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comparison dada2_learnErrors.xml @ 9:ef3ebaa70032 draft

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planemo upload for repository https://github.com/bernt-matthias/mb-galaxy-tools/tree/topic/dada2/tools/dada2 commit a54770771e567c7ad8a9dd75cc4689c3935ef11c
author matthias
date Tue, 28 May 2019 12:13:45 -0400
parents 382900945187
children 69d1d5dd7b21
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8:af8d1ccbd153 9:ef3ebaa70032
88 Description 88 Description
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91 Error rates are learned by alternating between sample inference and error rate estimation until convergence. Additionally a plot is generated that shows the observed frequency of each transition (eg. A->C) as a function of the associated quality score, the final estimated error rates (if they exist), the initial input rates, and the expected error rates under the nominal definition of quality scores. 91 Error rates are learned by alternating between sample inference and error rate estimation until convergence. Additionally a plot is generated that shows the observed frequency of each transition (eg. A->C) as a function of the associated quality score, the final estimated error rates (if they exist), the initial input rates, and the expected error rates under the nominal definition of quality scores.
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93 In addition a plot is generated (with plotErrors) that shows the observed frequency of each transition (eg. A->C) as a function of the associated quality score. Also the final estimated error rates (if they exist) are shown. Optionally also the initial input rates and the expected error rates under the nominal definition of quality scores can be added to the plot. 93 In addition a plot is generated (with plotErrors) that shows the observed frequency of each transition (eg. A->C) as a function of the associated quality score. Also the final estimated error rates (if they exist) are shown. Optionally also the initial input rates and the expected error rates under the nominal definition of quality scores can be added to the plot.
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95 Usage 95 Usage
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98 **Input** are the FASTQ dataset containing the filtered and trimmed reads of the samples. 98 **Input** are the FASTQ dataset containing the filtered and trimmed reads of the samples.
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111 The learnErrors method learns a parametric error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors). 111 The learnErrors method learns a parametric error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors).
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113 It is expected that the estimated error rates (black lines in the plot) are in a good fit to the observed rates (points in the plot), and that the error rates drop with increased quality. Try to increase the **number of bases to use for learning** if this is not the case. 113 It is expected that the estimated error rates (black lines in the plot) are in a good fit to the observed rates (points in the plot), and that the error rates drop with increased quality. Try to increase the **number of bases to use for learning** if this is not the case.
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115 Error functions: 115 Error functions:
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117 - loessErrfun: accepts a matrix of observed transitions, with each transition corresponding to a row (eg. row 2 = A->C) and each column to a quality score (eg. col 31 = Q30). It returns a matrix of estimated error rates of the same shape. Error rates are estimates by a loess fit of the observed rates of each transition as a function of the quality score. Self-transitions (i.e. A->A) are taken to be the left-over probability. 117 - loessErrfun: accepts a matrix of observed transitions, with each transition corresponding to a row (eg. row 2 = A->C) and each column to a quality score (eg. col 31 = Q30). It returns a matrix of estimated error rates of the same shape. Error rates are estimates by a loess fit of the observed rates of each transition as a function of the quality score. Self-transitions (i.e. A->A) are taken to be the left-over probability.
118 - noqualErrfun: accepts a matrix of observed transitions, groups together all observed transitions regardless of quality scores, and estimates the error rate for that transition as the observed fraction of those transitions. The effect is that quality scores will be effectively ignored. 118 - noqualErrfun: accepts a matrix of observed transitions, groups together all observed transitions regardless of quality scores, and estimates the error rate for that transition as the observed fraction of those transitions. The effect is that quality scores will be effectively ignored.
119 - PacBioErrfun: This function accepts a matrix of observed transitions from PacBio CCS amplicon sequencing data, with each transition corresponding to a row (eg. row 2 = A->C) and each column to a quality score (eg. col 31 = Q30). It returns a matrix of estimated error rates of the same shape. Error rates are estimates by loessErrfun for quality scores 0-92, and individually by the maximum likelihood estimate for the maximum quality score of 93. 119 - PacBioErrfun: This function accepts a matrix of observed transitions from PacBio CCS amplicon sequencing data, with each transition corresponding to a row (eg. row 2 = A->C) and each column to a quality score (eg. col 31 = Q30). It returns a matrix of estimated error rates of the same shape. Error rates are estimates by loessErrfun for quality scores 0-92, and individually by the maximum likelihood estimate for the maximum quality score of 93.
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