@article{Johnson2007,
abstract = {Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.},
author = {Johnson, W Evan and Li, Cheng and Rabinovic, Ariel},
doi = {10.1093/biostatistics/kxj037},
file = {:C$\backslash$:/Users/khadija/Documents/Masterarbeit/Materials/Biostat-2006-Johnson-biostatistics\_kxj037.pdf:pdf},
issn = {1465-4644},
journal = {Biostatistics (Oxford, England)},
keywords = {Bayes Theorem,Data Interpretation, Statistical,Gene Expression Profiling,Gene Expression Profiling: methods,Humans,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods},
month = jan,
number = {1},
pages = {118--27},
pmid = {16632515},
title = {{Adjusting batch effects in microarray expression data using empirical Bayes methods.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16632515},
volume = {8},
year = {2007}
}
@article{Roth2006a,
abstract = {Transcriptional profiling was performed to survey the global expression patterns of 20 anatomically distinct sites of the human central nervous system (CNS). Forty-five non-CNS tissues were also profiled to allow for comparative analyses. Using principal component analysis and hierarchical clustering, we were able to show that the expression patterns of the 20 CNS sites profiled were significantly different from all non-CNS tissues and were also similar to one another, indicating an underlying common expression signature. By focusing our analyses on the 20 sites of the CNS, we were able to show that these 20 sites could be segregated into discrete groups with underlying similarities in anatomical structure and, in many cases, functional activity. These findings suggest that gene expression data can help define CNS function at the molecular level. We have identified subsets of genes with the following patterns of expression: (1) across the CNS, suggesting homeostatic/housekeeping function; (2) in subsets of functionally related sites of the CNS identified by our unsupervised learning analyses; and (3) in single sites within the CNS, indicating their participation in distinct site-specific functions. By performing network analyses on these gene sets, we identified many pathways that are upregulated in particular sites of the CNS, some of which were previously described in the literature, validating both our dataset and approach. In summary, we have generated a database of gene expression that can be used to gain valuable insight into the molecular characterization of functions carried out by different sites of the human CNS.},
author = {Roth, Richard B and Hevezi, Peter and Lee, Jerry and Willhite, Dorian and Lechner, Sandra M and Foster, Alan C and Zlotnik, Albert},
doi = {10.1007/s10048-006-0032-6},
issn = {1364-6745},
journal = {Neurogenetics},
keywords = {Adult,Central Nervous System,Central Nervous System: anatomy \& histology,Central Nervous System: chemistry,Central Nervous System: physiology,Cluster Analysis,Female,Gene Expression Profiling,Genome, Human,Humans,Male,Middle Aged,Oligonucleotide Array Sequence Analysis,Reproducibility of Results},
mendeley-groups = {Phd-Papers},
month = may,
number = {2},
pages = {67--80},
pmid = {16572319},
title = {{Gene expression analyses reveal molecular relationships among 20 regions of the human CNS.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16572319},
volume = {7},
year = {2006}
}
@article{Su2004a,
abstract = {The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.},
author = {Su, Andrew I and Wiltshire, Tim and Batalov, Serge and Lapp, Hilmar and Ching, Keith A and Block, David and Zhang, Jie and Soden, Richard and Hayakawa, Mimi and Kreiman, Gabriel and Cooke, Michael P and Walker, John R and Hogenesch, John B},
doi = {10.1073/pnas.0400782101},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Animals,Female,Genomic Imprinting,Humans,Male,Mice,RNA, Messenger,RNA, Messenger: genetics,Transcription, Genetic},
mendeley-groups = {Phd-Papers,MGFM-vignette},
month = apr,
number = {16},
pages = {6062--7},
pmid = {15075390},
title = {{A gene atlas of the mouse and human protein-encoding transcriptomes.}},
url = {http://www.pnas.org/content/101/16/6062.abstract},
volume = {101},
year = {2004}
}
@article{Ge2005a,
abstract = {A critical and difficult part of studying cancer with DNA microarrays is data interpretation. Besides the need for data analysis algorithms, integration of additional information about genes might be useful. We performed genome-wide expression profiling of 36 types of normal human tissues and identified 2503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by reanalyzing a large collection of published DNA microarray datasets. We observed that the expression level of liver-specific genes in hepatocellular carcinoma (HCC) correlates with the clinically defined degree of tumor differentiation. Through unsupervised clustering of tissue-specific genes differentially expressed in tumors, we extracted expression patterns that are characteristic of individual cell types, uncovering differences in cell lineage among tumor subtypes. We were able to detect the expression signature of hepatocytes in HCC, neuron cells in medulloblastoma, glia cells in glioma, basal and luminal epithelial cells in breast tumors, and various cell types in lung cancer samples. We also demonstrated that tissue-specific expression signatures are useful in locating the origin of metastatic tumors. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage, and metastasis.},
author = {Ge, Xijin and Yamamoto, Shogo and Tsutsumi, Shuichi and Midorikawa, Yutaka and Ihara, Sigeo and Wang, San Ming and Aburatani, Hiroyuki},
doi = {10.1016/j.ygeno.2005.04.008},
issn = {0888-7543},
journal = {Genomics},
keywords = {Algorithms,Brain Neoplasms,Brain Neoplasms: genetics,Carcinoma, Hepatocellular,Carcinoma, Hepatocellular: genetics,Cell Differentiation,Cell Lineage,Cluster Analysis,Gene Expression Regulation,Genes, BRCA1,Genome, Human,Humans,Liver Neoplasms,Liver Neoplasms: genetics,Lung Neoplasms,Models, Genetic,Multigene Family,Neoplasm Metastasis,Oligonucleotide Array Sequence Analysis,Phylogeny,Promoter Regions, Genetic,RNA,RNA: metabolism,Tissue Distribution},
mendeley-groups = {Phd-Papers,MGFM-vignette},
month = aug,
number = {2},
pages = {127--41},
pmid = {15950434},
title = {{Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues.}},
url = {http://www.sciencedirect.com/science/article/pii/S0888754305001114},
volume = {86},
year = {2005}
}
@article{Edgar2002,
abstract = {The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.},
author = {Edgar, Ron and Domrachev, Michael and Lash, Alex E},
file = {:Users/khadija/Library/Application Support/Mendeley Desktop/Downloaded/Edgar, Domrachev, Lash - 2002 - Gene Expression Omnibus NCBI gene expression and hybridization array data repository.pdf:pdf},
journal = {Nucleic Acids Research},
keywords = {animals,communication,database management systems,databases,forecasting,gene expression profiling,genetic,genome,humans,information storage retrieval,internet,national library medicine (u.s.),oligonucleotide array sequence analysis,united states},
mendeley-tags = {animals,communication,database management systems,forecasting,gene expression profiling,genome},
pages = {207--210},
pmid = {11752295},
title = {{Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.}},
volume = {30},
year = {2002}
}
@MANUAL{Team2007,
  title = {R: A Language and Environment for Statistical Computing},
  author = {R Development Core Team},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2007},
  note = {{ISBN} 3-900051-07-0},
  owner = {Schmidberger},
  timestamp = {2007.08.28},
  url = {http://www.R-project.org}
}
@INCOLLECTION{Irizarry2002,
  author = {R. Irizarry and L. Gautier and L. Cope},
  title = {An R package for analyses of affymetrix oligonucleotide arrays},
  booktitle = {The Analysis of Gene Expression Data: Methods and Software},
  publisher = {Springer},
  year = {2002},
  editor = {G. Parmigiani and E.S. Garrett and R.A. Irizarry and S.L. Zeger},
  address = {New York},
  owner = {Schmidberger},
  timestamp = {2008.01.21}
}

