Top PDF Density based pruning for identification of differentially expressed genes from microarray data

Density based pruning for identification of differentially expressed genes from microarray data

Density based pruning for identification of differentially expressed genes from microarray data

Cyberinfrastructure, OCI and NSF/ICI senior science advisor; former Director of ASCI); Dr. Sandeep Chatterjee (Co-founder, Chief Technology Officer and Vice President, SourceTrace Systems, Inc.); Dr. Rahul Razdan (CEO, Raztech LLC); Prof. P. M. A. Sloot (Director of the Institute for Informatics, University of Amsterdam, The Netherlands and Editor-in-Chief, Future Generation Computer Systems, Elsevier); Prof. Michael Bank (Holon Institute of Technology, Israel and inventor of Frequency Bank Signal, FBS); and the 2009 International Conference on Bioinformatics and Computational Biology Keynote Speakers Dr. Brian D. Athey (Professor of Biomedical Informatics, University of Michigan Medical School, USA; Head, NIH National Center for Integrative Biomedical Informatics; Director, Biomedical Informatics Program of Michigan Institute for Clinical & Health Research); and Dr. Yi Pan (Chair and Professor, Department of CS, Georgia State University, Atlanta, USA). We would also like to thank the followings: UCMSS (Universal Conference Management Systems & Support, California, USA) for managing all aspects of the conference; Dr. Tim Field of APC for managing the printing of the proceedings; and the staff of Monte Carlo Resort in Las Vegas for the professional service they provided. Last but not least, we would like to thank the Associate Editors, Drs. Youping Deng, Chien-Tsai Liu, Ashu M. G. Solo, and Yanqing Zhang. We present the proceedings of BIOCOMP ‟09.
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Spotlight on differentially expressed genes in urinary bladder cancer.

Spotlight on differentially expressed genes in urinary bladder cancer.

Oligos microarray chips ( ,57K genes) were obtained from GE HealthCare (IL) and AppliedMicroarrays (MA) (CodeLink 57K Human Whole Genome). Hybridization was performed with the CodeLink RNA amplification and Labeling kit, utilizing the Cy5 fluorescent dye. Slides were scanned with a microarray scanner (ScanArray 4000XL). Images were generated with ScanArray microarray acquisition software (GSI Lumonics, USA). cRNAs from three experimental setups were used in single experiments with internal spikes as controls. The experimental setups consisted of 10 urinary BC samples of different histologies (T1/2-grade 3, T1-grade 1/2, T3-grade 3) and 5 control samples. The scanned images were further processed with the CodeLink Expression Analysis Software v5.0 from Amersham Biosciences. The experimental setup was analyzed based on the reference design as described previously [9,10,11]. All tumor samples were compared against the mean value of the control samples. Background correction was performed by subtracting the median global background from the median local background from the signal intensity. A threshold of 2 was set as cut-off, meaning that spot intensity for at least one channel should be twice as much as that of the background. Microarray data were normalized by dividing spot intensities by the global median. Normalized data were extracted, pre-processed and sorted with Microsoft ExcelH. Array data are available at the Gene Expression Omnibus (National Center for Biotechnology Information) with accession numbers GSM678186 through GSM678385 (http://www.ncbi. nlm.nih.gov/geo/query/acc.cgi?acc=GSE27448). Furthermore, each gene was tested for its significance in differential expression using a z-test. Genes were considered to be significantly differentially expressed if they obtained a p-value ,0.05. The False Discovery Rate was calculated as described previously [12,13,14]. Genes were further classified using two-way (genes- against samples) average-linkage hierarchical clustering with Euclidian distance using the Genesis 1.7.2 software (Technische Universitaet-Graz, Austria) [15].
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Resistance of renal cell carcinoma to sorafenib is mediated by potentially reversible gene expression.

Resistance of renal cell carcinoma to sorafenib is mediated by potentially reversible gene expression.

