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Results for “Principal Component Analysis”

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PubMed2003

DNA microarrays and likelihood ratio bioinformatic methods: discovery of human melanocyte biomarkers.

In this article, some of the advantages and limitations of DNA microarray technologies for gene expression profiling are summarized. As a model experiment, DermArray DNA microarrays were utilized to identify potential biomarkers of cultured normal human melanocytes in two different experimental comparisons. In the first case, melanocyte RNA was compared with vastly dissimilar non-melanocytic RNA samples of normal skin keratinocytes and fibroblasts. In the second case, melanocyte RNA was compared with a primary cutaneous melanoma line (MS7) and a metastatic melanoma cell line (SKMel-28). The alternative approaches provide dramatically different lists of 'normal melanocyte' biomarkers. The most robust biomarkers were identified using principal component analysis bioinformatic methods related to likelihood ratios. Only three of 25 robust biomarkers in the melanocyte-proximal study (i.e. melanocytes vs. melanoma cells) were coincidentally identified in the melanocyte-distal study (i.e. melanocytes vs. non-melanocytic cells). Selected up-regulated biomarkers of melanocytes (i.e. TRP-1, melan-A/MART-1, silver/Pmel17, and nidogen-2) were validated by qRT-PCR. Some of the melanocytic biomarkers identified here may be useful in molecular diagnostics, as potential molecular targets for drug discovery, and for understanding the biochemistry of melanocytic cells.

Aged↗

PubMed2005

Major life stress, coping styles, and social support in relation to psychological distress in patients with systemic lupus erythematosus.

The objective of this study was to examine psychological processes in systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) patients in relation to measures of life stress, coping styles, social support and cognitive ability. Fifty-two SLE patients without overt neuropsychiatric symptoms, 29 RA patients and 27 healthy controls completed measures of depression, mood, disease activity, perceived health, stressful life events, coping, and social support. Variables entered into the multiple regression analysis following principal component analysis were: group, major difficult event, major life threatening event, disengaging coping, emotional coping, social support, and cognitive impairment. Depressive symptoms were associated with SLE group status (P < 0.001), major life-threatening events (P < 0.01), disengage coping (P < 0.001) and emotional coping (P < 0.05). SLE group status (P < 0.05), disengage coping (P < 0.05) and emotional coping (P < 0.05) were associated with current distressed mood. SLE patients without overt, major neuropsychiatric symptoms had greater psychological distress compared to RA and control subjects. Increased depressive symptoms and distressed mood state in SLE patients were related to use of disengaging and emotional coping styles. These findings are limited to SLE patients with no overt neuropsychiatric illness and low disease activity, suggesting the need for future studies with a greater variety of SLE patients. Interventions aimed at improving active coping and minimizing emotional response to stress may lower psychological distress in SLE patients with mild disease.

Adaptation, Psychological↗

PubMed2005

Non-linear PCA: a missing data approach.

MOTIVATION: Visualizing and analysing the potential non-linear structure of a dataset is becoming an important task in molecular biology. This is even more challenging when the data have missing values. RESULTS: Here, we propose an inverse model that performs non-linear principal component analysis (NLPCA) from incomplete datasets. Missing values are ignored while optimizing the model, but can be estimated afterwards. Results are shown for both artificial and experimental datasets. In contrast to linear methods, non-linear methods were able to give better missing value estimations for non-linear structured data. APPLICATION: We applied this technique to a time course of metabolite data from a cold stress experiment on the model plant Arabidopsis thaliana, and could approximate the mapping function from any time point to the metabolite responses. Thus, the inverse NLPCA provides greatly improved information for better understanding the complex response to cold stress. CONTACT: scholz@mpimp-golm.mpg.de.

Adaptation, Physiological↗

PubMed2003

Differential UVC-induced gadd45 gene expression in xeroderma pigmentosum cells.

