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Gene Expression Microarray Analysis

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Most cited protocols related to «Gene Expression Microarray Analysis»

1
Standardizing Microbial Abundance Data for Multivariate Analysis
In multivariate analyses such as PCA, large differences in variances between columns are corrected by standardizing each column; i.e. dividing each column by its standard deviation. Thus each column will have the same weight in the multivariate analysis. For OTU abundance tables, such a procedure is inappropriate as the disparities in column sums can be 100-fold. Methods based on chi-squared distances rather than variances deal with this by comparing weighted column profiles [62] , computed as relative abundances for each OTU within a column, with the overall column sum retained as a weighting factor. However, chi-square distances are sums of squares and can be overly sensitive to outliers and sequencing “jackpot” effects such as those occurring in pyrosequencing data [63] (link). Bray-Curtis distances can be a useful alternative, as it is based on the distance between profiles, as long as the differences in actual column sums are also accounted for in the final study. The other approach to the problem of disparities between column sums has been to subsample the over-abundant columns down to the same number as the smaller ones. However this results in a loss of information, rarely an optimal procedure in statistical contexts. This subsampling procedure is inspired by the popular idea of rarefaction in coverage studies first invented by Sanders [64] , but has yet to be proved beneficial for all microbial community structures. The parallels between gene expression microarray analyses and microbial abundance analyses was mentioned in [65] (link), which proposed several expression-inspired strategies for robustifying abundance measurements. The main points were that rankings and thresholding are important in the presence of noise and high variability in sequence depths. As in gene expression analysis filtering the OTUs is beneficial, especially in the latter multiple testing adjustments. The phyloseq package enables easy filtering and rank transformations in the same vein as robust multi-array averaging (rma) [66] (link). We provide further details in (McMurdie and Holmes, [67] ).
McMurdie P.J, & Holmes S. (2013). phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217.
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Publication 2013
factor A Gene Expression Microarray Analysis Gene Expression Profiling Microbial Community Structure Strains Veins
2
Missing Value Imputation in Proteomics
Single-value imputation refers to replacing missing values by a constant or a randomly selected value. These simple replacement procedures have been shown in microarray-based gene expression analyses to result in low performances when compared with other more advanced approaches;20 (link) however, these approaches may perform well in the presence of largely left-censored missing values and thus are evaluated here. Left-censoring means the values are missing from the low intensity (i.e., left tail) across the full distribution of possible measured intensity values. When data is censored in such a way, it is considered to be NMAR.
One approach to selecting a replacement value for a dataset is to use some minimal observed values estimated as the limit of detection (LOD). Half of the global minimum and half of the peptide minimum are common approaches currently used in the proteomics community to fill in missing values.40 ,41 (link) Half of the global minimum is defined as the minimal observed intensity value (not on the log scale) among all peptides (LOD1). The peptide minimum is the lowest intensity value observed for an individual peptide, and half of this value is referred to as LOD2. Random tail imputation (RTI) is based on the assumption that the entire proteomics dataset can be modeled by a single distribution and that the majority of the missing data are left-censored and can be drawn from the tail of the distribution.42 (link),43 (link) RTI computes the global mean and standard deviation of all observed values within the proteomics dataset, μ and σ, respectively. Peptide intensities are plotted as frequency histograms, and the missing values are then drawn from a truncated normal distribution to obtain values that are within with the left tail of the distribution, N(μ,σ) – k. The parameter k is selected as a maximum value that allows the imputed data to merge into the left tail of the base distribution N(μ,σ) without yielding a bimodal distribution. The parameter selection of k is based on recursive visualization of the imputed data at various values of k using histograms until a suitable value is achieved.