Figure 2. Transcriptional profiling analysis of sorafenib treated tumors. A) Unsupervised Pearson Correlation based cluster of untreated tumors harvested at 12 mm (A), at treatment day 3 (C), treatment resistant (tumors that had grown to 20 mm sacrifice size despite continued treatment) (D), and reimplanted untreated tumors after one and two reimplantations harvested at 12 mm (A1 and A2) after normalizing the data. The treated tumors (D,C) form a separate cluster from control and reimplanted tumors. B) Cologram depicting the different expression patterns of the genes that are either differentially expressed in untreated vs. resistant or reimplanted vs. resistant tumors. The columns represent the samples and rows represent the genes. Gene expression is shown with pseudocolor scale (23 to 3) with red denoting high expression level and green denoting low expression level of gene. The Genes depict five major expression patterns (marked I to V). C) Venn diagram indicating overlap between differentially expressed genes untreated vs resistant, untreated vs responsive, untreated vs. reimplanted and reimplanted vs. resistant. The differentially expressed genes are extracted using Significantly Analysis of Microarray data (SAM) approach. The green circle shows the 626 transcripts are changed in resistant tumors as compared to untreated tumors. The overlap of the green and pink circles shows the 120 genes that are also differentially expressed at day 3 of therapy. The blue circle shows the 555 transcripts that are differentially expressed in reimplanted as compared to resistant tumors and the overlap of the blue and pink circles show the 70 of the 553 that are altered at day 3 (Responsive). 168 genes are commonly differentially expressed in the untreated and reimplanted tumors as compared to resistant tumors and not changed at day 3 of therapy and are circled. D) Validation of 6 resistance related genes was conducted using untreated, resistant and day3 treatment samples (n = 3 per group). The graphs represent the statistical analyzes of relative mRNA levels after normalization for 18S rRNA levels. The results are expressed using floating bars representing the minimum and maximum values in the group with a line representing the mean. (* P,0.05, ** P,0.001, by Unpaired Student’s T test) ANGPTL4, MMP1, SERPINE1 and NRP2 were significantly upregulated at resistance but not at day 3 in the gene expression profiling. ARG2 and INSIG1 were increased at day 3 and then decreased at resistance. The PCR showed results similar to transcriptional profiling.
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Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis.

Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis.

The raw expression intensity values (RNA.cel files) were filtered for low- or non-specific hybridization using the custom generated CDF files from the previous step and then normal- ized using the RMA algorithm present in “affy” package of BioConductor in an R environment [26]. To further reduce the noise in the normalized data, only the probe-sets present (P) in all the array slides (total 9 slides) were selected. Differentially expressed genes (DEGs) were calcu- lated for each threshold value (0–500) using a t-test and then corrected by using False Discov- ery Rates (FDR) for multiple hypothesis testing [27]. To consider a probe-set as differentially expressed, it should contain <0.01 corrected p-value with a >2 fold-change (either up- or down-regulated). Based on this criteria, the CDF file of 100 threshold values was considered as a best threshold value (Table 1) as it returned highest number of DEGs (375 in optimal vs. sub- optimal temperature and 659 in optimal vs. very sub-optimal temperature). Out of 375 DEGs, 204 genes are up-regulated while 171 genes are down-regulated under the sub-optimal temper- ature (S1 Table). On the other hand, out of 659 DEGs, 403 genes are up-regulated while 257 genes are down-regulated in the very sub-optimal temperature (S2 Table). There were 110 DEGs which are common between sub-optimal and very sub-optimal temperatures suggesting that a similar genetic response is likely to underlie the response of bambara groundnut to these two different temperatures. Out of these 110 common DEGs, 76 genes are up-regulated while 34 genes are down-regulated (S3 Table).
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High-resolution transcriptome of human macrophages.

High-resolution transcriptome of human macrophages.

