hg-u133a/b gene probe set analysis dataset  (Thermo Fisher)

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: BMC Genomics

Article Title: Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

doi: 10.1186/1471-2164-15-S12-S8

Figure Lengend Snippet: A comparison of expression profiles obtained using two different Affymetrix HG-U133A probesets mapped to RIPK2 gene .

Article Snippet: The test Affymetrix HG U133A dataset GSE1133 was downloaded from BioGPS database [ ].

Techniques: Expressing

Journal: BMC Genomics

Article Title: Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

doi: 10.1186/1471-2164-15-S12-S8

Figure Lengend Snippet: Probeset composition per gene in Affymetrix U133A microarray after reannotation .

Article Snippet: The test Affymetrix HG U133A dataset GSE1133 was downloaded from BioGPS database [ ].

Techniques: Microarray

Journal: BMC Genomics

Article Title: Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

doi: 10.1186/1471-2164-15-S12-S8

Figure Lengend Snippet: The distribution of correlation coefficients values for a subset of genes/gene groups matched by two different probesets . Pearson's product-moment correlation coefficient r is on the x-axis and Spearman's rank correlation coefficient ρ is on the y-axis. Each dot represents correlation between expression values obtained with two different probesets corresponding to one gene on the Affymetrix U133A microarray in GSE1133 dataset. Black lines mark the quadrants that distinguish sets of genes behaving in one or another distinct way that sets them apart from each other. The quadrants were drawn according to correlation coefficient values. Values above 0.7 and below 0.3 were considered cut-offs. Number of genes that fell into each quadrant is shown in red color. Question marks represent the areas populated by genes with less deviant behavior that is evident from milder degree of decrepancy between correlation coefficients calculated using Pearson's coefficient r and Spearman's coefficient ρ . Dark red dashed line represents linear regression line and its equation is in the bottom right corner of the plot.

Article Snippet: The test Affymetrix HG U133A dataset GSE1133 was downloaded from BioGPS database [ ].

Techniques: Expressing, Microarray

Journal: BMC Genomics

Article Title: Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

doi: 10.1186/1471-2164-15-S12-S8

Figure Lengend Snippet: An example of a gene ( FMO3 ) with tissue-specific isoforms with expression patterns described by low r and high ρ correlation coefficients (set 3) . a. The mapping of the probesets 40665_at and 206496_at to the genomic location of FMO3 gene according to PLANdbAffy custom track in UCSC Genome Browser. b. Expression profile of the FMO3 gene in tissue panel of the GSE1133 dataset.

Article Snippet: The test Affymetrix HG U133A dataset GSE1133 was downloaded from BioGPS database [ ].

Techniques: Expressing

Journal: BMC Genomics

Article Title: Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

doi: 10.1186/1471-2164-15-S12-S8

Figure Lengend Snippet: An example of a gene ( GAL ) with expression patterns described by low r and high ρ correlation coefficients (set 3) . a. The mapping of the probesets 2142405_at and 207466_at to the genomic location of GAL gene according to PLANdbAffy custom track in UCSC Genome Browser b. Expression profile of the GAL gene in tissue panel of the GSE1133 dataset.

Article Snippet: The test Affymetrix HG U133A dataset GSE1133 was downloaded from BioGPS database [ ].

Techniques: Expressing

Journal: BMC Genomics

Article Title: Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization

doi: 10.1186/1471-2164-15-S12-S8

Figure Lengend Snippet: Plot of means and coefficients of variation of expression data obtained with different probe sets annotated to one gene with Spearman's correlation ρ ≥ 0 .9 (upper panel) and 0.7 ≤ ρ < 0.9 (lower panel). Each dot represents means of expression data (left part) or coefficients of correlation (right part) obtained with two probesets corresponding to one gene on the Affymetrix U133A microarray in GSE1133 dataset (probe set 1 on the x-axis and probe set 2 on the y-axis). Black line represents linear regression line and its equation is in the top left corner of plot. See also Additional file Supplementary Fig S4.

Article Snippet: The test Affymetrix HG U133A dataset GSE1133 was downloaded from BioGPS database [ ].

Techniques: Expressing, Microarray

Journal: BMC Cancer

Article Title: Pan-cancer analysis identifies BIRC5 as a prognostic biomarker

doi: 10.1186/s12885-022-09371-0

Figure Lengend Snippet: Box plots showing BIRC5 expression in ( A ) 16 different TCGA cancer types, ( B ) breast cancer compared to corresponding normal tissue. BIRC5 expression is significantly higher in all of the analyzed cancer types than normal tissue. C Affymetrix HG-U133A RNA microarray dataset with 21 cancer types, and ( D ) Affymetrix HG-U133 Plus 2 RNA microarray dataset comprised of 25 cancer types. BIRC5 expression is significantly higher in the majority of the analyzed cancer types than normal tissue. The Wilcoxon test was used to calculate statistically significant (Benjamini-Hochberg adjusted p -values) differences in BIRC5 expression between cancer and normal tissue. ns = not significant ( P > 0.05); * P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001; **** P ≤ 0.0001

Article Snippet: After filtering, the Affymetrix HG-U133A dataset included 21 cancer types comprised of 16,539 cancer samples and 4283 normal samples, while the Affymetrix HG-133 Plus 2 dataset included 25 cancer types comprised of 35,523 cancer samples and 5063 normal samples.

Techniques: Expressing, Microarray