Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 1. Genomic coverage of the TruSeq EPIC at different sequencing depths for the 11 breast tissue samples. T, breast tumour tissue; AN, adjacent normal breast tissue; K, normal breast tissue.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques: Sequencing

Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 2. The distribution of CpGs by different genomic annotations from TruSeq EPIC (≥10X) and EPIC-array platforms. (a) CpG- island context; (b) genomic function context; (c) regulatory region context; (d) chromosome.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques:

Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 4. Pearson correlation between methylation β-values of the common CpGs across TruSeq EPIC (≥10X) and EPIC-array platforms by different sequencing depth for our 11 breast tissue samples.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques: Methylation, Sequencing

Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 3. Distribution of methylation β values from the two platforms for our 11 breast tissue samples. (a) the common CpGs across the two platforms in TruSeq EPIC (≥10X); (b) the common CpGs in EPIC-array; (c) all CpGs detected in TruSeq EPIC (≥10X); (d) all CpG detected in EPIC-array.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques: Methylation

Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 5. Scatterplots and Pearson correlations of the mean methylation β values for the common CpGs from TruSeq EPIC (≥10X) and EPIC-array data. Red dotted lines denote Y = X. (a) T samples; (b) AN samples; (c) K samples; (d) all samples combined.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques: Methylation

Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 6. Scatterplots and Pearson correlations of the mean differences of methylation β values (∆β) for the common CpGs between two tissue types from TruSeq EPIC (≥10X) and EPIC-array data. Red dotted lines denote Y = X. (a) T vs. K; (b) T vs. AN; and (c) AN vs. K.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques: Methylation

Journal: Epigenetics

Article Title: Genome-wide DNA methylation profiling in human breast tissue by Illumina TruSeq methyl capture EPIC sequencing and infinium methylationEPIC beadchip microarray.

doi: 10.1080/15592294.2020.1827703

Figure Lengend Snippet: Figure 7. The number of differentially methylated positions (DMPs) between tissue types identified by TruSeq EPIC (≥10X) and EPIC- array platforms. DMPs were defined by FDR < 0.05 and |∆β| ≥ 0.1. (a) the common CpGs across the two platforms; (b) all CpGs detected by each platform.

Article Snippet: A newly-developed platform, the Illumina TruSeq Methyl Capture EPIC library prep (TruSeq EPIC), builds on the content of the Infinium MethylationEPIC Beadchip Microarray (EPIC-array) and leverages the power of next-generation sequencing for targeted bisulphite sequencing.

Techniques: Methylation

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Differentially expressed in blood and biopsy by microarrays and RNA-seq using statistical confidence levels of FDR <10% and p <0.005 and p< 0.05

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Correlation between the blood findings and the biopsy findings comparing the two analytical methodologies (microarrays and RNA-seq). The similarity between the technologies was also reflected in the consistently higher number of differentially expressed genes in the biopsy compared to the blood. The M (log ratios) and A (average) scale (MA) plots for all comparisons are shown in Figures 1a for RNAseq (NGS) and b for the microarrays.

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: RNA Sequencing Assay

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Classifiers and predictive performance

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Shared and differentially expressed genes blood and biopsy by microarrays and RNA-seq using statistical confidence levels of FDR <10%, p <0.005 and p <0.05

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Comparison of the directionality of fold-changes among the shared genes between blood and biopsies by microarrays and RNA-seq.

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: Microarray

Journal: American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons

Article Title: Orthogonal Comparison of Molecular Signatures of Kidney Transplants with Subclinical and Clinical Acute Rejection – Equivalent Performance is Agnostic to either Technology or Platform

doi: 10.1111/ajt.14224

Figure Lengend Snippet: Scatter plots of the fold changes for cAR vs TX (x-axes) and subAR vs. TX (y-axes) to demonstrate that that cAR fold changes are of greater magnitude than subAR changes. 3a, Microarrays - Blood 3b, RNA-seq - Blood 3c, Microarray - Biopsies 3d RNA-seq - Biopsies. Green dots denote greater cAR vs TX fold changes and blue dots denote greater subAR vs. TX fold changes. The table shows the number of genes in each comparison and the overlapping genes that were plotted to create figures 3a-d.