To understand if RNA-seq would also enhance the un- derstanding of biological principles of macrophage polarization we applied network analysis based on a priori information assessing the information content of RNA-seq data in compar- ison to array data. Genes expressed at elevated levels in M1 RNA-seq data (FC .4) were used for network generation (Fig. 5A). This primary RNA-seq based M1 network was subsequently used to visualize array-based gene expression (Fig. 5B). When genes at a lower level of differential expression (FC .2) were included 73% of the network was revealed in the array data and central hubs of the network were also categorized as being highly (FC .4) differentially expressed. However, only RNA-seq data revealed two gene clusters of immunomodulating proteins highly enriched in the M1 network, namely apolipo- proteins L (APOL) (Fig. 5A and Figure S9) and the leukocyte immunoglobulin-like receptor (LILR) family (Fig. 5A and Figure S10) [34–36]. As exemplified for LILRB1 and APOL3 both genes were clearly identified by RNA-seq, qRT-PCR, and flow cytometry respective western blotting (Fig. 5E and F) but not by microarray analysis (data not shown). Applying the RNA-seq data-based M2 network (Fig. 5C) to the array data (Fig. 5D) revealed only 54% elevated genes and major network hubs were Figure 5. Network analysis of RNA-seq data. (A) Network of genes highly expressed in M1-like macrophages (fold-change .4.0) identified by RNA-seq. (B) Data generated by microarray analysis were loaded into the M1-network established using RNA-seq. (C) Network of genes highly expressed in M2-like macrophages (fold-change .2.5) identified by RNA-seq. (D) Data generated by microarray analysis were loaded into the M2- network established using RNA-seq. All networks were generated using EGAN. (E) APOL3 and (F) LILRB1 expression in human M1- and M2-like macrophages. Far left, relative expression as determined by RNA-seq. Left, representative images of sequencing reads across genes expressed in human macrophages as described in Fig. 4. Right, relative mRNA expression by qPCR in M1- and M2-like macrophages. Far right, protein data as determined by immunoblotting, respective flow cytometry. Data are representative of three experiments (RNA-seq, qPCR, and immunoblotting resp. flow cytometry; mean and s.e.m.) each with cells derived from a different donor. Isotype controls are depicted as dotted lines. *P,0.05 (Student’s t- test).
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Network Analysis for the Identification of Differentially Expressed Hub Genes Using Myogenin Knock-down Muscle Satellite Cells.

Network Analysis for the Identification of Differentially Expressed Hub Genes Using Myogenin Knock-down Muscle Satellite Cells.

MSC progeny can be distinguished from their quiescent progenitors based on distinctive gene expression patterns. In adults, MSCs cycle through the steps of embryonic myogenesis to either add to or replace current muscle fibers [15–19]. Unlike the enigmatic status of genes that perform important functions in bovines, expression of a large number of genes (particularly those corresponding to different transcription factors) has been observed in mouse MSCs [20]. Therefore, it is important to delineate the expression profile of genes with unknown function in bovine-derived MSCs. Our interests in obtaining the regulatory profile of genes with impor- tant functions in mouse MSCs led us to perform the current investigation with bovine MSCs to have a clear understanding of bovine muscle development. By employing microarray, expressed sequence tag (EST) followed by RNA-Seq techniques to MSCs satellite cell analysis, we were able to delineate the regulatory network of genes corresponding to different transcrip- tion factors and certain prominent members of the extracellular protein family, involved in controlling myoblast differentiation [20, 21]. We were able to elucidate and assign specific roles to certain genes, such as transthyretin, that are novel with respect to their involvement in myogenesis [22]. While investigating the gene expression profile of MYOG knock-down (MYOG kd ) in bovine MSCs using RNA-Seq, we observed differential expression patterns of
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Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

Gene expression profiles in both CSF cells and PBMCs were obtained from the ArrayExpress Database under the accession number of EMTAB- 69 based on the Human Genome 133 plus 2.0 arrays (Brynedal et al., 2010). Accordingly, this study consisted of 26 multiple sclerosis patients, of whom 12 and 14 patients were sampled during relapse and remission, respectively. The MS patients were selected from a large cohort of newly diagnosed MS patients, and none of the patients had ever received immunomodulatory drugs. Control population included 18 subjects with other neurological diseases to assess MS specific transcriptome. The microarray raw data were converted to gene expression values using the RMA algorithm by the affy package within R software (Gautier et al., 2004). After preprocessing, each expression profile containing 54, 675 probe sets that ones with less discriminative power were removed according to the measurement of overall variance by the varFilter function using the genefilter package from the Bioconductor project within R software (Gentleman et al., 2011). After the preprocessing, a total of 27,336 probe sets from each sample were used for further analysis. To identify differential expression of the selected probes, the limma package in R software was used to perform the moderated t -test (Smyth, 2005). Where a gene had more than one probe on the microarray, the average expression value of all the related probes was used to estimate expression level of the gene. Interactome data
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Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.

