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Title: Massive Associative K-biclustering (MAK) v1.0

Abstract

Massive Associative K-biclustering (MAK) is a method to address the key limitations in other biclustering approaches. In a formal evaluation against new simulated data sets with more realistic properties MAK improves recovery of biclusters exhibiting common behavior across conditions, with the highest F1 scores, a measure of a test's accuracy, and lowest false positive rates, the probability of falsely rejecting the null hypothesis, compared to established methods. The developers show how MAK can be applied to a popular data set from Saccharomyces cerevisiae to uncover new properties not captured by other methods. MAK biclusters showed increased statistical enrichments for known regulatory associations and gene functions, including for nearly 30% of the novel MAK biclusters not found by other methods. Compared to other methods, MAK results had at least two-fold higher coverage of differential expression in the overall dataset, an overlapping bicluster network with distinct modules for protein expression and energy metabolism, and biologically relevant overlapping biclusters including cases of combinatorial regulation. MAK is suited for large datasets with many potentially noisy and overlapping patterns of varying sizes.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bettis Atomic
Sponsoring Org.:
USDOE
Contributing Org.:
LAWRENCE BERKLEY NATIONAL LABORATORY
OSTI Identifier:
1347092
Report Number(s):
MAK v1.0; 005187MLTPL00
R&D Project: KP1601010; 2016-175
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Software
Software Revision:
00
Software Package Number:
005187
Software CPU:
MLTPL
Source Code Available:
Yes
Other Software Info:
LBNL reserves all rights to distribution of software.
Country of Publication:
United States

Citation Formats

JOACHIMIAK, MARCIN, TUGLUS, CATHY, SALAMZADE, RAUF, Arkin, Adam P., and Van der Laan, Mark. Massive Associative K-biclustering (MAK) v1.0. Computer software. Vers. 00. USDOE. 1 Mar. 2017. Web.
JOACHIMIAK, MARCIN, TUGLUS, CATHY, SALAMZADE, RAUF, Arkin, Adam P., & Van der Laan, Mark. (2017, March 1). Massive Associative K-biclustering (MAK) v1.0 (Version 00) [Computer software].
JOACHIMIAK, MARCIN, TUGLUS, CATHY, SALAMZADE, RAUF, Arkin, Adam P., and Van der Laan, Mark. Massive Associative K-biclustering (MAK) v1.0. Computer software. Version 00. March 1, 2017.
@misc{osti_1347092,
title = {Massive Associative K-biclustering (MAK) v1.0, Version 00},
author = {JOACHIMIAK, MARCIN and TUGLUS, CATHY and SALAMZADE, RAUF and Arkin, Adam P. and Van der Laan, Mark},
abstractNote = {Massive Associative K-biclustering (MAK) is a method to address the key limitations in other biclustering approaches. In a formal evaluation against new simulated data sets with more realistic properties MAK improves recovery of biclusters exhibiting common behavior across conditions, with the highest F1 scores, a measure of a test's accuracy, and lowest false positive rates, the probability of falsely rejecting the null hypothesis, compared to established methods. The developers show how MAK can be applied to a popular data set from Saccharomyces cerevisiae to uncover new properties not captured by other methods. MAK biclusters showed increased statistical enrichments for known regulatory associations and gene functions, including for nearly 30% of the novel MAK biclusters not found by other methods. Compared to other methods, MAK results had at least two-fold higher coverage of differential expression in the overall dataset, an overlapping bicluster network with distinct modules for protein expression and energy metabolism, and biologically relevant overlapping biclusters including cases of combinatorial regulation. MAK is suited for large datasets with many potentially noisy and overlapping patterns of varying sizes.},
doi = {},
year = {Wed Mar 01 00:00:00 EST 2017},
month = {Wed Mar 01 00:00:00 EST 2017},
note =
}

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