Docstring:
Usage: qiime pepsirf norm [OPTIONS]
Normalize raw count data with pepsirf's norm module
Inputs:
--i-peptide-scores ARTIFACT FeatureTable[RawCounts | Normed]
Name of FeatureTable matrix file containing peptide
scores. This file should be in the same format as the
output from the demux module. [required]
--i-negative-control ARTIFACT FeatureTable[RawCounts | Normed]
Name of FeatureTable matrix file containing data for
sb samples. [optional]
Parameters:
--p-normalize-approach VALUE Str % Choices('col_sum')¹ | Str %
Choices('diff')² | Str % Choices('diff_ratio')³ | Str % Choices('ratio')⁴
| Str % Choices('size_factors')⁵
'col_sum': Normalize the scores using a column-sum
method. Output per peptide is the score per million
for the sample (i.e., summed across all peptides).
'size_factors': Normalize the scores using the size
factors method (Anders and Huber 2010). 'diff':
Normalize the scores using the difference method. For
each peptide and sample, the difference between the
score and the respective peptide's mean score in the
negative controls is determined. 'ratio': Normalize
the scores using the ratio method. For each peptide
and sample, the ratio of score to the respective
peptide's mean score in the negative controls is
determined. 'diff_ratio': Normalize the scores using
the difference-ratio method. For each peptide and
sample, the difference between the score and the
respective peptide's mean score in the negative
controls is first determined. This difference is then
divided by the respective peptide's mean score in the
negative controls. [default: 'col_sum']
--p-negative-id TEXT Optional approach for identifying negative controls.
Provide a unique string at the start of all negative
control samples. [optional]
--p-negative-names TEXT...
List[Str] Optional approach for identifying negative controls.
Space-separated list of negative control sample
names. [optional]
--p-precision INTEGER Output score precision. The scores written to the
Range(0, None) output will be output to this many decimal places.
[default: 2]
--p-outfile TEXT The outfile that will produce a list of inputs to
PepSIRF. [default: './norm.out']
--p-pepsirf-binary TEXT
The binary to call pepsirf on your system.
[default: 'pepsirf']
Outputs:
--o-qza-output ARTIFACT FeatureTable[Normed¹ | NormedDifference² |
NormedDiffRatio³ | NormedRatio⁴ | NormedSized⁵]
the FeatureTable (.qza) output based on the
normalized approach given by user [required]
Miscellaneous:
--output-dir PATH Output unspecified results to a directory
--verbose / --quiet Display verbose output to stdout and/or stderr
during execution of this action. Or silence output if
execution is successful (silence is golden).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--help Show this message and exit.
Import:
from qiime2.plugins.pepsirf.methods import norm
Docstring:
pepsirf norm module
Normalize raw count data with pepsirf's norm module
Parameters
----------
peptide_scores : FeatureTable[RawCounts | Normed]
Name of FeatureTable matrix file containing peptide scores. This file
should be in the same format as the output from the demux module.
normalize_approach : Str % Choices('col_sum')¹ | Str % Choices('diff')² | Str % Choices('diff_ratio')³ | Str % Choices('ratio')⁴ | Str % Choices('size_factors')⁵, optional
'col_sum': Normalize the scores using a column-sum method. Output per
peptide is the score per million for the sample (i.e., summed across
all peptides). 'size_factors': Normalize the scores using the size
factors method (Anders and Huber 2010). 'diff': Normalize the scores
using the difference method. For each peptide and sample, the
difference between the score and the respective peptide's mean score in
the negative controls is determined. 'ratio': Normalize the scores
using the ratio method. For each peptide and sample, the ratio of score
to the respective peptide's mean score in the negative controls is
determined. 'diff_ratio': Normalize the scores using the difference-
ratio method. For each peptide and sample, the difference between the
score and the respective peptide's mean score in the negative controls
is first determined. This difference is then divided by the respective
peptide's mean score in the negative controls.
negative_control : FeatureTable[RawCounts | Normed], optional
Name of FeatureTable matrix file containing data for sb samples.
negative_id : Str, optional
Optional approach for identifying negative controls. Provide a unique
string at the start of all negative control samples.
negative_names : List[Str], optional
Optional approach for identifying negative controls. Space-separated
list of negative control sample names.
precision : Int % Range(0, None), optional
Output score precision. The scores written to the output will be output
to this many decimal places.
outfile : Str, optional
The outfile that will produce a list of inputs to PepSIRF.
pepsirf_binary : Str, optional
The binary to call pepsirf on your system.
Returns
-------
qza_output : FeatureTable[Normed¹ | NormedDifference² | NormedDiffRatio³ | NormedRatio⁴ | NormedSized⁵]
the FeatureTable (.qza) output based on the normalized approach given
by user