Results: Microarray based gene expression data and protein-protein interaction (PPI) databases were combined to construct the PPI networks of differentially expressed (DE) genes in post mortem brain tissue samples of patients with Parkinson’s disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM), run separately to construct two Query-Query PPI (QQPPI) networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs) and High Betweenness Low Connectivity (bottlenecks) were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub- networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS) out of the 37 markers were found to be associated with several neurotransmitters including dopamine.
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Transcription factors and microRNA-co-regulated genes in gastric cancer invasion in ex vivo.

Transcription factors and microRNA-co-regulated genes in gastric cancer invasion in ex vivo.

Aberrant miRNA expression abnormally modulates gene expression in cells and can con- tribute to tumorigenesis in humans. This study identified functionally relevant differentially expressed genes using the transcription factors and miRNA-co-regulated network analysis for gastric cancer. The TF-miRNA co-regulatory network was constructed based on data ob- tained from cDNA microarray and miRNA expression profiling of gastric cancer tissues. The network along with their co-regulated genes was analyzed using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Transcriptional Regulatory Element Database (TRED). We found eighteen (17 up-regulated and 1 down-regulated) differentially expressed genes that were co-regulated by transcription factors and miRNAs. KEGG path- way analysis revealed that these genes were part of the extracellular matrix-receptor inter- action and focal adhesion signaling pathways. In addition, qRT- PCR and Western blot data showed an increase in COL1A1 and decrease in NCAM1 mRNA and protein levels in gas- tric cancer tissues. Thus, these data provided the first evidence to illustrate that altered gene network was associated with gastric cancer invasion. Further study with a large sam- ple size and more functional experiments is needed to confirm these data and contribute to diagnostic and treatment strategies for gastric cancer.
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Genes and (common) pathways underlying drug addiction.

Genes and (common) pathways underlying drug addiction.

Common Molecular Pathways for Drug Addiction Because we collected metadata about each item of evidence linking genes to addiction, in particular the nature of the addictive substance, we could ask next what are the pathways underlying addiction to each type of substance, and what are the common pathways among them. We identified five pathways shared by all four addictive substances (Table 2). Three of the pathways had been linked to addictive behaviors in previous studies and were statistically confirmed here. For example, ‘‘long-term potentiation’’ had been linked to addiction-induced adaptations in glutamatergic transmission and synaptic plasticity [21]. In particular, a core component of this pathway, CAMKII, had been reported to regulate neurite extension and synapse formation through regulation of the actin cytoskeleton [22], providing possible explanations for morphological changes triggered by addictive drugs [17]. This pathway was also considered a key molecular circuit underling the memory system, highlighting the possible shared mechanisms between drug addiction and the learning and memory system [23]. ‘‘MAPK signaling pathway’’ is another example, as previous studies had suggested its roles in regulating synaptic plasticity related to long-lasting changes in both memory function and addictive properties [24].
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Suppression subtractive hybridization reveals transcript profiling of Chlorella under heterotrophy to photoautotrophy transition.

Suppression subtractive hybridization reveals transcript profiling of Chlorella under heterotrophy to photoautotrophy transition.

