Functions

netZooPy.panda

analyze_panda

class AnalyzePanda

 AnalyzePanda(Panda)
 Network plot.

top_network_plot

 top_network_plot(self, top = 100, file = 'panda_top_100.png')
 Select top genes

shape_plot_network

__shape_plot_network(self, subset_panda_results, file = 'panda.png')
Create plot

create_plot

__create_plot(self, unique_genes, links, file = 'panda.png')
Run plot

run_panda

Description:

    Run PANDA algorithm from the command line.

Usage:

    -h, --help: help
    -e, --expression: expression values
    -m, --motif: pair file of motif edges, or Pearson correlation matrix when not provided
    -p, --ppi: pair file of PPI edges
    -o, --out: output file
    -r, --rm_missing
    -q, --lioness: output for Lioness single sample networks

Example:

    python run_panda.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData.txt -p ./ToyData/ToyPPIData.txt -o test_panda.txt -q output_panda.txt

panda

class Panda

Panda(object)

Description:

	Using PANDA to infer gene regulatory network.

Usage:

	1. Reading in input data (expression data, motif prior, TF PPI data)
	2. Computing coexpression network
	3. Normalizing networks
	4. Running PANDA algorithm
	5. Writing out PANDA network

Authors: 

cychen, davidvi, alessandromarin

init

init__(self, expression_file, motif_file, ppi_file, save_memory = False, save_tmp=True, remove_missing=False, keep_expression_matrix = False):
    # =====================================================================
    # Data loading
    # =====================================================================

remove_missing

remove_missing(self)
    Remove genes and tfs not present in all files.

normalize_network

normalize_network(self, x)

panda_loop

panda_loop(self, correlation_matrix, motif_matrix, ppi_matrix)
    Panda algorithm.

t_function

    t_function(x, y=None)
T function.

update_diagonal

update_diagonal(diagonal_matrix, num, alpha, step)
    Update diagonal.

pearson_results_data_frame

pearson_results_data_frame(self)
    Results to data frame.

save_panda_results

save_panda_results(self, path='panda.npy')

top_network_plot

top_network_plot(self, top = 100, file = 'panda_top_100.png')
    Select top genes.

shape_plot_network

shape_plot_network(self, subset_panda_results, file = 'panda.png')
    Create plot.

create_plot

create_plot(self, unique_genes, links, file = 'panda.png')
    Run plot.

split_label

split_label(label)

return_panda_indegree

return_panda_indegree(self)
    Return Panda indegree.

return_panda_outdegree

return_panda_outdegree(self)
    Return Panda outdegree.

netZooPy.lioness

run_lioness

Description:

    Run LIONESS algorithm from the command line.

Usage:

    -h, --help: help
    -e, --expression: expression matrix (.npy)
    -m, --motif: motif matrix, normalized (.npy)
    -p, --ppi: ppi matrix, normalized (.npy)
    -n, --npy: PANDA network (.npy)
    -o, --out: output folder
    -f, --format: output format (txt, npy, or mat)
    start: to start from nth sample (optional)
    end: to end at nth sample (optional, must with start)

Example:

    python run_lioness.py -e expression.npy -m motif.npy -p ppi.npy -n panda.npy -o /tmp -f npy 1 100

lioness

class Lioness

Lioness(Panda)

Description:

   Using LIONESS to infer single-sample gene regulatory networks.

Usage:

   1. Reading in PANDA network and preprocessed middle data
   2. Computing coexpression network
   3. Normalizing coexpression network
   4. Running PANDA algorithm
   5. Writing out LIONESS networks

Authors:

   cychen, davidvi

init

init__(self, obj, start=1, end=None, save_dir='lioness_output', save_fmt='npy')

lioness_loop

lioness_loop(self)

save_lioness_results

save_lioness_results(self, file='lioness.txt')

analyze_lioness

class AnalyzeLioness

AnalyzeLioness(Lioness)

init

init__(self, lioness_data)
Load variables from lioness.

top_network_plot

top_network_plot(self, column = 0, top = 100, file = 'lioness_top_100.png')
Select top genes.

lioness_for_puma

class LionessPuma

LionessPuma(Puma)

Description:
     Using LIONESS to infer single-sample gene regulatory networks.

Usage:
    1. Reading in PUMA network and preprocessed middle data
    2. Computing coexpression network
    3. Normalizing coexpression network
    4. Running PUMA algorithm
    5. Writing out LIONESS networks

Authors:
    cychen, davidvi

init

init__(self, obj, start=1, end=None, save_dir='lioness_output', save_fmt='npy')

lioness_loop

 lioness_loop(self)

save_lioness_results

 save_lioness_results(self, file='lioness.txt')

netZooPy.puma

run_puma

Description:
    Run PUMA algorithm from the command line.

Usage:
    run_puma
    -h, --help: help
    -e, --expression: expression values
    -m, --motif: pair file of motif edges, or Pearson correlation matrix when not provided
    -p, --ppi: pair file of PPI edges
    -i, --mir (required): miR file
    -o, --out: output file
    -r, --rm_missing
    -q, --lioness: output for Lioness single sample networks

 Example:
     python run_puma.py -e ./ToyData/ToyExpressionData.txt -m ./ToyData/ToyMotifData.txt -p ./ToyData/ToyPPIData.txt -i ToyData/ToyMiRList.txt -o test_puma.txt -q output_lioness.txt

puma

class Puma

Puma(object)

Description:
    Using PUMA to infer gene regulatory network.

Usage:
    1. Reading in input data (expression data, motif prior, TF PPI data, miR)
    2. Computing coexpression network
    3. Normalizing networks
    4. Running PUMA algorithm
    5. Writing out PUMA network

Authors:
    cychen, davidvi, alessandromarin

init

init__(self, expression_file, motif_file, ppi_file, mir_file, save_memory = False, save_tmp=True, remove_missing=False, keep_expression_matrix = False)
# =====================================================================
# Data loading
# =====================================================================

remove_missing

remove_missing(self)
Remove genes and tfs not present in all files.

normalize_network

normalize_network(self, x)

puma_loop

puma_loop(self, correlation_matrix, motif_matrix, ppi_matrix)
Puma algorithm

t_function

t_function(x, y=None)
T function

update_diagonal

update_diagonal(diagonal_matrix, num, alpha, step)
Update diagoanl

pearson_results_data_frame

pearson_results_data_frame(self)
Results to data frame

pearson_results_data_frame

pearson_results_data_frame(self)
Results to data frame.

save_puma_results

save_puma_results(self, path='puma.npy')

top_network_plot

top_network_plot(self, top = 100, file = 'puma_top_100.png')
Select top genes.

shape_plot_network

shape_plot_network(self, subset_puma_results, file = 'puma.png')
Create plot

create_plot

create_plot(self, unique_genes, links, file = 'puma.png')
Run plot

split_label

split_label(label)

return_puma_indegree

return_puma_indegree(self)
Return Puma indegree

return_puma_outdegree

 return_puma_outdegree(self)
 Return Puma outdegree