miEAA Command Line Interface and API

The miRNA Enrichment Analysis and Annotation Tool (miEAA) facilitates the functional analysis of miRNA sets. This package provides a miEAA REST API wrapper and command line interface. As such, a stable internet connection is required to utilize these tools.

To learn more about miEAA or to utilize our online interface, please visit our web server.

All miEAA tools are provided and hosted by the Chair for Clinical Bioinformatics at Saarland University.

Source code is available on GitHub.

Documentation is available on Read the Docs.

Users can execute miEAA commands directly from the command line:

$ mieaa -h

A REST API is also provided for scripting purposes:

from mieaa import API

mieaa_api = API()

Complete examples for both Python and R (using reticulate) are available under Examples.

Installation

Dependencies:

• Python >= 3.5
• Requests >= 2.19

Python Package Index

$ pip install mieaa

Conda

$ conda install -c ccb-sb mieaa

API Wrapper

mieaa

API()	miEAA api wrapper class.
API.convert_mirbase(mirnas, Iterable[str], …)	Convert a set of either miRNAs/precursors from one miRbase version to another
API.to_precursor(mirnas, Iterable[str], IO], …)	Convert from mirna->precursor
API.to_mirna(mirnas, Iterable[str], IO], …)	Convert from precursor->mirna
API.get_enrichment_categories(mirna_type, …)	Get possible enrichment categories
API.get_enrichment_parameters()	Retrieve parameters used during enrichment analysis
API.get_gui_url(page[, job_id])	Get specific url to page in web tool
API.get_progress()	Retrieve enrichment analysis progress
API.get_results(results_format, …[, retries])	Return results in json or csv format
API.new_session()	Start a new session, clearing all job results
API.open_gui([page, job_id])	Open specific mieaa web tool page in browser
API.run_gsea(test_set, Iterable[T_co], IO], …)	Start miRNA Set Enrichment Analysis
API.run_ora(test_set, Iterable[T_co], IO], …)	Start Over Enrichment Analysis
API.save_enrichment_results(save_file, IO], …)	Save results in specified format

Examples

Python

Python API Example

A barebones example script can be found on Github.

from mieaa import API

mieaa_api = API()

Target sets, categories, and reference sets support strings, iterables, and file objects.

Mixed delimiters should still function, but are not recommended.

#initial_mirnas = ['hsa-miR-374c', 'hsa-miR-642b', 'hsa-miR-550b', 'hsa-miR-107', 'hsa-miR-125b']
initial_mirnas = 'hsa-miR-374c hsa-miR-642b,hsa-miR-550b;hsa-miR-107;hsa-miR-125b'

Convert between miRBase versions

Results can be optionally saved to a file by specifying the to_file argument.

# mieaa_api.convert_mirbase(initial_precursors, 9.1, 22, 'precursor', to_file='mirnas.txt')
updated_mirnas = mieaa_api.convert_mirbase(initial_mirnas, 16, 22, 'mirna')
updated_mirnas

['hsa-miR-374c-5p',
 'hsa-miR-642b-3p',
 'hsa-miR-550b-3p',
 'hsa-miR-107',
 'hsa-miR-125b-5p']

Convert between miRNAs <-> precursors

Results can be optionally saved to a file by specifying the to_file argument.

Some names are not uniquely converted. We can specify conversion type as either all (default) or unique.

We can also decide whether we want our output to only include converted results with multiple-mapped values separated by a semicolon (default, oneline), on their own individual lines (newline), or a tab separated input - output (tabsep).

precursors = mieaa_api.to_precursor(updated_mirnas, to_file='./precursors.txt', conversion_type='all')
precursors

['hsa-mir-374c',
 'hsa-mir-642b',
 'hsa-mir-550b-1;hsa-mir-550b-2',
 'hsa-mir-107',
 'hsa-mir-125b-1;hsa-mir-125b-2']

with open('./precursors.txt') as prec_file:
    mirnas = mieaa_api.to_mirna(prec_file, output_format='tabsep')
mirnas

['hsa-mir-374c\thsa-miR-374c-5p;hsa-miR-374c-3p',
 'hsa-mir-642b\thsa-miR-642b-5p;hsa-miR-642b-3p',
 'hsa-mir-550b-1\thsa-miR-550b-3p;hsa-miR-550b-2-5p',
 'hsa-mir-550b-2\thsa-miR-550b-3p;hsa-miR-550b-2-5p',
 'hsa-mir-107\thsa-miR-107',
 'hsa-mir-125b-1\thsa-miR-125b-5p;hsa-miR-125b-1-3p',
 'hsa-mir-125b-2\thsa-miR-125b-5p;hsa-miR-125b-2-3p']

Enrichment Analysis

Starting Enrichment Analysis

Run Gene Set Enrichment Analysis (GSEA) or Over-representation Analysis (ORA).