chlorophyll a-b binding protein, plastocyanin oxidoreductase iron–sulfur protein, and ribulose-1,5-bisphosphate carboxylase oxygenase large/small subunit. These proteins suggest the robust ability of Chlorella to restart photosynthetic function to protect heterotrophically grown cells from photodamage under sudden light stress. Likewise, the upregulation of these proteins in response to light stress has also been reported in higher plants and algae [37,38]. By contrast, the heterotrophic cells were expectedly deprived of photosynthetic performance; thus, few genes related to photosynthetic pathways were found in the reverse library. However, two special cases were found, namely, the antenna proteins of light-harvesting complexes (RYG131 and RYG034), which showed enhanced expression in response to heterotrophy. Plant and green algal light-harvesting complexes are composed of more than 20 different antenna proteins associated with photo- systems I and II [39]. Although the function of antenna proteins in darkness-grown cells was not explored, the relatively increased expression indicates their importance in heterotrophic Chlorella. In unicellular green algae Dunaliella tertiolecta, an increase in the relative abundance of chlorophyll a/b light-harvesting complex mRNA was also found following a shift from high light to darkness and from high light to low light [40]. LaRoche et al proposed that changes in energy balance brought about by a change in light intensity may control a regulatory factor acting to repress chlorophyll a/b binding protein mRNA expression [40]. Further- more, a unigene that encodes glutamate dehydrogenase (GDH), was also found in the forward library. GDH is an important intermediate enzyme between carbon and nitrogen metabolism and it functions in multiple directions, depending on the environment and the stress [41,42]. In transgenic plants, the overexpression of microbial GDH reportedly confers improved tolerance to herbicides, drought, and pathogenic infections [43]. Figure 3. Gene Ontology (GO) annotation of genes obtained from the SSH libraries. GO predictions identified several categories based on the three terms cellular component, molecular function, and biological process, and were plotted by WEGO. LI represents forward library under the light-induced treatment; HC represents the reverse library under the heterotrophic culture process.
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Epigenetic biomarkers as predictors of clinical outcomes in colorectal cancer

Epigenetic biomarkers as predictors of clinical outcomes in colorectal cancer

xii Em adição, cada um destes locais de metilação encontra-se correlacionado com os respetivos genes encontrados diferencialmente expressos no mesmo estadio (p-value < 0.05). De seguida, efetuou-se uma análise nas bases de dados KEGG e Gene Ontology (GO). A utilização destas ferramentas revelou que as funções mais enriquecidas estão relacionadas com o sistema nervoso. Estudos anteriores já tinham descrito alterações em genes envolvidos no desenvolvimento e regulação do sistema nervoso como desreguladas em diversos tipos de cancro. Em adição, foi ainda realizada uma análise com o objetivo de encontrar quais dos genes encontrados diferencialmente expressos e que continham locais de metilação diferencialmente metilados ainda não tinham sido reportados em associação com cancro colorretal e cancro em geral. Esta análise sugere que 87 genes nunca foram associados nem com cancro colorretal nem com cancro no geral. Em oposição, 511 já forma reportados em algum tipo de cancro. Destes últimos, 278 já foram também reportados em cancro colorretal enquanto 233 nunca foram descritos neste tipo de cancro.
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Gene Expression Differences in Peripheral Blood of Parkinson ’s Disease Patients with Distinct Progression Profiles

Gene Expression Differences in Peripheral Blood of Parkinson ’s Disease Patients with Distinct Progression Profiles

diagnosis of PD according to the UK Brain Bank criteria for idiopathic PD [13] and the pres- ence of motor symptoms for 10 years or longer. This was selected as a cut-off point to perform a cross-sectional evaluation and separate patients into two distinct groups with “Slow” or “Fast” progression, depending on the absence or presence of postural instability, respectively. Postural instability was chosen to discriminate groups because, as an axial symptom, it evolves more rapidly than other motor features and is associated with poorer quality of life [5]. It is present in around three quarters of patients at 10 years of disease duration [14, 15] and, although heterogeneous in time to axial symptoms, patients with postural instability at 10 years represent a group that arrived faster to a level of higher disease burden as opposed to a group without postural instability at this time. Postural instability was defined by item 3.12 of the MDS-UPDRS, part III. Slow progression if scored 0 (no postural instability) and rapid pro- gression if scored  1. The selection of this operational criterion was based on the consensual recognition that the onset of balance problems is associated with the risk of falls, which corre- sponds to a clear disability milestone in PD progression [16]. In addition, it is consensually accepted that the absence of postural instability after 10 or more years of symptom onset corre- sponds to a more benign disability profile that is associated with a slower progression of clinical factors related with loss of autonomy [15]. Our study design focused on the evaluation of gene expression differences in slow versus rapid progression patients and, therefore, we did not include clinically healthy subjects. A structured interview was used to obtain detailed informa- tion on PD history, family history of PD, antiparkinsonian treatment, comorbidities and con- comitant medication. PD was assessed using the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)[17] and the modified Hoehn and Yahr scales (mHY), the Mini-Mental State Examination and the Schwab and England activities of daily liv- ing scale (SE). If motor fluctuations were present, patients were assessed during Best-on period. Included patients (n = 70) were operationally divided into the two groups: slow (n = 35) or rapid (n = 35) progression according to their postural stability. Descriptive analysis was per- formed using means ± SD values for continuous variables, and absolute and relative frequen- cies for categorical variables. Statistical comparisons were performed using T-test for independent samples for continuous variables and Chi-square test for categorical variables, using R (version 3.0.3) and statistical significance set to p<0.05.
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Long noncoding RNA-EBIC promotes tumor cell invasion by binding to EZH2 and repressing E-cadherin in cervical cancer.

