CorSig – Statistical Inference of Correlation Significance

CorSig

:: DESCRIPTION

CorSig: A General Framework for Estimating Statistical Significance of Correlation and Its Application to Gene Co-expression Analysis.

::DEVELOPER

 the Tsai lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • R
:: DOWNLOAD

 CorSig

:: MORE INFORMATION

Citation

PLoS One. 2013 Oct 23;8(10):e77429. doi: 10.1371/journal.pone.0077429. eCollection 2013.
CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.
Wang HQ1, Tsai CJ.

QUASSI – Quantifying Significance of MHC II Residues

QUASSI

:: DESCRIPTION

QUASSI is a project to solve integer linear programming problem proposed in bioinformatics

::DEVELOPER

QUASSI team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • JRE

:: DOWNLOAD

 QUASSI

:: MORE INFORMATION

Citation

Quantifying Significance of MHC II Residues.
Ying Fan, Ruoshui Lu, Lusheng Wang, Andreatta M, Shuai Cheng Li.
IEEE/ACM Trans Comput Biol Bioinform. 2014 Jan-Feb;11(1):17-25. doi: 10.1109/TCBB.2013.138.

SAPS 2.2.0 – Significance Analysis of Prognostic Signatures

SAPS  2.2.0

:: DESCRIPTION

SAPS provides a robust method for identifying biologically significant gene sets associated with patient survival.

::DEVELOPER

Beck Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ WIndows/ MacOsX
  • R
  • BioConductor

:: DOWNLOAD

 SAPS

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2013;9(1):e1002875. doi: 10.1371/journal.pcbi.1002875. Epub 2013 Jan 24.
Significance analysis of prognostic signatures.
Beck AH1, Knoblauch NW, Hefti MM, Kaplan J, Schnitt SJ, Culhane AC, Schroeder MS, Risch T, Quackenbush J, Haibe-Kains B.

SAM 4.01 – Significance Analysis of Microarrays

SAM 4.01

:: DESCRIPTION

SAM (Significance Analysis of Microarrays) is a statistical technique for finding significant genes in a set of microarray experiments, a supervised learning software for genomic expression data mining.

The input to SAM is gene expression measurements from a set of microarray experiments, as well as a response variable from each experiment. The response variable may be a grouping like untreated, treated [either unpaired or paired], a multiclass grouping (like breast cancer, lymphoma, colon cancer, . . . ), a quantitative variable (like blood pressure) or a possibly censored survival time.

::DEVELOPER

Stanford University Statistics and Biochemistry Labs

:: SCREENSHOTS

:: REQUIREMENTS

:: DOWNLOAD

 SAM

:: MORE INFORMATION

Citation

Jun Li and Robert Tibshirani (2011)
Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data.
Stat Methods Med Res. 2011 Nov 28.

PathScan – Assesses Significance of Gene Groups

PathScan

:: DESCRIPTION

The PathScan package is implemented strictly as a test of a set of genes, e.g. a pathway, for a single individual. Specifically, knowing the gene lengths in the pathway, the number of genes that have at least one mutation, and the estimated background mutation rate, one can test the null hypothesis that these observed mutations are well-explained simply by the mechanism of random background mutation. However, it will often be the case that data for a pathway will be available for many individuals, meaning that we now have many tests of the given (single) hypothesis. (This should not be confused with the scenario of multiple hypothesis testing.) The set of values contains much more information than a single value, suggesting that significance must be judged on the basis of the collective result. For example, while no single p-value by itself may exceed the chosen statistical threshold, the overall set of probabilities may still give the impression of significance. Properly combining such numbers is a necessary, but not entirely trivial task. This package basically serves as a high-level interface to first perform individual tests using the methods of PathScan, and then to properly combine the resulting p-values using the methods of CombinePvals.

::DEVELOPER

Ding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/windows/MacOsX
  • Perl

:: DOWNLOAD

 PathScan

:: MORE INFORMATION

Citation:

Wendl MC, Wallis JW, Lin L, Kandoth C, Mardis ER, Wilson RK, Ding L.
PathScan: A Tool for Discerning Mutational Significance in Groups of Putative Cancer Genes.
Bioinformatics. 2011 Jun 15;27(12):1595-602. Epub 2011 Apr 14

GeneValorization – Gene List significance at-a-glance

GeneValorization

:: DESCRIPTION

GeneValorization is a web-based Java application tool  which aims at making the most of the text-mining effort done downstream to all high throughput technology assays.GeneValorization gives a very clear and handful overview of the bibliography corresponding to one particular gene list.