Please refer to documentation for possible keyword arguments.

For ORA, if reference_set is not specified or is left empty, default to using miEAA reference sets for specified categories.

# mieaa_api.run_gsea(precursors, ['HMDD, mndr'], 'precursor', 'hsa')
with open('./precursors.txt', 'r') as test_set_file:
    mieaa_api.run_ora(test_set_file, ['HMDD, mndr'], 'precursor', 'hsa', reference_set='')

Viewing computation progress

mieaa_api.get_progress()

0.7

Retrieving Enrichment Results

Get results after enrichment analysis has been completed, determining how often to check progress via check_progress_interval (default is 5 seconds).

json = mieaa_api.get_results(check_progress_interval=5)

The returned data can be easily turned into a pandas dataframe.

import pandas as pd
cols = ['category', 'subcategory', 'enrichment', 'p-value', 'p-adjusted', 'q-value', 'expected', 'observed', 'mirnas/precursors']
df = pd.DataFrame(json, columns=cols)
df.head()

category	subcategory	enrichment	p-value	p-adjusted	q-value	expected	observed	mirnas/precursors
Diseases (HMDD)	Alopecia	over-represented	0.0017138	0.0478121	0.0478121	0.0678879	2	hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Atopic Dermatitis	over-represented	0.0021345	0.0478121	0.0478121	0.075431	2	hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Lichen Planus	over-represented	0.0017138	0.0478121	0.0478121	0.0678879	2	hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Myotonic Muscular Dystrophy	over-represented	0.00063573	0.0356009	0.0356009	0.196121	3	hsa-mir-107; hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Nevus	over-represented	0.000145941	0.0163454	0.0163454	0.0226293	2	hsa-mir-125b-1; hsa-mir-125b-2

Results can also be obtained as a csv string.

csv_string = mieaa_api.get_results('csv')

Saving Enrichment Results

Results can be automatically saved to a json or csv (default) file.

# mieaa_api.save_enrichment_results('./example.json', file_type='json')
file_contents = mieaa_api.save_enrichment_results('./results.csv')

Alternatively, we can write the csv results to a file.

with open('results_2.csv', 'w+') as outfile:
    outfile.write(csv_string)

Miscellaneous

After running an analysis, we may wish to view the parameters we used for our analysis.

mieaa_api.get_enrichment_parameters()

{'enrichment_analysis': 'ORA',
 'p_value_adjustment': 'fdr',
 'independent_p_adjust': True,
 'significance_level': 0.05,
 'threshold_level': 2,
 'categories': ['HMDD_precursor', 'MNDR_precursor'],
 'reference_set': '',
 'testset_file': <_io.TextIOWrapper name='./precursors.txt' mode='r' encoding='UTF-8'>}

Upon running an analysis, our API instance is assigned a unique Job ID.

If we wish to reuse the same instance to run a new analysis, we must create a new session.

mieaa_api.new_session()

R

R API Example

The reticulate library allows us to utilize the wrapper class in R, assuming Python is also installed.

A barebones example script can be found on Github.

library(reticulate)
mieaa = import("mieaa")
mieaa_api = mieaa$API()

Target sets, categories, and reference sets support strings, iterables, and file objects.

Mixed delimiters should still function, but are not recommended.

# list('hsa-miR-374c', 'hsa-miR-642b', 'hsa-miR-550b', 'hsa-miR-107', 'hsa-miR-125b')
initial_mirnas = 'hsa-miR-374c hsa-miR-642b,hsa-miR-550b;hsa-miR-107;hsa-miR-125b'

Convert between miRBase versions

Results can be optionally saved to a file by specifying the to_file argument.

# mieaa_api$convert_mirbase(initial_precursors, '9.1', '22', 'precursor', to_file='mirnas.txt')
updated_mirnas = mieaa_api$convert_mirbase(initial_mirnas, '16', '22', 'mirna')
updated_mirnas

[[1]]
[1] "hsa-miR-374c-5p"

[[2]]
[1] "hsa-miR-642b-3p"

[[3]]
[1] "hsa-miR-550b-3p"

[[4]]
[1] "hsa-miR-107"

[[5]]
[1] "hsa-miR-125b-5p"

Convert between miRNAs <-> precursors

Results can be optionally saved to a file by specifying the to_file argument.