Long noncoding RNA-EBIC promotes tumor cell invasion by binding to EZH2 and repressing E-cadherin in cervical cancer.

Briefly, five CC tissues and five paired peritumoral tissues (Table S1) were used to synthesize double-stranded complementary DNA (cDNA) by reverse-transcription polymerase chain reaction. Dou- ble-stranded cDNA was hybridized to Glue Grant Human Transcriptome arrays (Affymetrix, USA) according to manufactur- er’s protocol, and AffymetrixH Expression Console Software (version 1.3.1) was used for microarray analysis. Raw data (CEL files) were normalized at the transcript level using the robust multi- average method (RMA workflow). Median summarization of transcript expression was calculated. Gene-level data represented genes found in the Rfamdb, fRNAdb, Ensembl, Noncodedb, and RefSeq databases. Using the same method, exon-level data represented full-length transcripts. The random variance model (RVM) t-test was used to identify differentially expressed genes between the CC and peritumoral groups, without increasing the rate of false positives [19]. We selected differentially expressed genes according to RVM and false discovery rate (FDR) analyses, with a predefined P-value threshold of ,0.05 [20]. Hierarchical clustering (Cluster3.0) and TreeView analysis (Stanford University, USA) were performed based on the results of differentially expressed genes. The microarray data discussed in this article have been submitted to National Center for Biotechnology Information (NCBI) Gene
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An extension of PPLS-DA for classification and comparison to ordinary PLS-DA.

An extension of PPLS-DA for classification and comparison to ordinary PLS-DA.

The development of PPLS-DA followed the development of powered partial least squares as a natural extension of the power methodology to handle discrete responses. Several factors moti- vated this advancement to PLS-DA. First the application of powers enables focusing on fewer explanatory variables in the loading weights, smoothing over some of the noise in the remaining variables. Second, focus can be shifted between the correlation and standard deviation parts of the loading weights, which is even more important for discrete responses. Finally, the maximization criterion is moved from the between-group varia- tion (B) to the product of the between group variation matrix and the inverse of the within-group variation matrix (W {1 B~T {1 B). This has the effect of moving from covariance maximization to a correlation maximization. Instead of just searching for the space having highest variation between the groups, we also minimize the variation inside the groups, increasing the likeliness of good group separation.
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Identification of differentially expressed proteins from Leishmania amazonensis associated with the loss of virulence of the parasites.

Identification of differentially expressed proteins from Leishmania amazonensis associated with the loss of virulence of the parasites.

Leishmania amazonensis can induce a diversity of clinical manifestations in mammal hosts, including tegumentary and visceral leishmaniasis. The present study evaluated the variation of infectivity of L. amazonensis, which was pre- isolated from lesions of chronically infected mice and in vitro cultured for 150 days, in turn connecting these results with the profile of parasite protein expression using a proteomic approach. Parasites were recovered after the first passage, as well as after 50, 100, and 150 days of axenic cultures, and were subsequently evaluated. A total of 37 proteins presented a significant decrease, whereas 19 proteins presented a significant increase in their protein expression content in the assays (both cases .2.0 fold). Some of the identified proteins have been reported in prior literature, including diagnosis and/or vaccine candi- dates for leishmaniasis, while others proved to be involved in the infectivity of Leishmania. It is interesting to note that proteins related to the parasites’ metabolism were also the majority of the proteins identified in the old cultures of L. amazonensis, suggesting a possible relation between the metabolic state of parasites and their possible loss of infectivity. In conclusion, the proteins identified in this study represent a contribution to the discovery of new vaccine candidates and/or immunotherapeutic targets against leishmaniasis.
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Identifying stably expressed genes from multiple RNA-Seq data sets

