Unbiased pattern detection in microarray data series

Ahnert, Sebastian E.; Willbrand, Karen; Brown, Francis; Fink, Thomas

doi:10.1093/bioinformatics/btl121

Cited by 20 publications

(40 citation statements)

References 15 publications

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“…However, identifying the set of periodically expressed genes is a non-trivial endeavor and has been the subject of intense study. 1,[14][15][16][17][18] Three separate studies have reported global time-series transcription data across the mitotic cell cycle, and generated unique lists of periodically transcribed genes. 1,17,18 The overlap between these published lists is surprisingly small (see Suppl.…”

Section: Identifying Periodic Genesmentioning

confidence: 99%

Transcription network and cyclin/CDKs: The yin and yang of cell cycle oscillators

Kovacs¹,

Orlando²,

Haase³

2008

Cell Cycle

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Section: Identifying Periodic Genesmentioning

confidence: 99%

Transcription network and cyclin/CDKs: The yin and yang of cell cycle oscillators

Kovacs¹,

Orlando²,

Haase³

2008

Cell Cycle

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“…Examples include cyclic patterns such as those encountered in the Spellman alphafactor synchronization yeast cell-cycle experiment. At this point, it might be necessary to point out certain resemblance between the zero-crossing analysis and the complexity maps proposed in [12]. The first-order crossing order crossings obtained repeated application of the difference operator (high-pass filter) [5].…”

Section: Spectral Analysis By Zero-crossingmentioning

confidence: 99%

“…On closer observation, we note subtle differences in their definitions. The complexity maps [12] use (i) ranks of the values as opposed to the sign of the meansubtracted values (1).…”

Section: Spectral Analysis By Zero-crossingmentioning

confidence: 99%

“…The latter is obtained by randomly shuffling the former. Complexity measures which overcome some of the caveats of entropy have recently been proposed to investigate gene expression profiles [12,15,16]. Traditionally, uncorrelated noise is considered to be maximally complex or random.…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of gene expression analysis, genes with low complexity are hypothesized to be biologically relevant. A recent study, proposed several measures of complexity for the analysis of temporal gene expression profiles in yeast cell-cycle experiment [12]. Such an analysis was carried out in an unbiased manner in the absence of any prior knowledge about the given data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Non-random Patterns from Gene Expression Profiles

Nagarajan

Upreti

Milanova

2008

Pattern Recognition in Bioinformatics

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Abstract. There has been considerable interest in identifying biologically relevant genes from temporal microarray gene expression profiles using linear and nonlinear measures. The present study uses two distinct approaches namely: classical order zero-crossing count (ZCC) and Lempel-Ziv (LZ) complexity in identifying non-random patterns from temporal gene expression profiles. While the former captures the linear statistical properties of the time series such a power-spectrum, the latter has been used to capture nonlinear dynamical properties of gene expression profiles. The results presented elucidate that ZCC can perform better than LZ in identifying biologically relevant genes. The robustness of the findings are established on the given gene expression profiles as well as their noisy versions. The performance of these two techniques is demonstrated on publicly available yeast cell-cycle gene expression data. A possible explanation for the better performance of the ZCC over LZ complexity may be attributed to inherent cyclic patterns characteristic of the yeast cell-cycle experiment. Finally we discuss the biological relevance of new genes identified using ZCC not previously reported.

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Analysis of Time Course Omics Datasets

Grigorov

2011

Methods in Molecular Biology

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Over the past 20 years, Omics technologies emerged as the consensual denomination of holistic molecular profiling. These techniques enable parallel measurements of biological -omes, or "all constituents considered collectively", and utilize the latest advancements in transcriptomics, proteomics, metabolomics, imaging, and bioinformatics. The technological accomplishments in increasing the sensitivity and throughput of the analytical devices, the standardization of the protocols and the widespread availability of reagents made the capturing of static molecular portraits of biological systems a routine task. The next generation of time course molecular profiling already allows for extensive molecular snapshots to be taken along the trajectory of time evolution of the investigated biological systems. Such datasets provide the basis for application of the inverse scientific approach. It consists in the inference of scientific hypotheses and theories about the structure and dynamics of the investigated biological system without any a priori knowledge, solely relying on data analysis to unveil the underlying patterns. However, most temporal Omics data still contain a limited number of time points, taken over arbitrary time intervals, through measurements on biological processes shifted in time. The analysis of the resulting short and noisy time series data sets is a challenge. Traditional statistical methods for the study of static Omics datasets are of limited relevance and new methods are required. This chapter discusses such algorithms which enable the application of the inverse analysis approach to short Omics time series.

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Unbiased pattern detection in microarray data series

Cited by 20 publications

References 15 publications

Transcription network and cyclin/CDKs: The yin and yang of cell cycle oscillators

Transcription network and cyclin/CDKs: The yin and yang of cell cycle oscillators

Identifying Non-random Patterns from Gene Expression Profiles

Analysis of Time Course Omics Datasets

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