Some names are not uniquely converted. We can specify conversion type as either all (default) or unique.

We can also decide whether we want our output to only include converted results with multiple-mapped values separated by a semicolon (default, oneline), on their own individual lines (newline), or a tab separated input - output (tabsep).

precursors = mieaa_api$to_precursor(updated_mirnas, to_file='./precursors.txt', conversion_type='all')
precursors

[[1]]
[1] "hsa-mir-374c"

[[2]]
[1] "hsa-mir-642b"

[[3]]
[1] "hsa-mir-550b-1;hsa-mir-550b-2"

[[4]]
[1] "hsa-mir-107"

[[5]]
[1] "hsa-mir-125b-1;hsa-mir-125b-2"

py = import_builtins()  # part of 'reticulate'

# Files need to be python file objects
with(py$open("precursors.txt", 'r') %as% prec_file, {
    mirnas = mieaa_api$to_mirna(prec_file, output_format='tabsep')
})
mirnas

[[1]]
[1] "hsa-mir-374c\thsa-miR-374c-5p;hsa-miR-374c-3p"

[[2]]
[1] "hsa-mir-642b\thsa-miR-642b-5p;hsa-miR-642b-3p"

[[3]]
[1] "hsa-mir-550b-1\thsa-miR-550b-3p;hsa-miR-550b-2-5p"

[[4]]
[1] "hsa-mir-550b-2\thsa-miR-550b-3p;hsa-miR-550b-2-5p"

[[5]]
[1] "hsa-mir-107\thsa-miR-107"

[[6]]
[1] "hsa-mir-125b-1\thsa-miR-125b-5p;hsa-miR-125b-1-3p"

[[7]]
[1] "hsa-mir-125b-2\thsa-miR-125b-5p;hsa-miR-125b-2-3p"

Enrichment Analysis

Starting Enrichment Analysis

Run Gene Set Enrichment Analysis (GSEA) or Over-representation Analysis (ORA).

Please refer to documentation for possible keyword arguments.

For ORA, if reference_set is not specified or is left empty, default to using miEAA reference sets for specified categories.

# mieaa_api$run_gsea(precursors, ['HMDD, mndr'], 'precursor', 'hsa')
with(py$open("precursors.txt", 'r') %as% test_set_file, {
    mieaa_api$run_ora(test_set_file, list('HMDD, mndr'), 'precursor', 'hsa', reference_set='')
})

Viewing computation progress

mieaa_api$get_progress()

0.7

Retrieving Enrichment Results

Get results after enrichment analysis has been completed, determining how often to check progress via check_progress_interval (default is 5 seconds).

json = mieaa_api$get_results(check_progress_interval=5)

The returned data can be easily turned into a dataframe.

cols = c('category', 'subcategory', 'enrichment', 'p-value', 'p-adjusted', 'q-value', 'expected', 'observed', 'mirnas/precursors')
df = data.frame(matrix(unlist(json), nrow=length(json), byrow=T))
colnames(df) = cols
head(df)

category	subcategory	enrichment	p-value	p-adjusted	q-value	expected	observed	mirnas/precursors
Diseases (HMDD)	Alopecia	over-represented	0.0017138	0.0478121	0.0478121	0.0678879	2	hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Atopic Dermatitis	over-represented	0.0021345	0.0478121	0.0478121	0.075431	2	hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Lichen Planus	over-represented	0.0017138	0.0478121	0.0478121	0.0678879	2	hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Myotonic Muscular Dystrophy	over-represented	0.00063573	0.0356009	0.0356009	0.196121	3	hsa-mir-107; hsa-mir-125b-1; hsa-mir-125b-2
Diseases (HMDD)	Nevus	over-represented	0.000145941	0.0163454	0.0163454	0.0226293	2	hsa-mir-125b-1; hsa-mir-125b-2

Results can also be obtained as a csv string.

csv_string = mieaa_api$get_results('csv')

Saving Enrichment Results

Results can be automatically saved to a json or csv (default) file.

