scholarly journals Exploiting sample variability to enhance multivariate analysis of microarray data

2007 ◽  
Vol 23 (20) ◽  
pp. 2733-2740 ◽  
Author(s):  
C. S. Moller-Levet ◽  
C. M. West ◽  
C. J. Miller
Keyword(s):  
Multivariate Analysis ◽  
Microarray Data

Increased Expression of Urokinase-Type Plasminogen Activator mRNA Determines Adverse Prognosis in ErbB2-Positive Primary Breast Cancer

2006 ◽  
Vol 24 (26) ◽  
pp. 4245-4253 ◽  
Author(s):  
Patrick Urban ◽  
Vincent Vuaroqueaux ◽  
Martin Labuhn ◽  
Mauro Delorenzi ◽  
Pratyaksha Wirapati ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Multivariate Analysis ◽  
Mrna Expression ◽  
Microarray Data ◽  
Data Sets ◽  
Breast Cancer Patients ◽  
Qrt Pcr ◽  
Type Plasminogen Activator ◽  
Urokinase Type Plasminogen Activator ◽  
Upa Mrna

Purpose To evaluate and validate mRNA expression markers capable of identifying patients with ErbB2-positive breast cancer associated with distant metastasis and reduced survival. Patients and Methods Expression of 60 genes involved in breast cancer biology was assessed by quantitative real-time PCR (qrt-PCR) in 317 primary breast cancer patients and correlated with clinical outcome data. Results were validated subsequently using two previously published and publicly available microarray data sets with different patient populations comprising 295 and 286 breast cancer samples, respectively. Results Of the 60 genes measured by qrt-PCR, urokinase-type plasminogen activator (uPA or PLAU) mRNA expression was the most significant marker associated with distant metastasis-free survival (MFS) by univariate Cox analysis in patients with ErbB2-positive tumors and an independent factor in multivariate analysis. Subsequent validation in two microarray data sets confirmed the prognostic value of uPA in ErbB2-positive tumors by both univariate and multivariate analysis. uPA mRNA expression was not significantly associated with MFS in ErbB2-negative tumors. Kaplan-Meier analysis showed in all three study populations that patients with ErbB2-positive/uPA–positive tumors exhibited significantly reduced MFS (hazard ratios [HR], 4.3; 95% CI, 1.6 to 11.8; HR, 2.7; 95% CI, 1.2 to 6.2; and, HR, 2.8; 95% CI, 1.1 to 7.1; all P < .02) as compared with the group with ErbB2-positive/uPA–negative tumors who exhibited similar outcome to those with ErbB2-negative tumors, irrespective of uPA status. Conclusion After evaluation of 898 breast cancer patients, uPA mRNA expression emerged as a powerful prognostic indicator in ErbB2-positive tumors. These results were consistent among three independent study populations assayed by different techniques, including qrt-PCR and two microarray platforms.

scholarly journals Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

Microbiology ◽  
2006 ◽  
Vol 152 (1) ◽  
pp. 257-272 ◽  
Author(s):  
Mariët J. van der Werf ◽  
Bart Pieterse ◽  
Nicole van Luijk ◽  
Frank Schuren ◽  
Bianca van der Werff-van der Vat ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Discriminant Analysis ◽  
Pseudomonas Putida ◽  
Microarray Data ◽  
Principal Component

scholarly journals Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

2008 ◽  
Vol 9 (1) ◽  
Author(s):  
Florent Baty ◽  
Daniel Jaeger ◽  
Frank Preiswerk ◽  
Martin M Schumacher ◽  
Martin H Brutsche
Keyword(s):  
Multivariate Analysis ◽  
Microarray Data

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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