Xeroderma pigmentosum C (XPC) is a DNA repair factor essential for global genome repair (GGR) in nucleotide excision repair (NER). In the present study we screened for factors regulated by XPC after DNA damage. Ultraviolet C (UVC) irradiation-induced stress response factors were analyzed by a cDNA microarray chip system in HeLa and XP4PA-SV xpc mutant cell lines. The principal component analysis (PCA) method was employed to identify groups of genes with similar expression patterns over time after UVC irradiation. The growth arrest and DNA damage-inducible gene gadd45, as well as a small group of other genes, was found to exhibit an inducible expression pattern after 30min of incubation in xpc mutants but not in HeLa cells. Subsequent studies showed that gadd45 gene expression post-UVC irradiation was also present in the GGR mutant cells xpa and xpd, but not in TCR mutant csb cells. This evidence indicates that gadd45 plays a regulatory role in GGR of NER.

Cell Line↗

PubMed2004

Robust PCA and classification in biosciences.

MOTIVATION: Principal components analysis (PCA) is a very popular dimension reduction technique that is widely used as a first step in the analysis of high-dimensional microarray data. However, the classical approach that is based on the mean and the sample covariance matrix of the data is very sensitive to outliers. Also, classification methods based on this covariance matrix do not give good results in the presence of outlying measurements. RESULTS: First, we propose a robust PCA (ROBPCA) method for high-dimensional data. It combines projection-pursuit ideas with robust estimation of low-dimensional data. We also propose a diagnostic plot to display and classify the outliers. This ROBPCA method is applied to several bio-chemical datasets. In one example, we also apply a robust discriminant method on the scores obtained with ROBPCA. We show that this combination of robust methods leads to better classifications than classical PCA and quadratic discriminant analysis. AVAILABILITY: All the programs are part of the Matlab Toolbox for Robust Calibration, available at http://www.wis.kuleuven.ac.be/stat/robust.html.

Algorithms↗

PubMed2006

The feasibility of gene expression profiling generated in fine-needle aspiration specimens from patients with follicular lymphoma and diffuse large B-cell lymphoma.

Lymphoma of germinal center cell (GC) origin generally is an indolent malignancy that transforms progressively into a more aggressive disease. According to the World Health Organization classification, lymphomas of follicular center cell origin are classified as either large B-cell lymphoma (LBCL) or follicular lymphoma (FL). The authors tested the feasibility of performing gene expression profiling using amplified RNA from fine-needle aspirates (FNA) obtained from lymph nodes. Twenty-four samples from patients with a diagnosis of FL or LBCL were obtained after Institutional Review Board-approved informed consent was obtained. The diagnoses were confirmed by 2 pathologists and were classified into 2 groups (10 LBCL samples and 14 FL samples) by using conventional morphology and immunophenotyping. One hundred nanograms of total RNA were subjected to 2 cycles of standard, double-stranded complementary DNA synthesis and in vitro transcription for target amplification using a small-sample target-labeling protocol. The biotinylated cRNA from each sample was hybridized to gene chips. Gene expression profiling results were analyzed first by principal-component analysis (PCA) by using a list of 146 probe sets that represented 62 genes that are characteristic of an activated B-cell (ABC) signature or a GC signature. The analysis identified 5 LBCL samples with an ABC cell signature. Using a list of 207 probe sets that represented 113 genes involved in FL transformation, PCA analysis identified 2 overlapping clusters corresponding to FL and GC-diffuse LBCL. To improve this classification further, the authors generated a list of 72 genes that were expressed differentially between FL and GC-LBCL. Using this list of genes, PCA analysis demonstrated a clear separation between FL and GC-LBCL. However, five FL samples clustered as an intermediate group between FL and GC-DLBCL. These samples were characterized morphologically by a mixed cell pattern with relatively fewer large, noncleaved lymphocytes and more small, cleaved lymphocytes. The results support the feasibility of FNA-based transcription profiles in patients with FL or LBCL, which, in combination with morphology and immunophenotyping, can help in the subtyping of these entities.

Biopsy, Fine-Needle↗

PubMed2006

Molecular characterization of human adenomyosis.

