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- builtins.object
-
- Papillon_db
- Papillon_list
class Papillon_db(builtins.object) |
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Create a Papillon_db object using read_folder() or read_files() and _papillon_builder
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Methods defined here:
- __init__(self, path, samples, comparisons, genes_detected, isoforms_detected)
- self.path - files path
self.samples - samples found
self.comparisons - comparisons found
self.genes_detected - dataframe of genes detected
self.isoforms_detected - dataframe of isoforms detected
- __str__(self)
- Return str(self).
- change_order(self, new_order)
- Change the samples order
Parameters
----------
new_order: list of samples order
Example
----------
MyProject.change_order(["Sample 4","Sample 3","Sample 2","Sample 1"])
- drop_comparison(self, comparison)
- Drop Comparison (str) or list of comparisons
Parameters
----------
comparison: comparison (str) or list of comparisons
Example
----------
MyProject.drop_comparison(specific_comparison)
- get_gene(self, genelist=None, comparison='all', comparison_sign=None, fold_ind=None, fold_sign='>')
- This method select genes per name or conditions. It return a Papillon_list object
Parameters
----------
genelist - accept string (1 gene name), list of gene names or file with a list of gene names
comparison - To select genes higher/lower in one condition compared to another. Accept either "all" to pass all the comparisons, or accept only 1 comparison as str (already present in the data)
comparison_sign - usable in combination with comparison, accept either ">" or "<"
fold_ind - fold induction (log2) higher/lower then number
fold_sign - usable in combination with fold_ind, accept either ">" or "<"
Example
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Selection=MyProject.get_gene(["CD44","CCL15"], comparison="Sample 1_vs_Sample 2", comparison_sign="<", fold_ind=1, fold_sign="<")
- get_isoform(self, genelist=None, comparison='all', comparison_sign=None, fold_ind=None, fold_sign='>')
- This function select isoforms. It creates a Papillon object
Parameters
----------
genelist - accept string (1 gene name), list of gene names or file with a list of gene names
comparison - To select genes higher/lower in one condition compared to another. Accept either "all" to pass all the comparisons, or accept only 1 comparison as str (already present in the data)
comparison_sign - usable in combination with comparison, accept either ">" or "<"
fold_ind - fold induction (log2) higher/lower then number
fold_sign - usable in combination with fold_ind, accept either ">" or "<"
Example
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Selection=MyProject.get_isoform(["IL6","CCL15","IL17RC"], comparison="Sample 2_vs_Sample 4", comparison_sign="<")
- search(self, word, where, how='table', export=False)
- search among genes/isoforms names in detected and significant
Parameters
----------
word - accept a str to search among the gene names
where - accept:
"genes_detected"
"genes_significant"
"isoforms_detected"
"isoforms_significant"
how - accept:
"table" return the dataframe with the genes found
"list" return a list of names, no duplicates
export - True/False
Example
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search_result=MyProject.search(word="IL6",where="genes_significant", how="table")
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
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class Papillon_list(builtins.object) |
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Class containing a selected list of genes, with data associated, generated by Papillon_db.get_gene() or Papillon_db.get_isoform() methods
Is possible add the content of two or more Papillon_list objects.
Example:
P_list3 = P_list1+P_list2
P_list4=sum([PL1, PL2, PL3])
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Methods defined here:
- __add__(self, other)
- __init__(self, df, what, comparisons, path, samples, comparison='all', comparison_sign=None, fold_ind=None, fold_sign='>', p=0.05)
- Initialize self. See help(type(self)) for accurate signature.
- __radd__(self, other)
- __str__(self)
- Return str(self).
- compare(self, other)
- Compare two Papillon_list objects
Parameters
----------
other: another Papillon_list object
Example
----------
Selection1.compare(Selection2)
- export(self)
- Export the selected genes/isoforms as excel file.
Example
----------
Selection.export()
- heatmap(self, z_score=False, col_cluster=False, method='complete', cmap='seismic', export=False, **options)
- Generate heatmap using selected genes/isoforms
Parameters
----------
z_score - True/False whether want or not apply z-score normalization
col_cluster - True/False whether want or not cluster the samples
method - clustering algorithm - default is complete-linkage
cmap - map color
export - True/False whether want or not export the dataframe of
selected genes
**options - all the options accepted by seaborn.clustermap
default metric is euclidean.
Example
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Selection.heatmap(z_score=True,export=True)
- lineplot(self, title='', legend=True, z_score=False, export=False, size=10, ci=None, **option)
- LinePlot selected genes expression levels. Max number of genes 200
Parameters
----------
title - accept a str as title of the plot
legend - True/False show the legend
z_score - True/False calculate the z-score normalization
export - True/False whether or not export the image
size - change the size of the plot
**options - all the options accepted by seaborn.factorplot
Example
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Selection.lineplot(export=True,z_score=True)
- onlyFPKM(self, return_as, remove_FPKM_name=False)
- Take a Papillon_list object and return only FPKM columns.
Parameters
----------
return as:
"df" - pandas DataFrame
"array" - numpy array
"gene name" - pandas DataFrame containing gene names
remove_FPKM_name: True/False
Example
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df=Selection.onlyFPKM("df")
- plot(**parameter)
- Deprecated. Use self.lineplot() instead
- search(self, string)
- Search a string in the Papillon_list
Parameters
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string - accept a str to search among the gene names
Example
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search_results=Selection.search("IL")
- select(self, genelist)
- Create another Papillon_list object
Parameters
----------
genelist: accept string (1 gene name), list of gene names or file
with a list of gene names
Example
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Selection2=Selection1.select(["IL6","CCL15"])
- show(self)
- Show genes/isoforms as Dataframe
Example
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Selection.show()
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
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