Journal: BMC Bioinformatics

Article Title: Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

doi: 10.1186/1471-2105-5-60

Figure Lengend Snippet: Mean versus variance plot. Plot of the mean probe set MAS 5.0 signal intensities by the corresponding variance across the 16 HG-U133A GeneChips ® .

Article Snippet: In this paper, an Affymetrix GeneChip ® HG-U133A dataset consisting of 16 technical replicates (QAQC Dataset), where the Microarray Suite Software (version 5.0) was used to derive the expression summaries for all probe sets, is used to demonstrate some of the problems associated with applying the log 2 transformation to absolute intensity data.

Techniques:

Journal: BMC Bioinformatics

Article Title: Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

doi: 10.1186/1471-2105-5-60

Figure Lengend Snippet: Mean versus variance plot of log 2 transformed data. Plot of the mean log 2 transformed MAS 5.0 probe set signal intensities by their associated variance for the 16 HG-U133A GeneChips ® .

Article Snippet: In this paper, an Affymetrix GeneChip ® HG-U133A dataset consisting of 16 technical replicates (QAQC Dataset), where the Microarray Suite Software (version 5.0) was used to derive the expression summaries for all probe sets, is used to demonstrate some of the problems associated with applying the log 2 transformation to absolute intensity data.

Techniques: Transformation Assay

Journal: BMC Bioinformatics

Article Title: Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

doi: 10.1186/1471-2105-5-60

Figure Lengend Snippet: Spread versus level plot. Spread-versus-level plot for 16 HG-U133A GeneChips ® using MAS 5.0 probe set expression summaries; parameter estimates from least squares regression: = 0.052, = 0.57.

Article Snippet: In this paper, an Affymetrix GeneChip ® HG-U133A dataset consisting of 16 technical replicates (QAQC Dataset), where the Microarray Suite Software (version 5.0) was used to derive the expression summaries for all probe sets, is used to demonstrate some of the problems associated with applying the log 2 transformation to absolute intensity data.

Techniques: Expressing

Journal: BMC Bioinformatics

Article Title: Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

doi: 10.1186/1471-2105-5-60

Figure Lengend Snippet: Mean versus variance plot of power transformed data. Plot of the mean of the probe set signal intensities after applying the transformation by the associated variance for the 16 HG-U133A GeneChips ® .

Article Snippet: In this paper, an Affymetrix GeneChip ® HG-U133A dataset consisting of 16 technical replicates (QAQC Dataset), where the Microarray Suite Software (version 5.0) was used to derive the expression summaries for all probe sets, is used to demonstrate some of the problems associated with applying the log 2 transformation to absolute intensity data.

Techniques: Transformation Assay

Journal: BMC Bioinformatics

Article Title: Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

doi: 10.1186/1471-2105-5-60

Figure Lengend Snippet: Rank versus spread plot of power transformed data. Plot of the rank of the median probe set signal intensities after applying the transformation by the associated fourth-spread across the 16 HG-U133A GeneChips ® . The fitted lowess regression curve is overlaid where the span is 1/3.

Article Snippet: In this paper, an Affymetrix GeneChip ® HG-U133A dataset consisting of 16 technical replicates (QAQC Dataset), where the Microarray Suite Software (version 5.0) was used to derive the expression summaries for all probe sets, is used to demonstrate some of the problems associated with applying the log 2 transformation to absolute intensity data.

Techniques: Transformation Assay

Journal: BMC Bioinformatics

Article Title: Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals

doi: 10.1186/1471-2105-5-60

Figure Lengend Snippet: Rank versus spread plot of generalized logarithm transformed data. Plot of the rank of the median probe set signal intensities after applying the generalized logarithm transformation by the associated fourth-spread across the 16 HG-U133A GeneChips ® . The fitted lowess regression curve is overlaid where the span is 1/3.

Article Snippet: In this paper, an Affymetrix GeneChip ® HG-U133A dataset consisting of 16 technical replicates (QAQC Dataset), where the Microarray Suite Software (version 5.0) was used to derive the expression summaries for all probe sets, is used to demonstrate some of the problems associated with applying the log 2 transformation to absolute intensity data.

Techniques: Transformation Assay

Journal: BMC Bioinformatics

Article Title: An eScience-Bayes strategy for analyzing omics data

doi: 10.1186/1471-2105-11-282

Figure Lengend Snippet: eScience-Bayes applied to microarray gene expression data . Illustration of using the eScience-Bayes approach to model time to development of distant metastases in breast cancer patients from microarray gene expression data. (Color-coding is as in Fig. ; the green blocks in Fig. are not included since we did not use any locally produced data or information.) (A) We downloaded five breast cancer gene expression datasets and their associated clinical data using the Gene Expression Omnibus (GEO) Web service. For trustworthy prior information about genes with altered transcriptional regulation in breast cancer patients, we used three Web services in conjunction: NetPath, DictService and Entrez Utilities. This gave us a list of integers that represented the number of times a cancer-pathway regulated gene on the HG-U133A array was mentioned in PubMed articles together with breast cancer. (B) We used the prior information derived in ( A ) to restrict the dimensionality of Models I and II by Bayesian variable selection. We did this by deriving prior distributions based on the assumption that the probability of a gene being related to breast cancer was reflected in the number of times a gene reported in NetPath was mentioned in combination with breast cancer in PubMed articles. (C) The models were fit by performing calculations a HPC resource.

Article Snippet: It would then be easy to obtain prior information and data by performing semantic queries, for example, "give me all breast cancer susceptibility genes and the probability distributions describing their association with increasing risk of distant metastasis development, together with all breast cancer Affymetrix HG-U133A datasets where distant metastasis development was the clinical end-point".

Techniques: Microarray, Expressing, Produced, Derivative Assay, Selection

Journal: Transcription

Article Title: linc00673 (ERRLR01) is a prognostic indicator of overall survival in breast cancer

doi: 10.1080/21541264.2017.1329684

Figure Lengend Snippet: LncRNA expression in breast cancer patient populations which was acquired using an Affymetrix U133A array dataset, and is depicted by box-whisker-plots. In this analysis, ERRLR01 expression was stratified into 4 subpopulations, and the mean rank expression was reported (A). 1 = TNBC, n = 577; 2 = Luminal A, n = 1432; 3 = Luminal B, n = 632; 4 = HER2+, n = 301. ERRLR01 expression in the TCGA data set (B), and data are presented in a log(2) transformed format. 1 = TNBC, n = 154; 2 = Luminal A, n = 91; 3 = Luminal B, n = 538; 4 = HER2+, n = 53. *denotes significance at P < 1 × 10−16 as determined by Kruskal-Wallis one-way variance analysis testing.

Article Snippet: Together, results from both patient datasets indicated that ERRLR01 expression was elevated in ERα − tumor subtypes, as compared with ERα + tumor subtypes ( p value < 1 × 10 −16 , as determined by Kruskal-Wallis analysis). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 1. caption a7 LncRNA expression in breast cancer patient populations which was acquired using an Affymetrix U133A array dataset, and is depicted by box-whisker-plots.

Techniques: Expressing, Whisker Assay, Transformation Assay