::DEVELOPER

GeneValorization team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bioinformatics. 2011 Apr 15;27(8):1187-9. doi: 10.1093/bioinformatics/btr073. Epub 2011 Feb 23.
Gene List significance at-a-glance with GeneValorization.
Brancotte B1, Biton A, Bernard-Pierrot I, Radvanyi F, Reyal F, Cohen-Boulakia S.

COMBASSOC 1.3 – Overall Significance of Multiple analysis

COMBASSOC 1.3

:: DESCRIPTION

COMBASSOC allows the user to run any number of analyses with a choice of the programs RUNGC and/or SCANASSOC and/or RUNLR, all of which analyse data from cases and controls genotyped for multiple markers. COMBASSOC combines the p values that are obtained from these analyses into a single measure of significance and then carries out sequential Monte Carlo testing to test the overall significance. All of the analysis programs used by COMBASSOC, along with their full documentation, are provided in the support programs for GENECOUNTING.

::DEVELOPER

Dave Curtis

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

COMBASSOC

:: MORE INFORMATION

Citation:

Curtis, D., Vine, A. E. and Knight, J. 2008.
A simple method for assessing the strength of evidence for association at the level of the whole gene.
Advances and Applications in Bioinformatics and Chemistry 1:115-120.

topGO 0.97 – Calculat Significance of Biological Terms from Gene Expression Data

topGO 0.97

:: DESCRIPTION

topGO (topology-based Gene Ontology scoring) is a software package for calculating the significance of biological terms from gene expression data. It implements various standard and advanced new algorithms for determining the relevance of Gene Ontology groups from microarrays. A specific feature of the advanced algorithms is the exploitation of the hierarchical graph structure of the GO annotation for coping with the large number of GO groups. Often, related biological terms are scored with a similar statistical significance. Dependencies between GO terms can be de-correlated by accounting for the neighborhood of a GO node when calculating its significance. The new algorithms better detect significant GO terms from gene expression data.

::DEVELOPER

Adrian Alexa  , Max-Planck-Institut Informatik

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 topGO

:: MORE INFORMATION

Citation:

Alexa, A.; Rahnenführer, J.; Lengauer, T.,
Improved scoring of functional groups from gene expression data by decorrelating GO graph structure.
Bioinformatics 2006, 22 (13), 1600-7.

STAC 1.2 – Significance Testing for Aberrant Copy-Number

STAC 1.2

:: DESCRIPTION

STAC (Significance Testing for Aberrant Copy-Number) is a method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments. It utilizes two complementary statistics in combination with a novel search strategy. The significance of both statistics is assessed, and P-values are assigned to each location on the genome by using a multiple testing corrected permutation approach. STAC identifies genomic alterations known to be of clinical and biological significance and provides statistical support for 85% of previously reported regions. Moreover, STAC identifies numerous additional regions of significant gain/loss in these data that warrant further investigation. The P-values provided by STAC can be used to prioritize regions for follow-up study in an unbiased fashion.

::DEVELOPER

the Computational Biology and Informatics Laboratory (in the Center for Bioinformatics at the University of Pennsylvania)

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  •  Java

:: DOWNLOAD

 STAC

:: MORE INFORMATION

Citation:

Diskin SJ, Eck T, Greshock J, Mosse YP, Naylor T, Stoeckert CJ Jr, Weber BL, Maris JM, Grant GR.
STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments
Genome Res. 2006 Sep;16(9):1149-58. Epub 2006 Aug 9.

RTP – Calculate Significance of Truncated Products of P-values

RTP

:: DESCRIPTION

RTP: C code for calculating significance of truncated products of P-values

::DEVELOPER

Frank Dudbridge

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • C Compiler

:: DOWNLOAD

  RTP

:: MORE INFORMATION

Citation

Genet Epidemiol. 2003 Dec;25(4):360-6.
Rank truncated product of P-values, with application to genomewide association scans.
Dudbridge F, Koeleman BP.