Webb-Robertson B.J., Wiberg H.K., Matzke M.M., Brown J.N., Wang J., McDermott J.E., Smith R.D., Rodland K.D., Metz T.O., Pounds J.G, & Waters K.M. (2015). Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics. Journal of proteome research, 14(5), 1993-2001.
Publication 2015
Gene Expression Microarray Analysis Peptides Tail
3
Vote Counting for Meta-analysis of Microarray Data
Vote counting is the most primitive but simplest and most intuitive method of meta-analysis. In the context of meta-analysis of microarray gene expression data, differential expression (DE) genes are first selected based on some criteria (e.g. adjusted P < 0.05) for each data set. The vote for each gene can then be calculated by counting the total number of times it occurs as DE across all data sets. This method is statistically inefficient and should be considered as a last resort in situations when other meta-analysis methods cannot be applied.
Xia J., Fjell C.D., Mayer M.L., Pena O.M., Wishart D.S, & Hancock R.E. (2013). INMEX—a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Research, 41(Web Server issue), W63-W70.
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Publication 2013
Gene Expression Gene Expression Microarray Analysis Genes
4
Temporal Transcriptome Profiling of C. elegans
Detailed information available in the Supplementary Methods and Materials. Caenorhabditis elegans strain Bristol N2 was grown on NGM at 20°C with E. coli OP50 as food source30 (link). Morphological changes in worms between the age of 48 and 67 hours after synchronisation were detected by analysing pictures of individual worms that were taken at a magnification of 100×. Samples for microarray analysis were drawn every hour between 44 and 58 hours after synchronisation by bleaching. RNA was isolated with either a RNEasy Micro Kit from Qiagen or with use of a Maxwell® 16 AS2000 instrument with a Maxwell® 16 LEV simplyRNA Tissue Kit (both Promega). After RNA isolation, one, two or three independent replicates per time point were analysed with microarrays (C. elegans (V2) Gene Expression Micorarray 4X44K slides from Agilent) following the ‘Two-Color Microarray-Based Gene Expression Analysis’-protocol from Agilent. Data were extracted with the Agilent Feature Extraction Software. All statistical analyses were performed in the ‘R’ statistical programming software (version 2.13.1 × 64). For normalization the Limma package was used with for Agilent array recommended settings31 (link). For k-means clustering, 20 iterations on 10 different starting situations were performed. Twelve k-means clusters were formed to be able to visualise gene expression changes properly. Enrichment studies were done using a hyper-geometric test. The physical distance from a gene to its closest neighbour within its k-means cluster was measured as the distance from start-site to start-site. The mutation frequency19 (link) of the genes from the k-means clusters was calculated using a permutation analysis. Heat-maps were constructed with the ‘heatmap’ function. Data of published gene expression studies were obtained from SPELL24 (link). Bacteria and genotypes for the bacterial food experiment are from20 (link). Data was stored in WormQTL32 (link)33 (www.WormQTL.org).
Snoek L.B., Sterken M.G., Volkers R.J., Klatter M., Bosman K.J., Bevers R.P., Riksen J.A., Smant G., Cossins A.R, & Kammenga J.E. (2014). A rapid and massive gene expression shift marking adolescent transition in C. elegans. Scientific Reports, 4, 3912.
Publication 2014
Bacteria Caenorhabditis elegans Escherichia coli Food Gene Expression Gene Expression Microarray Analysis Genes Genotype Helminths isolation Microarray Analysis Microtubule-Associated Proteins Mutation Promega Strains Tissues
5
Transcriptome Analysis of CTLA4 Blockade
Not a clear responder or non-responder, i.e. there was either a full regression of the indicator tumor but this was followed by tumor outgrowth within the observed time period (of at least 2 months), partial regression, delayed or slowed outgrowth or a tumor of <5 mm2 at time of surgery. These mice were excluded from subsequent analyses.
As a control group, tumours were removed 7 days after sham treatment with 100 μl PBS on day 6 after tumor inoculation.
We performed gene expression microarray analysis comparing anti-CTLA4-treated mice that had shown full regression of the contralateral tumor without reccurrence in the following 2 months (responders, n = 10) with mice that had continuous growth of the contralateral tumours (non-responders, n = 10). We used PBS-treated mice as control (n = 10).
Lesterhuis W.J., Rinaldi C., Jones A., Rozali E.N., Dick I.M., Khong A., Boon L., Robinson B.W., Nowak A.K., Bosco A, & Lake R.A. (2015). Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations. Scientific Reports, 5, 12298.
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Publication 2015
Cancer Vaccines CTLA4 protein, human Gene Expression Microarray Analysis Mus Neoplasms Placebos

