A Comprehensive and Universal Method for Assessing the Performance of Differential Gene Expression Analyses

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods

The cell cycle regulatory DREAM complex is disrupted by high expression of oncogenic B-Myb.

Overexpression of the oncogene MYBL2 (B-Myb) is associated with increased cell proliferation and serves as a marker of poor prognosis in cancer. However, the mechanism by which B-Myb alters the cell cycle is not fully understood. In proliferating cells, B-Myb interacts with the MuvB core complex including LIN9, LIN37, LIN52, RBBP4, and LIN54, forming the MMB (Myb-MuvB) complex, and promotes transcription of genes required for mitosis. Alternatively, the MuvB core interacts with Rb-like protein p130 and E2F4-DP1 to form the DREAM complex that mediates global repression of cell cycle genes in G0/G1, including a subset of MMB target genes. Here, we show that overexpression of B-Myb disrupts the DREAM complex in human cells, and this activity depends on the intact MuvB-binding domain in B-Myb. Furthermore, we found that B-Myb regulates the protein expression levels of the MuvB core subunit LIN52, a key adapter for assembly of both the DREAM and MMB complexes, by a mechanism that requires S28 phosphorylation site in LIN52. Given that high expression of B-Myb correlates with global loss of repression of DREAM target genes in breast and ovarian cancer, our findings offer mechanistic insights for aggressiveness of cancers with MYBL2 amplification, and establish the rationale for targeting B-Myb to restore cell cycle control

Internal standard-based analysis of microarray data2—Analysis of functional associations between HVE-genes

In this work we apply the Internal Standard-based analytical approach that we described in an earlier communication and here we demonstrate experimental results on functional associations among the hypervariably-expressed genes (HVE-genes). Our working assumption was that those genetic components, which initiate the disease, involve HVE-genes for which the level of expression is undistinguishable among healthy individuals and individuals with pathology. We show that analysis of the functional associations of the HVE-genes is indeed suitable to revealing disease-specific differences. We show also that another possible exploit of HVE-genes for characterization of pathological alterations is by using multivariate classification methods. This in turn offers important clues on naturally occurring dynamic processes in the organism and is further used for dynamic discrimination of groups of compared samples. We conclude that our approach can uncover principally new collective differences that cannot be discerned by individual gene analysis

High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses

<p>Abstract</p> <p>Background</p> <p>Microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses.</p> <p>Methods</p> <p>We present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses.</p> <p>Results</p> <p>13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data.</p> <p>Conclusions</p> <p>Normalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not only empowers meta-analytic approaches, but aids in reproducibility by providing a standard way of approaching the problem.</p> <p>Data availability: matrix containing normalized expression of 20,813 genes across 13,000 experiments is available for download at . Source code for GDS files pre-processing is available from the authors upon request.</p

B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for celltype-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE

Internal standard-based analysis of microarray data2—Analysis of functional associations between HVE-genes

In this work we apply the Internal Standard-based analytical approach that we described in an earlier communication and here we demonstrate experimental results on functional associations among the hypervariably-expressed genes (HVE-genes). Our working assumption was that those genetic components, which initiate the disease, involve HVE-genes for which the level of expression is undistinguishable among healthy individuals and individuals with pathology. We show that analysis of the functional associations of the HVE-genes is indeed suitable to revealing disease-specific differences. We show also that another possible exploit of HVE-genes for characterization of pathological alterations is by using multivariate classification methods. This in turn offers important clues on naturally occurring dynamic processes in the organism and is further used for dynamic discrimination of groups of compared samples. We conclude that our approach can uncover principally new collective differences that cannot be discerned by individual gene analysi