59