POWER_SAGE: comparing statistical tests for SAGE experiments

Man, Michael; Wang, Xuning; Wang, Yixin

doi:10.1093/bioinformatics/16.11.953

Cited by 154 publications

(122 citation statements)

References 14 publications

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“…The overall number of unique tags in both libraries was 14 968, 4092 of which appear more than once in the two libraries pooled together. (b) The number of unique tags has been plotted as a function of their occurrence in the self-renewal library (SR, white bars) or in the 24 h differentiation library (Di, gray bars) SAGE on chicken erythrocytic progenitors F Damiola et al comparison occurs by chance (Figure 2) using the Z test (Man et al, 2000). Since we have validated some of our candidate genes by real-time PCR (see below), we experimentally define the cutoff value for significantly differential genes as the largest P-value subsequently confirmed by real-time PCR.…”

Section: Differential Comparison Between Tags Abundancementioning

confidence: 99%

Global transcription analysis of immature avian erythrocytic progenitors: from self-renewal to differentiation

et al. 2004

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The molecular mechanisms regulating the cell fate decision between self-renewal and differentiation/apoptosis in stem and progenitor cells are poorly understood. Here, we report the first comprehensive identification of genes potentially involved in the switch from self-renewal toward differentiation of primary, non-immortalized erythroid avian progenitor cells (T2EC cells). We used the Serial Analysis of Gene Expression (SAGE) technique in order to identify and quantify the genome fraction functionally active in a self-renewing versus a differentiating cell population. We generated two SAGE libraries and sequenced a total of 37 589 tags, thereby obtaining the first transcriptional profile characterization of a chicken cell. Tag identification was performed using a new relational database (Identitag) developed in the laboratory, which allowed a highly satisfactory level of identification. Among 123 differentially expressed genes, 11 were investigated further and for nine of them the differential expression was subsequently confirmed by real-time PCR. The comparison of tag abundance between the two libraries revealed that only a small fraction of transcripts was differentially expressed. The analysis of their functions argue against a prominent role for a master switch in T2EC cells decision-making, but are in favor of a critical role for coordinated small variations in a relatively small number of genes that can lead to essential cellular identity changes.

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Section: Differential Comparison Between Tags Abundancementioning

confidence: 99%

Global transcription analysis of immature avian erythrocytic progenitors: from self-renewal to differentiation

et al. 2004

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“…This comparison showed that the chi-squared test has consistently a higher power and is more robust than the other tests, especially at low expression levels (Ͻ15 tags/50,000). Therefore, the chi-squared test, which is equivalent to the Z test, was concluded to be the preferred choice for evaluating SAGE experiments (12).…”

Section: Review: Analysis and Design Of Sage Experimentsmentioning

confidence: 99%

“…The Fisher's Exact test has been proposed by the Cancer Genome Anatomy Project for comparison of specific tags between SAGE libraries (3,12). Also, the chi-squared test has been used (14).…”

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“…Recently the chi-squared test, Fisher's Exact test, and the Audic and Claverie test were compared with respect to their power and robustness (12). The Madden test and SAGE300 were not included in this comparison, nor was there a comparison of the differences that are needed to lead to a statistically significant result.…”

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confidence: 99%

“…Another program that will calculate the number of required tags and perform a chi-squared test between SAGE libraries is POWER_SAGE (E-mail: michael.man @pfizer.com; Ref. 12), which is based on Monte Carlo simulations.…”

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Statistical evaluation of SAGE libraries: consequences for experimental design

Kampen

Baas

2002

Physiological Genomics

107

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Since the introduction of serial analysis of gene expression (SAGE) as a method to quantitatively analyze the differential expression of genes, several statistical tests have been published for the pairwise comparison of SAGE libraries. Testing the difference between the number of specific tags found in two SAGE libraries is hampered by the fact that each SAGE library is only one measurement: the necessary information on biological variation or experimental precision is not available. In the currently available tests, a measure of this variance is obtained from simulation or based on the properties of the tag distribution. To help the user of SAGE to decide between these tests, five different pairwise tests have been compared by determining the critical values, that is, the lowest number of tags that, given an observed number of tags in one library, needs to be found in the other library to result in a significant P value. The five tests included in this comparison are SAGE300, the tests described by Madden et al. ( Oncogene 15: 1079–1085, 1997) and by Audic and Claverie ( Genome Res 7: 986–995, 1997), Fisher’s Exact test, and the Z test, which is equivalent to the chi-squared test. The comparison showed that, for SAGE libraries of equal as well as different size, SAGE300, Fisher’s Exact test, Z test, and the Audic and Claverie test have critical values within 1.5% of each other. This indicates that these four tests will give essentially the same results when applied to SAGE libraries. The Madden test, which can only be used for libraries of similar size, is, with 25% higher critical values, more conservative, probably because the variance measure in its test statistic is not appropriate for hypothesis testing. The consequences for the choice of SAGE library sizes are discussed.

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A comparison of existing tools for ontological analysis of gene expression data

Khatri

Drăghici

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high‐throughput experiments, and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues, and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis.

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POWER_SAGE: comparing statistical tests for SAGE experiments

Abstract: michael.man@pfizer.com

Cited by 154 publications

References 14 publications

Global transcription analysis of immature avian erythrocytic progenitors: from self-renewal to differentiation

Global transcription analysis of immature avian erythrocytic progenitors: from self-renewal to differentiation

Statistical evaluation of SAGE libraries: consequences for experimental design

A comparison of existing tools for ontological analysis of gene expression data

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