Adenomyosis is a common gynaecological disorder characterized by the abnormal growth of endometrium into the myometrium and myometrial hypertrophy/hyperplasia. Uterine fibroids are benign neoplasms of the myometrium, and they represent a diagnostic pitfall for adenomyosis. In this study, we have used the genome-wide Affymetrix U133 Plus 2.0 microarray platform to compare the gene expression patterns of adenomyosis, uterine fibroids, normal endometrium and myometrium. Unsupervised principal component analysis (PCA) revealed that these four tissue types could be segregated from one another solely based on their gene expression profiles. Analysis of variance (ANOVA), followed by Tukey means separation test, significance analysis of microarrays (SAM) and 2-fold change threshold, identified 7415 probe sets as differentially expressed among the four groups of samples. Supervised cluster analysis based on these probe sets clustered adenomyosis most closely with endometrium and uterine fibroids with myometrium, consistent with the anatomic origin of these two diseases. The Tukey means separation post hoc testing found 2073 probe sets altered between adenomyosis and normal endometrium or myometrium, and 2327 probe sets altered in expression when comparing uterine fibroids with myometrium. Using Ingenuity Pathways Analysis (IPA), we found 9 highly significant functional networks in adenomyosis and 10 in uterine fibroids. Notably, the top network in both cases was associated with functions implicated in cancer and cell death. Finally, we compared the gene expression profiles of adenomyosis and uterine fibroids and identified 471 differentially expressed probe sets that may represent potential biomarkers for the differential diagnosis of these diseases.

Analysis of Variance↗

PubMed2006

Ischemic and nephrotoxic acute renal failure are distinguished by their broad transcriptomic responses.

Acute renal failure (ARF) has a high morbidity and mortality. In animal ARF models, effective treatments must be administered before or shortly after the insult, limiting their clinical potential. We used microarrays to identify early biomarkers that distinguish ischemic from nephrotoxic ARF or biomarkers that detect both injury types. We compared rat kidney transcriptomes at 2 and 8 h after ischemia/reperfusion and after mercuric chloride. Quality control and statistical analyses were necessary to normalize microarrays from different lots, eliminate outliers, and exclude unaltered genes. Principal component analysis revealed distinct ischemic and nephrotoxic trajectories and clear array groupings. Therefore, we used supervised analysis, t-tests, and fold changes to compile gene lists for each group, exclusive or nonexclusive, alone or in combination. There was little network connectivity, even in the largest group. Some microarray-identified genes were validated by TaqMan assay, ruling out artifacts. Western blotting confirmed that heme oxygenase-1 (HO-1) and activating transcription factor-3 (ATF3) proteins were upregulated; however, unexpectedly, their localization changed within the kidney. HO-1 staining shifted from cortical (early) to outer stripe of the outer medulla (late), primarily in detaching cells, after mercuric chloride but not ischemia/reperfusion. ATF3 staining was similar, but with additional early transient expression in the outer stripe after ischemia/reperfusion. We conclude that microarray-identified genes must be evaluated not only for protein levels but also for anatomical distribution among different zones, nephron segments, or cell types. Although protein detection reagents are limited, microarray data lay a rich foundation to explore biomarkers, therapeutics, and the pathophysiology of ARF.

Activating Transcription Factor 3↗

PubMed2002

Multivariate analyses of morphometrical features from Apatemon gracilis (Rudolphi, 1819) Szidat, 1928 and A.annuligerum (v.Nordman, 1832) (Digenea: Strigeidae) metacercariae.

Only two species of the strigeid subgenus Apatemon (Apatemon) sensu Sudarikov (1959), Apatemon gracilis (Rudolhi, 1819) and A. annuligerum (von Nordmann, 1832), are known to infect British fishes. A. annuligerum is poorly characterised, with few life-cycle data available, since only the adult and metacercarial stages have been described. Both of these life-stages demonstrate only minor morphological differences from those of A. gracilis. The multidimensional approach of principal components analysis (PCA) was employed to determine the intra- and inter-specific morphological variation that exists for metacercariae of these species. Analyses revealed that metacercarial morphometrics cannot confidently discriminate between Apatemon gracilis and A. annuligerum specimens. Indeed, levels of intra-specific variation present between A. gracilis metacercariae originating from different piscine hosts were as marked for certain populations as those between the two nominal species. Individual populations of A. gracilis metacercariae differed most in the relative dimensions of their internal organs, whereas variation between the two nominal species was primarily in length-related variables. A. gracilis specimens originating from Welsh and Scottish stoneloach represented two populations with the most comparable total body lengths, yet clustered separately. Conversely, A. gracilis metacercariae infecting rainbow trout and salmon parr differed markedly in size, but demonstrated minimal composite morphological variation. Much, if not all, of the variation described between populations and species of parasites can be attributed to host or to metacercarial location within a particular host. These findings and the lack of strong life-cycle data for A. annuligerum place some doubt on its validity as a species discrete from A. gracilis.