Article Snippet: The results of the study demonstrate: 1) diagnostic performance based on the ability to retrospectively predict known clinical phenotypes using SVM were equivalent across technologies and platforms (Affymetrix DNA microarrays, Ion Torrent and Illumina RNA-seq), 2) optimal classifiers selected using the SVM algorithm were different reflecting the technical differences inherent in the chemistries for detection and quantification of gene expression across platforms 3) despite significant overlap in the differentially expressed genes between the blood and biopsies, directionality of these changes differed considerably between compartments, suggesting that these represent distinct immune compartments, and 4) allo-immune/inflammatory pathway mapping to compare differential gene expression in blood and biopsies revealed a number of different genes in each of these two compartments, but these mapped to similar pathways across all technologies.

Techniques: RNA Sequencing Assay, Microarray

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: CircRNA collections on the circRNA microarrays.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Microarray, Sequencing, Labeling, Reverse Transcription, Amplification, In Vitro

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: CircRNA microarray detection of circRNAs. ( A ) The array’s probe sequence spans the circular junction (arrow) of the circRNA. In the circRNA microarray workflow, total RNA ( B ) is treated with RNase R to selectively digest the linear RNAs so as to enrich the circRNAs ( C ). The circRNAs are copied by reverse transcriptase (RT) from the annealed random primers containing a T7 promoter. The cRNA is synthesized from the T7 promoter at the end of the cDNA template by in vitro T7 polymerase transcription, incorporating fluorescent Cy3-UTP ( D ). The fluorescent cRNA is hybridized to the probes printed on the array surface ( E ).

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Microarray, Sequencing, Reverse Transcription, Synthesized, In Vitro

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: CircRNA probe signal QC flags.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques:

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Snapshot of differentially expressed circRNA table.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques:

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: An example of circular RNA and its binding miRNA. The MRE type (7mer-m8), seed region, 3′ pairing, local AU, and position in the circRNA are displayed.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Binding Assay

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Biofluid volumes for circRNA microarray.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Microarray, Isolation, Clinical Proteomics, Cell Culture

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Published biofluid biomarker studies based on circRNA microarray profiling.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Biomarker Discovery, Microarray, Clinical Proteomics, Functional Assay, RNA Expression, Diagnostic Assay, Expressing, Virus, Migration, Permeability

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: In circRNA-qPCR, Primer F and Primer R are situated on the two sides of the circular junction, producing the amplicon that spans the circular junction. The exon numbers are for illustration only.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Amplification

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Differentially expressed circRNAs from circRNA microarray profiling, analyzed for miRNA sponge functions (published in years 2020–2022).

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Microarray, Migration

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: ( A ) CircRNA abundance relative to mRNA in total RNA. ( B ) Circular junction reads (in the yellow box region) only represent a small portion of the entire circRNA. The reads from other parts of the circRNA body (arrowed region) are indistinguishable from that of the linear RNA counterparts.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques:

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Low raw counts of a randomly picked circRNA hsa_circ_0054254 in various tissues and cells in the circRNA studies.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques:

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Density distribution of circRNA counts by circRNA-seq in colorectal cancer and normal control samples (GEO Accession: GSE205241). The limit of quantification (LoQ) is indicated by the blue vertical line and the limit of differential ratio (LoDR, down to a 2-fold change) by the red vertical line. Less than 0.3% of the circRNAs are above the LoDR (shaded red). The circRNA-seq was run on Illumina NovaSeq 6000 at a 150 bp read length and a coverage of 40 million paired end fragments.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Control

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Quantification accuracy (i.e., low error) with RNA abundance by using a microarray or RNA sequencing. Data are based on a large-scale clinical study . While RNA sequencing rapidly loses accuracy (i.e., high error) when the RNA abundance is low, microarray accuracy is relatively unaffected even with a low RNA abundance. Typical circRNA abundance is indicated by the black arrow.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Microarray, RNA Sequencing

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Differentially expressed circRNAs by circRNA microarray and circRNA-seq.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Microarray

Journal: Biomolecules

Article Title: Circular RNA Expression Profiling by Microarray—A Technical and Practical Perspective

doi: 10.3390/biom13040679

Figure Lengend Snippet: Comparison of the circRNA microarray and circRNA-seq for circRNA expression profiling.

