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updated documentation
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GuyTeichman committed Oct 15, 2022
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2 changes: 1 addition & 1 deletion docs/build/.buildinfo
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: bb1eb5e0e5c901ebcfc674ab6a6d6864
config: b970ada4cb3d5597f4d7b000044e4c6b
tags: 645f666f9bcd5a90fca523b33c5a78b7
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rnalysis.filtering.CountFilter.ma\_plot
=======================================

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.ma_plot
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rnalysis.filtering.CountFilter.normalize\_median\_of\_ratios
============================================================

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.normalize_median_of_ratios
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rnalysis.filtering.CountFilter.normalize\_rle
=============================================

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.normalize_rle
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rnalysis.filtering.CountFilter.normalize\_tmm
=============================================

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.normalize_tmm
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rnalysis.filtering.CountFilter.normalize\_to\_quantile
======================================================

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.normalize_to_quantile
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Expand Up @@ -3,4 +3,4 @@ rnalysis.filtering.CountFilter.normalize\_to\_rpm

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.normalize_to_rpm_htseqcount
.. automethod:: CountFilter.normalize_to_rpm
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rnalysis.filtering.CountFilter.normalize\_to\_rpm\_htseqcount
=============================================================

.. currentmodule:: rnalysis.filtering

.. automethod:: CountFilter.normalize_to_rpm_htseqcount
23 changes: 15 additions & 8 deletions docs/build/_sources/rnalysis.filtering.CountFilter.rst.txt
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.. autoclass:: CountFilter




.. automethod:: __init__







.. autosummary::
:toctree: .

CountFilter.biotypes
CountFilter.box_plot
CountFilter.clustergram
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CountFilter.from_folder
CountFilter.head
CountFilter.intersection
CountFilter.ma_plot
CountFilter.majority_vote_intersection
CountFilter.normalize_median_of_ratios
CountFilter.normalize_rle
CountFilter.normalize_tmm
CountFilter.normalize_to_quantile
CountFilter.normalize_to_rpm
CountFilter.normalize_to_rpm_htseqcount
CountFilter.normalize_with_scaling_factors
CountFilter.number_filters
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CountFilter.transform
CountFilter.union
CountFilter.violin_plot



20 changes: 18 additions & 2 deletions docs/build/_sources/user_guide.rst.txt
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Expand Up @@ -505,7 +505,21 @@ There are a few filtering operations unique to CountFilter. Those include 'filte

Normalizing reads with CountFilter
------------------------------------
:term:`CountFilter` offers two methods for normalizing reads: supply user-defined scaling factors, or normalize to reads per million (RPM). Data normalized in other methods (such as RPKM) can be used as input for CountFilter, but it cannot perform such normalization methods on its own.
:term:`CountFilter` offers two methods for normalizing reads: normalize with one of the pre-made normalization methods *RNAlysis* supplies, or using user-defined scaling factors. Data normalized in other methods (such as RPKM) can be used as input for CountFilter as well.

*RNAlysis* supplies the following normalization methods:

* Relative Log Expression (RLE - 'normalize_rle'), used by default by R's DESeq2
* Trimmed Mean of M-values (TMM - 'normalize_tmm'), used by default by R's edgeR
* Quantile normalization, a generalization of Upper Quantile normalization (UQ - 'normalize_quantile'), used by default by R's Limma
* Median of Ratios Normalization (MRN - 'normalize_mrn')
* Reads Per Million (RPM - 'normalize_to_rpm')

To normalize a :term:`CountFilter` with one of these functions, simply call the function with your preferred parameters, if there are any. For example::

>>> counts = filtering.CountFilter('tests/test_files/counted.csv')
>>> counts.normalize_rle()
Normalized the values of 22 features. Normalized inplace.

To normalize a :term:`CountFilter` with user-generated scaling factors, we need a `csv` table with the size factor for each sample:

Expand All @@ -518,7 +532,8 @@ To normalize a :term:`CountFilter` with user-generated scaling factors, we need
We would then supply the function with the path to the scaling factors file::

>>> counts = filtering.CountFilter('tests/test_files/counted.csv')
>>> counts.normalize with_scaling_factors('scaling_factors.csv')
>>> counts.normalize_with_scaling_factors('scaling_factors.csv')
Normalized the values of 22 features. Normalized inplace.

The resulting :term:`CountFilter` object will be normalized with the scaling factors (dividing the value of each column by the value of the corresponding scaling factor).

Expand All @@ -544,6 +559,7 @@ We would then supply the normalization function with the path to the special cou

>>> counts = CountFilter("tests/test_files/counted.csv")
>>> counts.normalize_to_rpm_htseqcount("tests/test_files/uncounted.csv")
Normalized the values of 22 features. Normalized inplace.

The resulting :term:`CountFilter` object will be normalized to RPM with the formula (1,000,000 * reads in cell) / (sum of aligned reads + __no_feature + __ambiguous + __alignment_no_unique)

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2 changes: 1 addition & 1 deletion docs/build/_static/documentation_options.js
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URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
VERSION: '3.0.1',
VERSION: '3.1.0',
LANGUAGE: 'None',
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BUILDER: 'html',
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