Bone marrow plasma cells (PCs) from 74 patients with newly diagnosed multiple myeloma (MM), 5 with monoclonal gammopathy of undetermined significance (MGUS), and 31 healthy volunteers (normal PCs) were purified by CD138 ؉ selection. Gene expression of purified PCs and 7 MM cell lines were profiled using highdensity oligonucleotide microarrays interrogating about 6800 genes. On hierarchical clustering analysis, normal and MM PCs were differentiated and 4 distinct subgroups of MM (MM1, MM2, MM3, and MM4) were identified. The expression pattern of MM1 was similar to normal PCs and MGUS, whereas MM4 was similar to MM cell lines. Clinical parameters linked to poor prognosis, abnormal karyotype (P ‫؍‬ .002) and high serum ␤ 2 -microglobulin levels (P ‫؍‬ .0005), were most prevalent in MM4. Also, genes involved in DNA metabolism and cell cycle control were overexpressed in a comparison of MM1 and MM4. In addition, using 2 and Wilcoxon rank sum tests, 120 novel candidate disease genes were identified that discriminate normal and malignant PCs (P < .0001); many are involved in adhesion, apoptosis, cell cycle, drug resistance, growth arrest, oncogenesis, signaling, and transcription. A total of 156 genes, including FGFR3 and CCND1, exhibited highly elevated ("spiked") expression in at least 4 of the 74 MM cases (range, 4-25 spikes). Elevated expression of these 2 genes was caused by the translocation t(4;14)(p16;q32) or t(11;14)(q13;q32). Thus, novel candidate MM disease genes have been identified using gene expression profiling and this profiling has led to the development of a gene-based classification system for MM. (Blood. 2002;99: 1745-1757

We introduce a transformation that stabilizes the variance of microarray data across the full range of expression. Simulation studies also suggest that this transformation approximately symmetrizes microarray data.

RNA-Seq is a widely used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An underemphasized feature of normalization is the assumptions on which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this article, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment.

Our findings warrant attention for IRDS and diaphragmatic hernia, close monitoring of the aortic root early in life, and extensive vascular imaging afterwards. EM on skin biopsies shows disease-specific abnormalities.

Mahalanobis-type distances in which the shape matrix is derived from a consistent, high-breakdown robust multivariate location and scale estimator have an asymptotic chisquared distribution as is the case with those derived from the ordinary covariance matrix. For example, Rousseeuw's minimum covariance determinant (MCD) is a robust estimator with a high breakdown. However, even in quite large samples, the chi-squared approximation to the distances of the sample data from the MCD center with respect to the MCD shape is poor. We provide an improved F approximation that gives accurate outlier rejection points for various sample sizes.