Mercurial > repos > galaxyp > pyprophet_score
changeset 1:00816d9855fc draft
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/pyprophet commit ededbd58827da5e8c14d1e2a6b2bab0f293a7482"
| author | galaxyp |
|---|---|
| date | Thu, 02 Apr 2020 01:34:49 -0400 |
| parents | 8b11789d8b95 |
| children | 8b30c8ffa687 |
| files | pyprophet_score.xml test-data/score2.osw test-data/score_report2.pdf |
| diffstat | 3 files changed, 29 insertions(+), 10 deletions(-) [+] |
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--- a/pyprophet_score.xml Wed Feb 26 04:15:49 2020 -0500 +++ b/pyprophet_score.xml Thu Apr 02 01:34:49 2020 -0400 @@ -1,4 +1,4 @@ -<tool id="pyprophet_score" name="PyProphet score" version="@VERSION@.0"> +<tool id="pyprophet_score" name="PyProphet score" version="@VERSION@.1"> <description> Error-rate estimation for MS1, MS2 and transition-level data </description> @@ -10,13 +10,13 @@ <![CDATA[ pyprophet score --in='$input' - --classifier=$conditional_classifier.classifier - #if str($conditional_classifier.classifier)=='XGBoost': + --classifier=$conditional_classifier.classifier $conditional_classifier.xgb_autotune - #end if - #if $apply_weights: - --apply_weights + #elif str($conditional_classifier.classifier)=='LDA': + --classifier=$conditional_classifier.classifier + #elif str($conditional_classifier.classifier)=='prev_weights': + --apply_weights=$conditional_classifier.apply_weights #end if --xeval_fraction=$xeval_fraction --xeval_num_iter=$xeval_num_iter @@ -42,24 +42,27 @@ --ipf_max_peakgroup_pep=$ipf_max_peakgroup_pep $tric_chromprob $test_mode + --threads "\${GALAXY_SLOTS:-4}" --out='./output.osw' && mv *_report.pdf report.pdf - ]]> </command> <inputs> <param name="input" type="data" format="osw" label="Input file" help="This file needs to be in OSW format (--in)" /> <conditional name="conditional_classifier"> - <param argument="--classifier" type="select" label="Either a 'LDA' or 'XGBoost' classifier is used for semi-supervised learning" > + <param name="classifier" type="select" label="Either a 'LDA' or 'XGBoost' classifier is used for semi-supervised learning or previously calculated Pyprophet score weights can be loaded" help="(--classifier)"> <option value="LDA" selected="True" >LDA</option> <option value="XGBoost">XGBoost</option> + <option value="prev_weights">Apply previously calculated weights</option> </param> <when value="LDA"/> <when value="XGBoost"> <param name="xgb_autotune" type="boolean" truevalue="--xgb_autotune" falsevalue="--no-xgb_autotune" label="XGBoost: Autotune hyperparameters" help="(--xgb_autotune / --no-xgb_autotune)"/> </when> + <when value="prev_weights"> + <param name="apply_weights" type="data" format="osw" label="Apply PyProphet score weights file instead of semi-supervised learning." help="(--apply_weights)" /> + </when> </conditional> - <param argument="apply_weights" type="data" format="txt" optional="True" label="Apply PyProphet score weights file instead of semi-supervised learning." /> <param argument="--level" type="select" display="radio" label="The data level selected for scoring. 'ms1ms2' integrates both MS1- and MS2-level scores and can be used instead of 'ms2'-level results" > <option value="ms1" >MS1</option> <option value="ms2" >MS2</option> @@ -85,7 +88,7 @@ <param argument="pi0_smooth_df" type="integer" value="3" label="Number of degrees-of-freedom to use when estimating pi_0 with a smoother" /> <param name="pi0_smooth_log" type="boolean" truevalue="--pi0_smooth_log_pi0" falsevalue="--no-pi0_smooth_log_pi0" label="If True and pi0_method = 'smoother', pi0 will be estimated by applying a smoother to a scatterplot of log(pi0) estimates against the tuning parameter lambda" help="(--pi0_smooth_log_pi0 / --no-pi0_smooth_log_pi0)"/> <param name="lfdr_truncate" type="boolean" checked="True" truevalue="--lfdr_truncate" falsevalue="--no-lfdr_truncate" label="If True, local FDR values >1 are set to 1" help="(--lfdr_truncate / --no-lfdr_truncate)"/> - <param name="lfdr_monotone" type="boolean" checked="True" truevalue="--lfdr_monotone" falsevallUE="--no-lfdr_monotone" label="If True, local FDR values are non-decreasing with increasing p-values" help="(--lfdr_monotone / --no-lfdr_monotone)"/> + <param name="lfdr_monotone" type="boolean" checked="True" truevalue="--lfdr_monotone" falsevalue="--no-lfdr_monotone" label="If True, local FDR values are non-decreasing with increasing p-values" help="(--lfdr_monotone / --no-lfdr_monotone)"/> <param argument="--lfdr_transformation" type="select" display="radio" label="Either a 'probit' or 'logit' transformation is applied to the p-values so that a local FDR estimate can be formed that does not involve edge effects of the [0,1] interval in which the p-values lie" > <option value="probit" selected="True" >probit</option> <option value="logit">logit</option> @@ -116,6 +119,22 @@ <output name="output" file="score.osw" compare="sim_size" /> <output name="score_report" file="score_report.pdf" compare="sim_size" /> </test> + <test> + <param name="input" value="merged.osw" ftype="osw"/> + <conditional name="conditional_classifier"> + <param name="classifier" value="prev_weights"/> + <param name="apply_weights" value="score.osw" ftype="osw"/> + </conditional> + <param name="level" value="ms2"/> + <param name="xeval_num_iter" value="2" /> + <param name="ss_num_iter" value="2" /> + <param name="pi0_lambda_start" value="0.1" /> + <param name="pi0_lambda_end" value="0.3" /> + <param name="pi0_lambda_steps" value="0.01" /> + <param name="test_mode" value="True" /> + <output name="output" file="score2.osw" compare="sim_size" /> + <output name="score_report" file="score_report2.pdf" compare="sim_size" /> + </test> </tests> <help> <![CDATA[