# mieaa_api$save_enrichment_results('./example.json', file_type='json')
file_contents = mieaa_api$save_enrichment_results('./results.csv')

Alternatively, we can write the csv results to a file.

outfile = file("./results_2.csv")
writeLines(csv_string, outfile)
close(outfile)

Miscellaneous

After running an analysis, we may wish to view the parameters we used for our analysis.

mieaa_api$get_enrichment_parameters()

$enrichment_analysis
[1] "ORA"

$p_value_adjustment
[1] "fdr"

$independent_p_adjust
[1] TRUE

$significance_level
[1] 0.05

$threshold_level
[1] 2

$categories
[1] "HMDD_precursor" "MNDR_precursor"

$reference_set
[1] ""

$testset_file
<_io.TextIOWrapper name='precursors.txt' mode='r' encoding='UTF-8'>

Upon running an analysis, our API instance is assigned a unique Job ID.

If we wish to reuse the same instance to run a new analysis, we must create a new session.

mieaa_api$new_session()

Command Line Interface

Commands can be invoked via the command line using mieaa SUBCOMMAND.Help and options for all subcommands can be view with mieaa SUBCOMMAND -h

Supported Species

• hsa - Homo sapiens
• mmu - Mus musculus
• rno - Rattus norvegicus
• ath - Arabidopsis thaliana
• bta - Bos taurus
• cel - Caenorhabditis elegans
• dme - Drosophila melanogaster
• dre - Danio rerio
• gga - Gallus gallus
• ssc - Sus scrofa

Specifying precursorsFor subcommands where you need to specify precursor or mature, mature is always assumed unless the --precursor (-p) flag is set.

Mutually exclusive optionsMost subcommands require one of --mirna-set (-m) or --mirna-set-file (-M) to be specified.

• mieaa SUBCOMMAND --mirna-set MIRNA [MIRNA ...]
• mieaa SUBCOMMAND --mirna-set MIRNAS_STRING
• mieaa SUBCOMMAND --mirna-set-file MIRNA_FILE

Subcommands

to_precursor

Convert miRNA -> precursor

usage: miEAA to_precursor [-h] [-v] [-m MIRNA_SET [MIRNA_SET ...]]
                          [-M MIRNA_SET_FILE] [-p] [-o OUTFILE]
                          [--oneline | --newline | --tabsep] [-u]

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Always print results to stdout
  -p, --precursor, --precursors
                        Use if running on a set of precursors as opposed to
                        miRNAs
  -o OUTFILE, --outfile OUTFILE
                        Save results to provided file
  --oneline             Output style: Multi-mapped ids are separated by a
                        semicolon (default)
  --newline             Output style: Multi-mapped ids are separated by a
                        newline
  --tabsep              Output style: Tab-separated `original converted` ids
  -u, --unique          Only output ids that map uniquely

mutually exclusive required arguments:
  either a set or file must be provided

  -m MIRNA_SET [MIRNA_SET ...], --mirna-set MIRNA_SET [MIRNA_SET ...]
                        miRNA/precursor target set
  -M MIRNA_SET_FILE, --mirna-set-file MIRNA_SET_FILE
                        Specify miRNA/precursor target set via file

Examples:

$ mieaa to_precursor -m hsa-miR-20b-5p hsa-miR-144-5p --tabsep --unique
$ mieaa to_precursor -m 'hsa-miR-20b-5p,hsa-miR-144-5p' --newline -o precursors.txt
$ mieaa to_precursor -M mirnas.txt --outfile precursors.txt

to_mirna

Converting between precursor -> miRNA

usage: miEAA to_mirna [-h] [-v] [-m MIRNA_SET [MIRNA_SET ...]]
                      [-M MIRNA_SET_FILE] [-p] [-o OUTFILE]
                      [--oneline | --newline | --tabsep] [-u]

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Always print results to stdout
  -p, --precursor, --precursors
                        Use if running on a set of precursors as opposed to
                        miRNAs
  -o OUTFILE, --outfile OUTFILE
                        Save results to provided file
  --oneline             Output style: Multi-mapped ids are separated by a
                        semicolon (default)
  --newline             Output style: Multi-mapped ids are separated by a
                        newline
  --tabsep              Output style: Tab-separated `original converted` ids
  -u, --unique          Only output ids that map uniquely

mutually exclusive required arguments:
  either a set or file must be provided

  -m MIRNA_SET [MIRNA_SET ...], --mirna-set MIRNA_SET [MIRNA_SET ...]
                        miRNA/precursor target set
  -M MIRNA_SET_FILE, --mirna-set-file MIRNA_SET_FILE
                        Specify miRNA/precursor target set via file

Examples:

$ mieaa to_mirna -m hsa-mir-20b hsa-mir-144 --tabsep --unique
$ mieaa to_mirna -m 'hsa-mir-20b,hsa-mir-144' --newline -o mirnas.txt
$ mieaa to_mirna -M precursors.txt --outfile mirnas.txt

convert_mirbase

Converting miRBase version

usage: miEAA convert_mirbase [-h] [-v] [-m MIRNA_SET [MIRNA_SET ...]]
                             [-M MIRNA_SET_FILE] [-p] [-o OUTFILE]
                             [--oneline | --newline | --tabsep] [--to TO]
                             FROM

positional arguments:
  FROM                  mirBase version to convert miRNAs/precursors from

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Always print results to stdout
  -p, --precursor, --precursors
                        Use if running on a set of precursors as opposed to
                        miRNAs
  -o OUTFILE, --outfile OUTFILE
                        Save results to provided file
  --oneline             Output style: Multi-mapped ids are separated by a
                        semicolon (default)
  --newline             Output style: Multi-mapped ids are separated by a
                        newline
  --tabsep              Output style: Tab-separated `original converted` ids
  --to TO               mirBase version to convert miRNAs/precursors from
                        (default=22)

mutually exclusive required arguments:
  either a set or file must be provided

  -m MIRNA_SET [MIRNA_SET ...], --mirna-set MIRNA_SET [MIRNA_SET ...]
                        miRNA/precursor target set
  -M MIRNA_SET_FILE, --mirna-set-file MIRNA_SET_FILE
                        Specify miRNA/precursor target set via file

Examples:

$ mieaa convert_mirbase 16 -m hsa-miR-642b,hsa-miR-550b
$ mieaa convert_mirbase 16 --to 22 -m hsa-miR-642b hsa-miR-550b
$ mieaa convert_mirbase 16  -M version_16.txt -o version_22.txt

gsea

Gene Set Enrichment Analysis (GSEA)

usage: miEAA gsea [-h] [-v] [-m MIRNA_SET [MIRNA_SET ...]] [-M MIRNA_SET_FILE]
                  [-p] [-o OUTFILE] [-x] [-c CATEGORIES [CATEGORIES ...]]
                  [-C CATEGORIES_FILE] [-t THRESHOLD] [-s SIGNIFICANCE] [-g]
                  [-a {none,fdr,bonferroni,BY,holm,hochberg,hommel}]
                  [--csv | --json]
                  {hsa,mmu,rno,ath,bta,cel,dme,dre,gga,ssc}

positional arguments:
  {hsa,mmu,rno,ath,bta,cel,dme,dre,gga,ssc}
                        Species

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Always print results to stdout
  -p, --precursor, --precursors
                        Use if running on a set of precursors as opposed to
                        miRNAs
  -o OUTFILE, --outfile OUTFILE
                        Save results to provided file
  -x, --no-results      Do not monitor progress or obtain results. Can
                        retrieve later using Job ID.
  -t THRESHOLD, --threshold THRESHOLD
                        Filter out subcategories that contain less than this
                        many miRNAs/precursors (default=2)
  -s SIGNIFICANCE, --significance SIGNIFICANCE, --alpha SIGNIFICANCE
                        Significance level (default=0.05)
  -g, --group-adjust    Adjust p-values over aggregated groups (By default
                        each group is adjusted independently)
  -a {none,fdr,bonferroni,BY,holm,hochberg,hommel}, --adjustment {none,fdr,bonferroni,BY,holm,hochberg,hommel}
                        p-value adjustment method (default='fdr')
  --csv                 Store results in output file in csv format (default)
  --json                Store results in output file in json format (default
                        is csv)

mutually exclusive required arguments:
  either a set or file must be provided

  -m MIRNA_SET [MIRNA_SET ...], --mirna-set MIRNA_SET [MIRNA_SET ...]
                        miRNA/precursor target set
  -M MIRNA_SET_FILE, --mirna-set-file MIRNA_SET_FILE
                        Specify miRNA/precursor target set via file

mutually exclusive optional arguments:
  either a set or file may be provided

  -c CATEGORIES [CATEGORIES ...], --categories CATEGORIES [CATEGORIES ...]
                        Set of categories to include in analysis, can include
                        `all`, `default`, `expert` or specific categories
  -C CATEGORIES_FILE, --categories-file CATEGORIES_FILE
                        File specifying categories to include in analysis

Examples:

