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Methods defined here:
- __init__(self, path, drop_comparison=[])
- read cummeRbund files and return:
self.path - files path
self.samples - samples found
self.comparison - comparisons found
self.genes_detect - dataframe of genes detected
self.genes_significant - dataframe of genes significant
self.isoforms_detect - dataframe of isoforms detected
self.isoforms_significant - dataframe of isoforms significant
expressed
- __str__(self)
- Return str(self).
- change_order(self, new_order)
- Change the samples order
- dropComparison(self, comparison)
- Drop Comparison (str) or list of comparisons and re-calculate
df_significant
- get_gene(self, genelist=None, comparison=None, sign=None, export=False)
- This function select genes. Create self.selected and
self.type_selected="gene".
genelist - accept string (gene name), list of gene names or file
with a list of gene name
comparison - accept only 1 comparison as str (already present in
the data)
sign - usable in combination with comparison, accept either ">" or
"<"
export - True/False whether want or not export the dataframe of
selected genes
- get_isoform(self, genelist=None, comparison=None, sign=None, export=False, show_dup=False)
- This function select isoforms. Create self.selected and
self.type_selected="isoform"
genelist - accept string (gene name), list of gene names or file
with a list of gene name
comparison - accept only 1 comparison as str (already present in
the data)
sign - usable in combination with comparison, accept either ">" or
"<"
export - True/False whether want or not export the dataframe of
selected genes
show_dup - True/False whether want or not highlight duplicated
isoforms for the same gene
- heatmap(self, z_score=True, col_cluster=False, method='complete', cmap='seismic', export=False, **options)
- Generate heatmap using selected genes/isoforms
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.
- onlyFPKM(self, return_as, **option)
- Return a DataFrame with only FPKM columns,
return as:
"df" - pandas DataFrame
"array" - numpy array
"gene name" - pandas DataFrame containing gene names
It uses self.selected, or an extra_df.
- plot(self, title='', legend=True, z_score=False, export=False, df=None, size=10, ci=None, **option)
- LinePlot a selected dataframe of genes. Max number of genes 200
title - accept a string as title of the plot
legend - True/False show the legend
z_score - True/False calculate the z-score normalization
export - True/False whether want or not export image
df - accept a dataframe different from self.selected
**options - all the options accepted by seaborn.factorplot
- search(self, word, where, how='table', export=False)
- search among genes/isoforms names in detected and significant
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
"selected" put the genes found among the differential expressed
genes in self.selected (to plot),
working only with where="significant"
- selected_exist(self, remove=False)
- Check if self.selected exists
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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