Article Snippet: Technology platform , Arraystar Human CircRNA Microarray V2 , Illumina NovaSeq 6000 (40 mil paired-end read coverage, 150-base read length).

Techniques: Comparison, Microarray, Expressing, Sequencing

Journal: Molecular Medicine Reports

Article Title: Systematic analysis of microarray datasets to identify Parkinson's disease-associated pathways and genes

doi: 10.3892/mmr.2017.6124

Figure Lengend Snippet: Summary of the 19 included microarray datasets.

Article Snippet: They were generated from 10 microarray platforms ( ): A-AFFY-33, A-AFFY-34, A-AFFY-37, A-AFFY-41, A-AFFY-44, A-AFFY-54 (Affymetrix; Thermo Fisher Scientific, Inc.), A-MEXP-1174 (Illumina, Inc., San Diego, CA, USA), A-AGIL-28 (Agilent), A-GEOD-17047 and A-AFFY-168 (Affy HUGENE; Affymetrix; Thermo Fisher Scientific, Inc.).

Techniques: Microarray

Journal: Molecular Medicine Reports

Article Title: Systematic analysis of microarray datasets to identify Parkinson's disease-associated pathways and genes

doi: 10.3892/mmr.2017.6124

Figure Lengend Snippet: Number of overlapping genes between platforms.

Article Snippet: They were generated from 10 microarray platforms ( ): A-AFFY-33, A-AFFY-34, A-AFFY-37, A-AFFY-41, A-AFFY-44, A-AFFY-54 (Affymetrix; Thermo Fisher Scientific, Inc.), A-MEXP-1174 (Illumina, Inc., San Diego, CA, USA), A-AGIL-28 (Agilent), A-GEOD-17047 and A-AFFY-168 (Affy HUGENE; Affymetrix; Thermo Fisher Scientific, Inc.).

Techniques:

Journal: Molecular Medicine Reports

Article Title: Systematic analysis of microarray datasets to identify Parkinson's disease-associated pathways and genes

doi: 10.3892/mmr.2017.6124

Figure Lengend Snippet: Clinical information for the 17 selected microarray datasets.

Article Snippet: They were generated from 10 microarray platforms ( ): A-AFFY-33, A-AFFY-34, A-AFFY-37, A-AFFY-41, A-AFFY-44, A-AFFY-54 (Affymetrix; Thermo Fisher Scientific, Inc.), A-MEXP-1174 (Illumina, Inc., San Diego, CA, USA), A-AGIL-28 (Agilent), A-GEOD-17047 and A-AFFY-168 (Affy HUGENE; Affymetrix; Thermo Fisher Scientific, Inc.).

Techniques: Microarray, Control

Journal: Molecular Medicine Reports

Article Title: Systematic analysis of microarray datasets to identify Parkinson's disease-associated pathways and genes

doi: 10.3892/mmr.2017.6124

Figure Lengend Snippet: Box plots for microarray data from 7 platforms. (A) AFFY-168. (B) AFFY-33. (C) AFFY-37. (D) AFFY-44. (E) AFFY-54. (F) AGIL-28. (G) GEOD-1704. (H) integrated microarray data.

Article Snippet: They were generated from 10 microarray platforms ( ): A-AFFY-33, A-AFFY-34, A-AFFY-37, A-AFFY-41, A-AFFY-44, A-AFFY-54 (Affymetrix; Thermo Fisher Scientific, Inc.), A-MEXP-1174 (Illumina, Inc., San Diego, CA, USA), A-AGIL-28 (Agilent), A-GEOD-17047 and A-AFFY-168 (Affy HUGENE; Affymetrix; Thermo Fisher Scientific, Inc.).

Techniques: Microarray