$ mieaa gsea hsa --precursors -M precursors.txt -C categories.txt -o results.csv
$ mieaa gsea hsa -p -M precursors.txt -c HMDD MNDR > results.csv
$ mieaa gsea mmu -M mirnas.txt -C categories.txt --adjustment none
$ mieaa gsea rno -M mirnas.txt -C categories.txt -a bonferroni --json -o results.json

ora

Over-representation Analysis (ORA)

usage: miEAA ora [-h] [-v] [-m MIRNA_SET [MIRNA_SET ...]] [-M MIRNA_SET_FILE]
                 [-p] [-o OUTFILE] [-x] [-c CATEGORIES [CATEGORIES ...]]
                 [-C CATEGORIES_FILE] [-t THRESHOLD] [-s SIGNIFICANCE] [-g]
                 [-a {none,fdr,bonferroni,BY,holm,hochberg,hommel}]
                 [--csv | --json] [-r REFERENCE_SET [REFERENCE_SET ...]]
                 [-R REFERENCE_SET_FILE]
                 {hsa,mmu,rno,ath,bta,cel,dme,dre,gga,ssc}

positional arguments:
  {hsa,mmu,rno,ath,bta,cel,dme,dre,gga,ssc}
                        Species

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Always print results to stdout
  -p, --precursor, --precursors
                        Use if running on a set of precursors as opposed to
                        miRNAs
  -o OUTFILE, --outfile OUTFILE
                        Save results to provided file
  -x, --no-results      Do not monitor progress or obtain results. Can
                        retrieve later using Job ID.
  -t THRESHOLD, --threshold THRESHOLD
                        Filter out subcategories that contain less than this
                        many miRNAs/precursors (default=2)
  -s SIGNIFICANCE, --significance SIGNIFICANCE, --alpha SIGNIFICANCE
                        Significance level (default=0.05)
  -g, --group-adjust    Adjust p-values over aggregated groups (By default
                        each group is adjusted independently)
  -a {none,fdr,bonferroni,BY,holm,hochberg,hommel}, --adjustment {none,fdr,bonferroni,BY,holm,hochberg,hommel}
                        p-value adjustment method (default='fdr')
  --csv                 Store results in output file in csv format (default)
  --json                Store results in output file in json format (default
                        is csv)

mutually exclusive required arguments:
  either a set or file must be provided

  -m MIRNA_SET [MIRNA_SET ...], --mirna-set MIRNA_SET [MIRNA_SET ...]
                        miRNA/precursor target set
  -M MIRNA_SET_FILE, --mirna-set-file MIRNA_SET_FILE
                        Specify miRNA/precursor target set via file

mutually exclusive optional arguments:
  either a set or file may be provided

  -c CATEGORIES [CATEGORIES ...], --categories CATEGORIES [CATEGORIES ...]
                        Set of categories to include in analysis, can include
                        `all`, `default`, `expert` or specific categories
  -C CATEGORIES_FILE, --categories-file CATEGORIES_FILE
                        File specifying categories to include in analysis

mutually exclusive optional arguments:
  either a set or file may be provided

  -r REFERENCE_SET [REFERENCE_SET ...], --reference-set REFERENCE_SET [REFERENCE_SET ...]
                        (Optional) Set of background miRNAs/precursors
  -R REFERENCE_SET_FILE, --reference-set-file REFERENCE_SET_FILE
                        (Optional) File specifying background
                        miRNAs/precursors

Examples:

$ mieaa ora hsa --precursors -M precursors.txt -C categories.txt > results.csv
$ mieaa ora hsa -p -M precursors.txt -c HMDD MNDR -o results.csv
$ mieaa ora hsa -p -M precursors.txt -C categories.txt -R reference.txt
$ mieaa ora mmu -M mirnas.txt -C categories.txt --adjustment none
$ mieaa ora rno -M mirnas.txt -C categories.txt -a bonferroni --json -o results.json

open

Open the specified mieaa web tool page in browser

usage: miEAA open [-h] [-v] [-j JOB_ID] {input,progress,results}

positional arguments:
  {input,progress,results}
                        Open MiEAA interface in browser

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         Always print results to stdout
  -j JOB_ID, --jobid JOB_ID
                        Job ID

Examples:

$ mieaa open input
$ mieaa open progress -j 31b41542-7856-40be-91b2-fd6afe28fa0b
$ mieaa open results --jobid 31b41542-7856-40be-91b2-fd6afe28fa0b

Links

• miEAA Web Server
• miEAA on the Python Package Index
• miEAA on Anaconda
• Chair for Clinical Bioinformatics, Saarland University
• GitHub IssueTracker
• GitHub Source Code

License

MIT License

Copyright (c) 2020 Jeffrey Solomon

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.