Animals↗

PubMedDate not supplied

Identification of fat, protein matrix, and water/starch on microscopy images of sausages by a principal component analysis-based segmentation scheme.

A color-based segmentation scheme applied to microscopy images of cryosectioned sausages is proposed. The segmentation scheme is capable of segmenting three different levels on the microscopy images: the fat particles, the protein matrix, and water/starch. The method is based on principal component analysis. A user-friendly program was developed for the manual segmentation of a selection of image pixels by microscopists. Principal component models based on the manually classified pixels are then used to segment fat, protein matrix, and starch/water on microscopy images. The program can also be used as a training tool for microscopists.

Animals↗

PubMed2006

Health indicators and human development in the Arab region.

BACKGROUND: The present paper deals with the relationship between health indicators and human development in the Arab region. Beyond descriptive analysis showing geographic similarities and disparities inter countries, the main purpose is to point out health deficiencies and to propose pragmatic strategies susceptible to improve health conditions and consequently enhance human development in the Arab world. METHODS: Data analysis using Principal Components Analysis is used to compare the achievements of the Arab countries in terms of direct and indirect health indicators. The variables (indicators) are seen to be well represented on the circle of correlation, allowing for interesting interpretation and analysis. The 19 countries are projected on the first and second plane respectively. RESULTS: The results given by the present analysis give a good panorama of the Arab countries with their geographic similarities and disparities. The high correlation between health indicators and human development is well illustrated and consequently, countries are classified by groups having similar human development. The analysis shows clearly how health deficits are impeding human development in the majority of Arab countries and allows us to formulate suggestions to improve health conditions and enhance human development in the Arab World. DISCUSSION: The discussion is based on the link between different direct and indirect health indicators and the relationship between these indicators and human development index. Without including the GDP indicator, our analysis has shown that the 19 Arab countries may be classified, independently of their geographic proximity, in three different groups according to their global human development level (Low, Medium and High). Consequently, while identifying health deficiencies in each group, the focus was made on the countries presenting a high potential of improvement in health indicators. In particular, maternal mortality and infant mortality which are really challenging health authorities of the first and third group were critically discussed. CONCLUSION: The Arab countries have made substantial economic and social progress during the last decades by improving life expectancy and reducing maternal and infant mortality. However, considering its natural wealth and human resources, the Arab region has accomplished less than expected in terms of human development. Huge social inequalities and health inequities exist inter and intra Arab countries. In most Arab countries, a large percentage of populations, especially in rural areas, are deprived of access to health facilities. Consequently, many women still die during pregnancy and labour, yielding unacceptable levels of maternal and infant mortality. However, the problem is seen to be more complex, going beyond geography and technical accessibility to health care, it compasses, among others, levels of literacy, low social and economic status of women, qualification of health staff, general behaviour and interactions between patients and medical personnel (including corruption).

Arab World↗

PubMed2004

Caveolins as tumour markers in lung cancer detected by combined use of cDNA and tissue microarrays.

To identify new potential diagnostic markers for lung cancer, the expression profiles of 37 lung tumours were analysed using cDNA arrays. Seven samples were from small-cell lung cancer (SCLC), two from large-cell neuroendocrine tumours (LCNEC), and 28 from other non-small-cell lung cancers (mainly squamous cell cancer and adenocarcinoma). Principal component analysis and the permutation test were used to detect differences in the gene expression profiles and a set of genes was found that distinguished high-grade neuroendocrine carcinomas (SCLC and LCNEC) from other lung cancers. In addition, several genes, such as caveolin-1 (CAV1) and caveolin-2 (CAV2), were constantly deregulated in all types of tumour sample, compared with normal tissue. The expression of these two genes was investigated further at the protein level on a tissue microarray containing tumours from 161 patients and normal tissues. Immunostaining for CAV1 was negative in 48% of tumours, whereas 28% of the tumours did not express CAV2. Lack of CAV1 protein expression was not caused by methylation or mutation. In stage I adenocarcinomas, CAV2 protein expression correlated with shorter survival. In conclusion, the present study was able to identify genes that have not previously been implicated in lung cancer by the combined use of two different array techniques. Some of these genes may provide novel diagnostic markers for lung cancer.

