netzoopy Documentation

(1)

netZooPy Documentation

Release 0.1.1

netZooPy

(2)

(3)

1 Contents 3 1.1 Installation guide . . . 3 1.1.1 Requirements . . . 3 1.1.2 Install . . . 3 1.1.3 Troubleshooting. . . 4 1.2 Functions . . . 4 1.2.1 netZooPy.panda . . . 4 1.2.2 netZooPy.lioness . . . 7 1.2.3 netZooPy.puma . . . 9 1.3 Tutorials . . . 12 1.4 Changelog . . . 12 1.4.1 0.7.2 (2020-07-18) . . . 12 1.4.2 0.7.1 (2020-06-27) . . . 13 1.4.3 0.7.0 (2020-01-18) . . . 13 1.4.4 0.6.2 (Stockholm) (2020-05-15) . . . 13 1.4.5 0.6.1 (2020-01-18) . . . 13 1.4.6 0.5.0 (2019-11-22) . . . 13 1.4.7 0.4.0 (2019-11-18) . . . 13 1.4.8 0.3.0 (2019-11-14) . . . 13 1.4.9 0.2.0 (2019-11-13) . . . 14 1.4.10 0.1.1 (2019-9-3). . . 14 1.4.11 0.1.0 (2019-7-26) . . . 14

2 Indices and tables 15

(4)

(5)

netZooPy Documentation, Release 0.1.1

netZooPy is a catalog of methods for the reconstruction and analysis of gene regulatory networks. netZooPy has started as a merge of the following repositories and forks:

• pypandafrom David Van Ijzendorn,

• Alessandro Marin’s pypanda and Marieke Kuijjer’s pypumafork, and • Cho-Yi Chen’sfork.

• Genis Calderer’spysambarandpycondor

(6)

(7)

CHAPTER

1

1.1 Installation guide

To install netZooPy on your computer, please check the following requirements:

1.1.1 Requirements

• Python 3

In addition to the following pip packages: • networkx • numpy • pandas • matplotlib • scipy • python-igraph

1.1.2 Install

• git clone https://github.com/netZoo/netZooPy.git • cd netZooPy

• pip3 install -e .

• Then you can import netZooPy in your code through import netZooPy

(8)

1.1.3 Troubleshooting

• If you experience installation issues with igraph on MacOs. Please install igraph through brew install igraph.

Please refer to the detailed error fix for MacOs and Ubuntuhere. Refer to the igraph instrallationmanual

• To report any installation issue or function bug, please report through opening anissueon github.

1.2 Functions

1.2.1 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

(9)

(continued from previous page)

-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.

(10)

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(self, subset_panda_results, file = 'panda.png')

(11)

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.

1.2.2 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

(12)

class Lioness

Lioness(Panda) Description:

Using LIONESS to infer single-sample gene regulatory networks.

Usage:

1. Reading in PANDA network and preprocessed middle data

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)

(13)

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

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')

1.2.3 netZooPy.puma

run_puma

Description:

Run PUMA algorithm from the command line.

Usage: run_puma

(continues on next page)

(14)

(continued from previous page)

-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)

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)

(15)

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(self) Results to data frame

pearson_results_data_frame(self) Results to data frame.

save_puma_results

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

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

(16)

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

1.3 Tutorials

Tutorials in netZooPy useJupternotebooks.

• Up_and_running_with_PANDA_and_netZooPyby Daniel C. Morgan.

• Building single-sample regulatory networks using LIONESS and netZooPyby Alex Qi Song. • CONDOR usage exampleby Genìs Calderer.

• SAMBAR usage exampleby Genìs Calderer.

• Running Panda and Lioness using the Graphical Processing Unit (GPU)by Daniel C. Morgan. • Controlling the variance of PANDA networksby Kalyan Palepu.

1.4 Changelog

1.4.1 0.7.2 (2020-07-18)

• changed condor ground truth to match output ofpython-igraph 0.8.2. • PANDA reads arguments as dataframes in addition to file paths

(17)

1.4.2 0.7.1 (2020-06-27)

• Major fix for OTTER behavior across platforms.

1.4.3 0.7.0 (2020-01-18)

• new tool: OTTER • unit test for OTTER • fix for PANDA force field

• tweaks for compatibility of gpuPANDA with cupy

1.4.4 0.6.2 (Stockholm) (2020-05-15)

• Added gpuPANDA, which is a gpu-accelerated implementation of PANDA • Added gpuLIONESS

• Added a gpuPANDA and gpuLIONESS tutorial

• Fixed condor dependency to python-igraph (still under investigation in #82 )

1.4.5 0.6.1 (2020-01-18)

• sambar tutorial • condor tutorial

• added 3.8 to Ubunutu test server (along with 3.6 and 3.7 ) • Created three options for data processing in PANDA.

– Union: adds rows for genes/TFs that are missing in at least one prior (expression, ppi, motif) – Intersection: removes TF/genes that missing in at least one prior

– Legacy: previous data processing behavior

• The default was set to union in netZooM, netZooR, netZooPy as it is the default in netZooC.

1.4.6 0.5.0 (2019-11-22)

• pysambar

1.4.7 0.4.0 (2019-11-18)

• pycondor

1.4.8 0.3.0 (2019-11-14)

• pypuma 1.4. Changelog 13

(18)

1.4.9 0.2.0 (2019-11-13)

• pylioness

1.4.10 0.1.1 (2019-9-3)

• fixed call to save_memory=True

1.4.11 0.1.0 (2019-7-26)

• transition to python 3 • Changelog added to the doc

• pypanda: original import and NaN values in normalized matrices are replaced with values normalized by the overall z-score. This allows running the Toy Data provided in this repository.

netzoopy Documentation