StepMiner statistics

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StepMiner specifications

Information


Unique identifier OMICS_28866
Name StepMiner
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline David Dill

Publication for StepMiner

StepMiner citations

 (5)
library_books

Analysis of cardiomyocyte clonal expansion during mouse heart development and injury

2018
Nat Commun
PMCID: 5821855
PMID: 29467410
DOI: 10.1038/s41467-018-02891-z

[…] ounts to TPM following log2 scaling. Total number of reads mapped to the genome showed a bimodal distribution. This bimodal distribution separated high quality data and low-quality data. We performed StepMiner analysis to determine a threshold to identify cells with high quality data. All the cells with low-quality data were removed from subsequent analysis. Clustering was performed using standard […]

library_books

OLFM4, KNG1 and Sec24C identified by proteomics and immunohistochemistry as potential markers of early colorectal cancer stages

2017
PMCID: 5364649
PMID: 28344541
DOI: 10.1186/s12014-017-9143-3

[…] rom the one used in our study using grades instead of pTNM stages. The data were normalised and the “standard robust average algorithm” generated the reference absolute “set expression level” []. The StepMiner algorithm allowed to assign to each protein a specific threshold which was used for result interpretation []. We built several populations and several models for the selected protein/gene ca […]

library_books

Single cell dissection of transcriptional heterogeneity in human colon tumors

2011
Nat Biotechnol
PMCID: 3237928
PMID: 22081019
DOI: 10.1038/nbt.2038

[…] ecked for redundancies, as previously described . Gene-expression arrays included in this study are listed in . Gene-expression thresholds between positive and negative samples were defined using the StepMiner algorithm , and Boolean implication relationships between pairs of genes using the BooleanNet software . Differences in gene-expression levels among different sample groups were evaluated us […]

library_books

Boolean implication networks derived from large scale, whole genome microarray datasets

2008
Genome Biol
PMCID: 2760884
PMID: 18973690
DOI: 10.1186/gb-2008-9-10-r157

[…] expression levels were assigned for each gene in each array, using the log (base 2) of the expression values (Figure illustrates this process).First, a threshold was assigned to each gene using the StepMiner algorithm [], which was originally designed to fit step functions to time-course data. For this application, the expression values for each gene were ordered from low-to-high, and StepMiner […]

library_books

Combined Analysis of Murine and Human Microarrays and ChIP Analysis Reveals Genes Associated with the Ability of MYC To Maintain Tumorigenesis

2008
PLoS Genet
PMCID: 2390767
PMID: 18535662
DOI: 10.1371/journal.pgen.1000090

[…] horoughly washing the cells with PBS ( and ).cDNA microarray analysis was performed on the RNA samples prepared from tumors in which MYC was inactivated and reactivated for different lengths of time. StepMiner analysis () was applied to this time-course microarray experiment to identify changes in gene expression at discrete time points before and after MYC inactivation and reactivation. StepMine […]

Citations

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StepMiner institution(s)
Department of Electrical Engineering, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; Department of Radiology and Stanford University, Stanford, CA, USA; Department of Health Research and Policy and Department of Statistics, Stanford University, Stanford, CA, USA
StepMiner funding source(s)
Supported by the National Institute of Health as part of the Integrative Cancer Biology Program, under grant number NIH 5U56CA112973-02.

StepMiner review

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Martin Abba

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Desktop
Excellent tool for discretization (e.g.: high or low, negative or positive) of continuous variable such as gene expression profiles obtained from chips and RNAseq platforms that provides the cutoff value and also the confidence interval.