Two-dimensional method for time aligning liquid chromatography-mass spectrometry data.

We describe a new time alignment method that takes advantage of both dimensions of LC-MS data to resolve ambiguities in peak matching while remaining computationally efficient. This approach, Warp2D, combines peak extraction with a two-dimensional correlation function to provide a reliable alignment scoring function that is insensitive to spurious peaks and background noise. One-dimensional alignment methods are often based on the total-ion-current elution profile of the spectrum and are unable to distinguish peaks of different masses. Our approach uses one-dimensional alignment in time, but with a scoring function derived from the overlap of peaks in two dimensions, thereby combining the specificity of two-dimensional methods with the computational performance of one-dimensional methods. The peaks are approximated as two-dimensional Gaussians of varying width. This approximation allows peak overlap (the measure of alignment quality) to be calculated analytically, without computationally intensive numerical integration in two dimensions. To demonstrate the general applicability of Warp2D, we chose a variety of complex samples that have substantial biological and analytical variability, including human serum and urine. We show that Warp2D works well with these diverse sample sets and with minimal tuning of parameters, based on the reduced standard deviation of peak elution times after warping. The combination of high computational speed, robustness with complex samples, and lack of need for detailed tuning makes this alignment method well suited to high-throughput LC-MS studies.

Identification of a global gene expression signature of B-chronic lymphocytic leukemia.

B-chronic lymphocytic leukemia (B-CLL) is an adult-onset leukemia characterized by significant accumulation of apoptosis-resistant monoclonal B lymphocytes. In this study, we performed gene expression profiling on B cells obtained from 10 healthy age-matched individuals and CLL B cells from 38 B-CLL patients to identify key genetic differences between CLL and normal B cells. In addition, we leveraged recent independent studies to assess the reproducibility of our molecular B-CLL signature. We used a novel combination of several methods of data analysis including our own software and identified 70 previously unreported genes that differentiate leukemic cells from normal B cells, as well as confirmed recently reported B-CLL specific expression levels of an additional 10 genes. Importantly, many of these genes have previously been linked with other cancers, thus lending further support to their importance as candidate genes leading to B-CLL pathogenesis. We have also validated a subset of these genes using independent methodologies. Moreover, we show that our genes can be used to create a diagnostics signature that performs with perfect sensitivity and specificity in an independent cohort of 21 B-CLL and 20 normal subjects, thus strongly validating the informative nature of our set of genes. Finally, we identified a group of 31 genes that distinguish between low (Rai stage 0) and high (Rai stage 4) risk patients, suggesting that there may also be a gene expression signature that associates with disease progression.

A robust meta-classification strategy for cancer diagnosis from gene expression data.

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome.wi.mit.du/MPR /lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

MOTIVATION: Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. RESULTS: We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. AVAILABILITY: Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

Can we identify cellular pathways implicated in cancer using gene expression data?

The cancer state of a cell is characterized by alterations of important cellular processes such as cell proliferation, apoptosis, DNA-damage repair, etc. The expression of genes associated with cancer related pathways, therefore, may exhibit differences between the normal and the cancerous states. We explore various means to find these differences. We analyze 6 different pathways (p53, Ras, Brca, DNA damage repair, NFkappab and beta-catenin) and 4 different types of cancer: colon, pancreas, prostate and kidney. Our results are found to be mostly consistent with existing knowledge of the involvement of these pathways in different cancers. Our analysis constitutes proof of principle that it may be possible to predict the involvement of a particular pathway in cancer or other diseases by using gene expression data. Such method would be particularly useful for the types of diseases where biology is poorly understood.