Adenocarcinoma↗

PubMed2006

Multivariate curve resolution of time course microarray data.

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.

Algorithms↗

PubMed2006

A novel data mining method to identify assay-specific signatures in functional genomic studies.

BACKGROUND: The highly dimensional data produced by functional genomic (FG) studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays). Although dimensionality reduction methods such as principal component analysis (PCA) have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies. RESULTS: The proposed method (PM) is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data--a study of classification of human tissue samples and a study of E. coli cultures with different medium formulations. CONCLUSION: We have developed a PCA-based method that effectively identifies assay-specific signatures in ranked groups of genes from the full data set in a more efficient and simplistic procedure than current approaches. Although this work demonstrates the ability of the PM to identify assay-specific signatures in DNA microarray experiments, this approach could be useful in areas such as proteomics and metabolomics.

Algorithms↗

PubMed1990

Classification of glucose-6-phosphate dehydrogenase variants by multivariate statistical analysis.

Using principal component analysis a two-dimensional presentation of kinetic parameters from various G6PD-variants was obtained from which similarities between individual enzymes became detectable. Conclusions could be drawn as to how far the kinetic parameters contribute to the discrimination between different variants. An objective classification of G6PD-variants was achieved by application of cluster analysis. The proposed methods provide an effective means for differential elucidation of G6PD-variants and other heterogeneous enzymopathies.

Cluster Analysis↗

PubMed2002

Principal component similarity analysis of Raman spectra to study the effects of pH, heating, and kappa-carrageenan on whey protein structure.

Raman spectroscopy was used to elucidate structural changes of beta-lactoglobulin (BLG), whey protein isolate (WPI), and bovine serum albumin (BSA), at 15% concentration, as a function of pH (5.0, 7.0, and 9.0), heating (80 degrees C, 30 min), and presence of 0.24% kappa-carrageenan. Three data-processing techniques were used to assist in identifying significant changes in Raman spectral data. Analysis of variance showed that of 12 characteristics examined in the Raman spectra, only a few were significantly affected by pH, heating, kappa-carrageenan, and their interactions. These included amide I (1658 cm(-1)) for WPI and BLG, alpha-helix for BLG and BSA, beta-sheet for BSA, CH stretching (2880 cm(-1)) for BLG and BSA, and CH stretching (2930 cm(-1)) for BSA. Principal component analysis reduced dimensionality of the characteristics. Heating and its interaction with kappa-carrageenan were identified as the most influential in overall structure of the whey proteins, using principal component similarity analysis.

Carrageenan↗

PubMed2002

Influence of physico-chemical parameters of some barbituric acid derivatives on their retention on an amide embedded RP silica column.

Retention parameters of 45 different barbituric acid derivatives were determined on an amide embedded RP silica column (Discovery RP-AmideC16) using non-buffered water-acetonitrile eluent systems. Linear correlation were calculated between the logarithm of the capacity factor and the acetonitrile concentration in the eluent. To determine the retention behavior of barbituric acid derivatives, stepwise regression analysis (SRA) and principal component analysis (PCA) followed by two-dimensional nonlinear and modified nonlinear mapping was used. It can be concluded, the retention of barbituric acid derivatives are governed mainly by the steric parameters of the substituents. Principal component analysis indicated that the barbituric acid derivatives have mixed retention on this amide embedded RP silica column in water-acetonitrile eluent.

Barbiturates↗

PubMed2000

Osteometrical skull character in the four species of tree shrew.

The skull size and shape were osteometrically examined in the four species of the tree shrews (Tupaia tana, T. javanica, T. minor and T. dorsalis). We suggest that the skull characters were affected by the species specific behavior and terrestrial, arbo-terrestrial and arboreal life, among the genus Tupaia. The neurocranium was laterally narrower in the braincase area, and the splanchnocranium was longer only on dorsal side in T. tana, and these characters were opposite to T. minor. The principal component analysis confirmed the obviously separated clusters among T. tana, T. javanica and T. minor, affected by the adaptation for each behavior. T. dorsalis was considered as terrestrial species from the results of proportion analysis and the principal component analysis.

Animals↗