Most recents protocols related to «Gene Expression Microarray Analysis»

1
Differential Gene Expression Analysis
Differential gene expression analyses for both microarray expression and RNA-seq data were performed using the limma (v3.42.2) package81 (link). For RNA-seq data, the read counts were first filtered to exclude nonexpressed genes, such that only genes were included for which at least three samples had a CPM (Counts Per Million) value above 1, i.e. genes for which sum(cpm>1)>=3. Secondly, read counts were normalized with respect to the trimmed mean of M-values (TMM82 (link)) via the calcNormFactors function from the edgeR (v3.28.1) package83 (link), and then finally further processed using the voom function from the limma package. If otherwise not indicated, Box-plots for illustrating differential gene expression between groups of samples were generated in R using standard settings, such that the center line represents the median expression within the group, the box limits correspond to versions of the 1st and 3rd quartile, respectively, whiskers indicate the most extreme data points that are at most 1.5 times the interquartile region (IQR) above or below the box respectively, and points outside the whiskers are considered outliers.
Mainwaring O.J., Weishaupt H., Zhao M., Rosén G., Borgenvik A., Breinschmid L., Verbaan A.D., Richardson S., Thompson D., Clifford S.C., Hill R.M., Annusver K., Sundström A., Holmberg K.O., Kasper M., Hutter S, & Swartling F.J. (2023). ARF suppression by MYC but not MYCN confers increased malignancy of aggressive pediatric brain tumors. Nature Communications, 14, 1221.
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Publication 2023
Gene Expression Gene Expression Microarray Analysis Genes RNA-Seq Venous Catheter, Central Vibrissae
2
Differential Gene Expression Analysis Workflows
In the analyses of microarray transcriptome datasets, the series matrix with probes was annotated into a gene expression matrix with the annotation document of the microarray platform. Differential gene expression analyses of microarray datasets were performed using the limma package.14 (link) In the analyses of RNA-seq transcriptome datasets, gene expression analyses with raw counts were first used if available, and differential expression analyses were performed using DESeq2.15 (link) For RNA-seq transcriptome datasets employing other data forms such as Fragments Per Kilobase Million or Transcripts Per Million, differential expression analyses were performed using the limma package.14 (link) In the differential gene expression analyses above, outcome lists of differentially expressed genes (DEGs) were obtained for subsequent analyses.
Zheng Q., Liu L., Wang B., He Y., Zhang M, & Shi G. (2023). Phosphorylated signal transducer and activator of transcription proteins 1 in salivary glandular tissue: an important histological marker for diagnosis of primary Sjögren’s syndrome. RMD Open, 9(1), e002694.
Publication 2023
Gene Expression Gene Expression Microarray Analysis Gene Expression Profiling Genes Microarray Analysis RNA-Seq Transcriptome
3
Standardized PAM50 Gene Expression Analysis
The levels of expression of the genes of prediction analysis of microarray 50
(PAM50) were normalized and standardized relative to five housekeepers, as
described.27 (link) The samples were classified using the published
PAM50 algorithm into breast cancer subtypes: luminal A, luminal B,
basal-like, HER2-enriched, normal-like, and not applicable (NA).
Shin J., Ham B., Seo J.H., Lee S.B., Park I.A., Gong G., Kim S.B, & Lee H.J. (2023). Immune repertoire and responses to neoadjuvant TCHP therapy in HER2-positive breast cancer. Therapeutic Advances in Medical Oncology, 15, 17588359231157654.
Publication 2023
ERBB2 protein, human Gene Expression Microarray Analysis Malignant Neoplasm of Breast Phenobarbital
4
Differential Gene Expression in TNBC
In order to identify consistently differentially expressed genes specific to TNBC compared to other types of breast cancer, we explored the publicly available transcriptomics data repository of the National Center for Biotechnology Information (NCBI), the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/, accessed on 2 January 2023)—a genomic data repository—for datasets of patients with breast cancer. For consistency, we selected publicly available datasets which can be analyzed using GEO2R, a built-in platform within NCBI GEO, to carry out differential gene expression analysis on microarray data. This platform utilizes the computer language R and the limma statistical package to carry out various statistical calculations, such as the empirical Bayes statistics, to identify genes that are differentially expressed between different patient groups.
The inclusion criteria for the datasets were: human sample sources, data type was expression profiling by microarray, and datasets had breast cancer patients with TNBC patients included. A total of nine datasets (n = 1027; TNBC n = 207) were used for analysis (Table 1). Patients of each respective dataset were grouped into two groups: a TNBC group and non-TNBC group. Figure 1 illustrates a simplified flowchart of the re-analysis process.
Kaddoura R., Alqutami F., Asbaita M, & Hachim M. (2023). In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers. Life, 13(2), 422.
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Publication 2023
Gene Expression Gene Expression Microarray Analysis Gene Expression Profiling Genes Genome Homo sapiens Malignant Neoplasm of Breast Microarray Analysis Patients
5
Breast Tissue Biopsy and Gene Expression Analysis
Three CNB specimens from each patient were prepared at baseline and on days 54–56 of treatment. We directed the biopsies stereo-tactically towards areas of the highest mammographic density of the upper outer quadrant of the left breast under local anesthesia on a prone table (LORAD) using a 14G needle and normal breast tissue was procured [32 (link)]. Detailed IHC data for Ki-67 and Bcl-2 have been published from the clinical trial [8 (link)].
One specimen stored in RNA-Later® was used for this current study, namely, gene expression analyses with microarray and Q-PCR according to the manufacturer’s instructions (Life Technologies Ltd., Paisley, UK).
Lalitkumar P.G., Lundström E., Byström B., Ujvari D., Murkes D., Tani E, & Söderqvist G. (2023). Effects of Estradiol/Micronized Progesterone vs. Conjugated Equine Estrogens/Medroxyprogesterone Acetate on Breast Cancer Gene Expression in Healthy Postmenopausal Women. International Journal of Molecular Sciences, 24(4), 4123.
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Publication 2023
BCL2 protein, human Biopsy Breast Gene Expression Microarray Analysis Local Anesthesia Needles Patients Tissues

