comparison deseq2.xml @ 25:de44f8eff84a draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/deseq2 commit 9eb6d07510ccf27d6499172d62c81661078ec57b"

24:71bacea10eee

            label="Output rLog normalized table" />
        <param name="normVST" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Output VST normalized table" />
        <param name="many_contrasts" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Output all levels vs all levels of primary factor (use when you have >2 levels for primary factor)"
            help=" DESeq2 performs independent filtering by default using the mean of normalized counts as a filter statistic" />
        <param name="esf" type="select" label="(Optional) Method for estimateSizeFactors" 
            help="Method for estimation: either 'ratio', 'poscounts', or 'iterate'. 'ratio' uses the standard median ratio method introduced in DESeq. 
                The size factor is the median ratio of the sample over a 'pseudosample': for each gene, the geometric mean of all samples. 
                'poscounts' and 'iterate' offer alternative estimators, which can be used even when all genes contain a sample with a zero (a problem 
                for the default method, as the geometric mean becomes zero, and the ratio undefined). The 'poscounts' estimator deals with a gene with 

                counts with large Cook’s distance with the trimmed mean over all samples, scaled up by the size factor
                or normalization factor for that sample" />
        <param name="outlier_filter_off" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Turn off outliers filtering (only affects with >2 replicates)"
            help="When there are more than 2 replicates for a given sample, the DESeq2 will automatically
                filter genes which contain a Cook’s distance above a cutoff" />
        <param name="auto_mean_filter_off" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Turn off independent filtering"
            help=" DESeq2 performs independent filtering by default using the mean of normalized counts as a filter statistic" />
    </inputs>
    <outputs>
        <data name="deseq_out" format="tabular" label="DESeq2 result file on ${on_string}">
            <filter>many_contrasts is False</filter>
            <actions>

25:de44f8eff84a

            label="Output rLog normalized table" />
        <param name="normVST" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Output VST normalized table" />
        <param name="many_contrasts" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Output all levels vs all levels of primary factor (use when you have >2 levels for primary factor)"
            help=" DESeq2 performs independent filtering by default using the mean of normalized counts as a filter statistic" />
        <param name="esf" type="select" label="(Optional) Method for estimateSizeFactors" 
            help="Method for estimation: either 'ratio', 'poscounts', or 'iterate'. 'ratio' uses the standard median ratio method introduced in DESeq. 
                The size factor is the median ratio of the sample over a 'pseudosample': for each gene, the geometric mean of all samples. 
                'poscounts' and 'iterate' offer alternative estimators, which can be used even when all genes contain a sample with a zero (a problem 
                for the default method, as the geometric mean becomes zero, and the ratio undefined). The 'poscounts' estimator deals with a gene with 

                counts with large Cook’s distance with the trimmed mean over all samples, scaled up by the size factor
                or normalization factor for that sample" />
        <param name="outlier_filter_off" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Turn off outliers filtering (only affects with >2 replicates)"
            help="When there are more than 2 replicates for a given sample, the DESeq2 will automatically
                filter genes which contain a Cook’s distance above a cutoff" />
        <param name="auto_mean_filter_off" type="boolean" truevalue="1" falsevalue="0" checked="false"
            label="Turn off independent filtering"
            help=" DESeq2 performs independent filtering by default using the mean of normalized counts as a filter statistic" />
    </inputs>
    <outputs>
        <data name="deseq_out" format="tabular" label="DESeq2 result file on ${on_string}">
            <filter>many_contrasts is False</filter>
            <actions>