Identifying stably expressed genes from multiple RNA-Seq data sets

A reference set of stably-expressed genes will be useful for count normalization. A key task of RNA-Seq analysis is to detect DE genes under various experimental or environmental conditions. Count normalization is needed to adjust for differences in sequencing depths or library sizes (total numbers of mapped reads for each biological sample) due to chance variation in sample preparation. In DE analysis, gene expression levels are often estimated from relative read frequencies. For this reason, normalization is also needed to account for the fact that non-differentially expressing genes may exhibit an apparent reduction or increase in relative read frequencies due to the respective increased or decreased relative read frequencies of truly differentially expressing genes. Many existing normalization methods, such as the trimmed mean of M -values normalization method (TMM) (Robinson & Oshlack, 2010) and Anders and Huber’s normalization (Anders & Huber, 2010), assume that the majority of the genes within an experiment are not DE, and examine the sample distribution of the fold changes between samples. If the experiment condition can affect expression levels of more than half of the genes, many of the existing normalization methods may be unreliable (Lovén et al., 2012; Wu et al., 2013). This difficulty could be alleviated if one could identify a set of stably expressed genes whose expression levels are known or expected to not vary much under different experimental conditions. Our idea is to identify such a reference set based on a large number of existing data sets.
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Evaluation of gene selection metrics for tumor cell classification

Evaluation of gene selection metrics for tumor cell classification

In spite of the method employed to acquire the gene expression data, their analysis involves several aspects that have been addressed in the literature (Brazma and Vilo, 2000; Dopazo et al., 2001). One of these aspects is gene se- lection. Gene expression profiles present the expression level of thousands of genes. Depending on the issue under investigation, this large amount of data makes the analysis impractical. Besides, and more importantly, large changes in a particular phenotype can be due to changes in the ex- pression of a small subset of its genes. Thus, it is important to select subsets of relevant genes to work with. In sum- mary, the interest in a small set of genes can be motivated by financial, personal workload or experimental reasons. Gene selection gives the biologists a small set of genes to make more specific, complex and usually expensive inves- tigations.
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Gene expression pro filing analysis of lung adenocarcinoma

Gene expression pro filing analysis of lung adenocarcinoma

The present study screened potential genes related to lung adenocarcinoma, with the aim of further understanding disease pathogenesis. The GSE2514 dataset including 20 lung adenocarcinoma and 19 adjacent normal tissue samples from 10 patients with lung adenocarcinoma aged 45-73 years was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the two groups were screened using the t-test. Potential gene functions were predicted using functional and pathway enrichment analysis, and protein-protein interaction (PPI) networks obtained from the STRING database were constructed with Cytoscape. Module analysis of PPI networks was performed through MCODE in Cytoscape. In total, 535 upregulated and 465 downregulated DEGs were identified. These included ATP5D, UQCRC2, UQCR11 and genes encoding nicotinamide adenine dinucleotide (NADH), which are mainly associated with mitochondrial ATP synthesis coupled electron transport, and which were enriched in the oxidative phosphorylation pathway. Other DEGs were associated with DNA replication (PRIM1, MCM3, and RNASEH2A), cell surface receptor-linked signal transduction and the enzyme-linked receptor protein signaling pathway (MAPK1, STAT3, RAF1, and JAK1), and regulation of the cytoskeleton and phosphatidylinositol signaling system (PIP5K1B, PIP5K1C, and PIP4K2B). Our findings suggest that DEGs encoding subunits of NADH, PRIM1, MCM3, MAPK1, STAT3, RAF1, and JAK1 might be associated with the development of lung adenocarcinoma.
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