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1.  Fold change rank ordering statistics: a new method for detecting differentially expressed genes 
BMC Bioinformatics  2014;15:14.
Background
Different methods have been proposed for analyzing differentially expressed (DE) genes in microarray data. Methods based on statistical tests that incorporate expression level variability are used more commonly than those based on fold change (FC). However, FC based results are more reproducible and biologically relevant.
Results
We propose a new method based on fold change rank ordering statistics (FCROS). We exploit the variation in calculated FC levels using combinatorial pairs of biological conditions in the datasets. A statistic is associated with the ranks of the FC values for each gene, and the resulting probability is used to identify the DE genes within an error level. The FCROS method is deterministic, requires a low computational runtime and also solves the problem of multiple tests which usually arises with microarray datasets.
Conclusion
We compared the performance of FCROS with those of other methods using synthetic and real microarray datasets. We found that FCROS is well suited for DE gene identification from noisy datasets when compared with existing FC based methods.
doi:10.1186/1471-2105-15-14
PMCID: PMC3899927  PMID: 24423217
Differentially expressed genes; Fold change; Averages of ranks; Microarray
2.  Transcriptomic Analysis of Murine Embryos Lacking Endogenous Retinoic Acid Signaling 
PLoS ONE  2013;8(4):e62274.
Retinoic acid (RA), an active derivative of the liposoluble vitamin A (retinol), acts as an important signaling molecule during embryonic development, regulating phenomenons as diverse as anterior-posterior axial patterning, forebrain and optic vesicle development, specification of hindbrain rhombomeres, pharyngeal arches and second heart field, somitogenesis, and differentiation of spinal cord neurons. This small molecule directly triggers gene activation by binding to nuclear receptors (RARs), switching them from potential repressors to transcriptional activators. The repertoire of RA-regulated genes in embryonic tissues is poorly characterized. We performed a comparative analysis of the transcriptomes of murine wild-type and Retinaldehyde Dehydrogenase 2 null-mutant (Raldh2−/−) embryos — unable to synthesize RA from maternally-derived retinol — using Affymetrix DNA microarrays. Transcriptomic changes were analyzed in two embryonic regions: anterior tissues including forebrain and optic vesicle, and posterior (trunk) tissues, at early stages preceding the appearance of overt phenotypic abnormalities. Several genes expected to be downregulated under RA deficiency appeared in the transcriptome data (e.g. Emx2, Foxg1 anteriorly, Cdx1, Hoxa1, Rarb posteriorly), whereas reverse-transcriptase-PCR and in situ hybridization performed for additional selected genes validated the changes identified through microarray analysis. Altogether, the affected genes belonged to numerous molecular pathways and cellular/organismal functions, demonstrating the pleiotropic nature of RA-dependent events. In both tissue samples, genes upregulated were more numerous than those downregulated, probably due to feedback regulatory loops. Bioinformatic analyses highlighted groups (clusters) of genes displaying similar behaviors in mutant tissues, and biological functions most significantly affected (e.g. mTOR, VEGF, ILK signaling in forebrain tissues; pyrimidine and purine metabolism, calcium signaling, one carbon metabolism in posterior tissues). Overall, these data give an overview of the gene expression changes resulting from embryonic RA deficiency, and provide new candidate genes and pathways that may help understanding retinoid-dependent molecular events.
doi:10.1371/journal.pone.0062274
PMCID: PMC3634737  PMID: 23638021
3.  A Python module to normalize microarray data by the quantile adjustment method 
Microarray technology is widely used for gene expression research targeting the development of new drug treatments. In the case of a two-color microarray, the process starts with labeling DNA samples with fluorescent markers (cyanine 635 or Cy5 and cyanine 532 or Cy3), then mixing and hybridizing them on a chemically treated glass printed with probes, or fragments of genes. The level of hybridization between a strand of labeled DNA and a probe present on the array is measured by scanning the fluorescence of spots in order to quantify the expression based on the quality and number of pixels for each spot. The intensity data generated from these scans are subject to errors due to differences in fluorescence efficiency between Cy5 and Cy3, as well as variation in human handling and quality of the sample. Consequently, data have to be normalized to correct for variations which are not related to the biological phenomena under investigation. Among many existing normalization procedures, we have implemented the quantile adjustment method using the python computer language, and produced a module which can be run via an HTML dynamic form. This module is composed of different functions for data files reading, intensity and ratio computations and visualization. The current version of the HTML form allows the user to visualize the data before and after normalization. It also gives the option to subtract background noise before normalizing the data. The output results of this module are in agreement with the results of other normalization tools.
doi:10.1016/j.meegid.2010.10.008
PMCID: PMC3087835  PMID: 20970526
module; quantile method; python; microarray; normalization
4.  Novel insights into the relationships between dendritic cell subsets in human and mouse revealed by genome-wide expression profiling 
Genome Biology  2008;9(1):R17.
Genome-wide expression profiling of mouse and human leukocytes reveal conserved transcriptional programs of plasmacytoid or conventional dendritic cell subsets.
Background
Dendritic cells (DCs) are a complex group of cells that play a critical role in vertebrate immunity. Lymph-node resident DCs (LN-DCs) are subdivided into conventional DC (cDC) subsets (CD11b and CD8α in mouse; BDCA1 and BDCA3 in human) and plasmacytoid DCs (pDCs). It is currently unclear if these various DC populations belong to a unique hematopoietic lineage and if the subsets identified in the mouse and human systems are evolutionary homologs. To gain novel insights into these questions, we sought conserved genetic signatures for LN-DCs and in vitro derived granulocyte-macrophage colony stimulating factor (GM-CSF) DCs through the analysis of a compendium of genome-wide expression profiles of mouse or human leukocytes.
Results
We show through clustering analysis that all LN-DC subsets form a distinct branch within the leukocyte family tree, and reveal a transcriptomal signature evolutionarily conserved in all LN-DC subsets. Moreover, we identify a large gene expression program shared between mouse and human pDCs, and smaller conserved profiles shared between mouse and human LN-cDC subsets. Importantly, most of these genes have not been previously associated with DC function and many have unknown functions. Finally, we use compendium analysis to re-evaluate the classification of interferon-producing killer DCs, lin-CD16+HLA-DR+ cells and in vitro derived GM-CSF DCs, and show that these cells are more closely linked to natural killer and myeloid cells, respectively.
Conclusion
Our study provides a unique database resource for future investigation of the evolutionarily conserved molecular pathways governing the ontogeny and functions of leukocyte subsets, especially DCs.
doi:10.1186/gb-2008-9-1-r17
PMCID: PMC2395256  PMID: 18218067

Results 1-4 (4)