Top products related to «Gene Expression Microarray Analysis»

1
Rneasy mini kit by Qiagen
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
2
Agilent one color microarray based gene expression analysis protocol by Agilent Technologies
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The Agilent One-Color Microarray-Based Gene Expression Analysis protocol is a laboratory procedure designed to analyze gene expression patterns. It utilizes microarray technology to measure the abundance of messenger RNA (mRNA) transcripts in a sample. The protocol outlines the steps required to label, hybridize, and scan samples on Agilent microarray platforms, providing a comprehensive workflow for gene expression profiling.
3
Trizol reagent by Thermo Fisher Scientific
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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Agilent 2100 bioanalyzer by Agilent Technologies
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5
Agilent dna microarray scanner by Agilent Technologies
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The Agilent DNA Microarray Scanner is a laboratory instrument designed to detect and quantify fluorescent signals on DNA microarray slides. It provides high-resolution scanning and accurate data capture for gene expression analysis and other genomic applications.
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Mrna only eukaryotic mrna isolation kit by Illumina
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The MRNA-ONLY™ Eukaryotic mRNA Isolation Kit is a laboratory tool designed to isolate and purify messenger RNA (mRNA) from eukaryotic cells or tissues. The kit utilizes specific chemistry and technologies to selectively capture and extract mRNA molecules, separating them from other cellular components such as ribosomal RNA and DNA.
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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.
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Feature extraction software by Agilent Technologies
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The Feature Extraction software is a tool used to analyze and process data from microarray experiments. It provides a standardized and automated method for extracting meaningful information from raw microarray image data.
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GeneSpring GX v12.1 is a software application designed for the analysis and visualization of gene expression data. It provides a comprehensive suite of tools for data normalization, statistical analysis, and clustering, enabling researchers to gain insights into complex biological systems.

More about "Gene Expression Microarray Analysis"

Gene expression microarray analysis is a powerful technique used to study the expression levels of thousands of genes simultaneously.
It involves the use of microarray technology, which allows for the measurement of the expression of multiple genes in a single experiment.
This approach is widely used in various fields, including biomedicine, genetics, and molecular biology, to investigate complex biological processes, identify disease-related genes, and develop personalized treatments.
The gene expression microarray analysis workflow typically involves several key steps, including sample preparation, RNA extraction, labeling, hybridization, and data analysis.
The RNeasy Mini Kit is a commonly used tool for RNA extraction, providing high-quality and purified RNA for downstream applications.
The Agilent One-Color Microarray-Based Gene Expression Analysis protocol outlines the steps for sample labeling and hybridization on microarray platforms, such as the Agilent DNA Microarray Scanner.
The TRIzol reagent is another popular tool for RNA extraction, while the Agilent 2100 Bioanalyzer is used to assess the quality and quantity of the extracted RNA.
The MRNA-ONLY™ Eukaryotic mRNA Isolation Kit can be employed to specifically isolate messenger RNA (mRNA) from total RNA samples.
Data analysis is a crucial aspect of gene expression microarray studies.
The Feature Extraction software is used to process the raw data obtained from the microarray scans, while the GeneSpring GX v12.1 software provides a comprehensive platform for data visualization, normalization, and statistical analysis.
These tools enable researchers to identify differentially expressed genes, cluster similar expression patterns, and uncover insights into complex biological pathways.
Overall, gene expression microarray analysis is a versatile and widely used technique that has transformed our understanding of gene regulation and its role in various biological processes.
By leveraging the power of PubCompare.ai, researchers can access the best protocols, methods, and products to ensure the reproducibility and accuracy of their gene expression microarray experiments, ultimately advancing their research and discoveries.