DNA microarray  DNA 微阵列

Duration: 1 minute and 24 seconds.1:24Subtitles available.CC

A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles (10⁻¹² moles) of a specific DNA sequence, known as probes (or reporters or oligos). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by detection of fluorophore-, silver-, or chemiluminescence-labeled targets to determine relative abundance of nucleic acid sequences in the target. The original nucleic acid arrays were macro arrays approximately 9 cm × 12 cm and the first computerized image based analysis was published in 1981.^[1] It was invented by Patrick O. Brown. An example of its application is in SNPs arrays for polymorphisms in cardiovascular diseases, cancer, pathogens and GWAS analysis. It is also used for the identification of structural variations and the measurement of gene expression.
DNA 微阵列（通常也称为 DNA 芯片或生物芯片）是一组附着在固体表面上的微观 DNA 点。科学家使用 DNA 微阵列来同时测量大量基因的表达水平或对基因组的多个区域进行基因分型。每个 DNA 点含有皮摩尔（10^-12 摩尔）的特定 DNA 序列，称为探针（或报告分子或寡核苷酸）。这些探针可以是基因或其他 DNA 元件的短片段，用于在高严格条件下与 cDNA 或 cRNA（也称为反义 RNA）样本（称为靶标）进行杂交。探针-靶标杂交通常通过检测荧光团、银或化学发光标记的靶标来检测和定量，以确定靶标中核酸序列的相对丰度。最初的核酸阵列是大约 9 厘米×12 厘米的宏观阵列，基于计算机图像的首个分析发表于 1981 年。它由 Patrick O. Brown 发明。其应用的一个例子是用于心血管疾病、癌症、病原体的多态性以及 GWAS 分析的 SNP 阵列。 它也用于结构变异的鉴定和基因表达的测量。

The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs. A high number of complementary base pairs in a nucleotide sequence means tighter non-covalent bonding between the two strands. After washing off non-specific bonding sequences, only strongly paired strands will remain hybridized. Fluorescently labeled target sequences that bind to a probe sequence generate a signal that depends on the hybridization conditions (such as temperature), and washing after hybridization. Total strength of the signal, from a spot (feature), depends upon the amount of target sample binding to the probes present on that spot. Microarrays use relative quantitation in which the intensity of a feature is compared to the intensity of the same feature under a different condition, and the identity of the feature is known by its position.
微阵列的核心原理是两条 DNA 链之间的杂交，即互补核酸序列通过形成氢键在互补核苷酸碱基对之间特异性配对的性质。核苷酸序列中互补碱基对的数量越多，两条链之间的非共价键结合就越紧密。在洗去非特异性结合序列后，只有强配对的链会保持杂交状态。与探针序列结合的荧光标记靶序列会产生信号，该信号取决于杂交条件（如温度）以及杂交后的洗涤。来自一个点（特征）的总信号强度取决于结合到该点上探针的靶样本量。微阵列使用相对定量，其中特征的强度与不同条件下相同特征的强度进行比较，并且特征的身份由其位置确定。

Many types of arrays exist and the broadest distinction is whether they are spatially arranged on a surface or on coded beads:
存在多种类型的阵列，最广泛的区别在于它们是在表面上还是在编码珠上空间排列：

• The traditional solid-phase array is a collection of orderly microscopic "spots", called features, each with thousands of identical and specific probes attached to a solid surface, such as glass, plastic or silicon biochip (commonly known as a genome chip, DNA chip or gene array). Thousands of these features can be placed in known locations on a single DNA microarray.
  传统的固相阵列是一组有序的微观“斑点”，称为特征，每个特征都有数千个相同且特定的探针附着在玻璃、塑料或硅生物芯片（通常称为基因组芯片、DNA 芯片或基因阵列）等固体表面上。数千个这样的特征可以放置在单个 DNA 微阵列上的已知位置。
• The alternative bead array is a collection of microscopic polystyrene beads, each with a specific probe and a ratio of two or more dyes, which do not interfere with the fluorescent dyes used on the target sequence.
  替代性珠阵列是一系列微小的聚苯乙烯珠子，每颗珠子都带有特定的探针和两种或更多染料的比率，这些染料不会干扰用于目标序列的荧光染料。

DNA microarrays can be used to detect DNA (as in comparative genomic hybridization), or detect RNA (most commonly as cDNA after reverse transcription) that may or may not be translated into proteins. The process of measuring gene expression via cDNA is called expression analysis or expression profiling.
DNA 微阵列可用于检测 DNA（如在比较基因组杂交中），或检测可能翻译也可能不翻译成蛋白质的 RNA（最常见的是逆转录后的 cDNA）。通过 cDNA 测量基因表达的过程称为表达分析或表达谱分析。

Applications include:   应用包括：

Application or technology 应用或技术	Synopsis   摘要
Gene expression profiling 基因表达谱分析	In an mRNA or gene expression profiling experiment the expression levels of thousands of genes are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on gene expression. For example, microarray-based gene expression profiling can be used to identify genes whose expression is changed in response to pathogens or other organisms by comparing gene expression in infected to that in uninfected cells or tissues.[2] 在 mRNA 或基因表达谱实验中，同时监测数千个基因的表达水平，以研究某些处理、疾病和发育阶段对基因表达的影响。例如，基于微阵列的基因表达谱分析可以通过比较感染与未感染的细胞或组织中的基因表达，来识别那些响应病原体或其他生物体的基因表达变化。 [2]
Comparative genomic hybridization 比较基因组杂交	Assessing genome content in different cells or closely related organisms, as originally described by Patrick Brown, Jonathan Pollack, Ash Alizadeh and colleagues at Stanford.[3][4] 评估不同细胞或密切相关生物体中的基因组内容，最初由斯坦福大学的 Patrick Brown、Jonathan Pollack、Ash Alizadeh 及其同事描述。 [3] [4]
GeneID   基因 ID	Small microarrays to check IDs of organisms in food and feed (like GMO [1]), mycoplasms in cell culture, or pathogens for disease detection, mostly combining PCR and microarray technology. 使用小型微阵列来检查食品和饲料中生物体的 ID（如转基因生物[1]）、细胞培养中的支原体或用于疾病检测的病原体，通常结合 PCR 和微阵列技术。
Chromatin immunoprecipitation on Chip 染色质免疫沉淀芯片	DNA sequences bound to a particular protein can be isolated by immunoprecipitating that protein (ChIP), these fragments can be then hybridized to a microarray (such as a tiling array) allowing the determination of protein binding site occupancy throughout the genome. Example protein to immunoprecipitate are histone modifications (H3K27me3, H3K4me2, H3K9me3, etc.), Polycomb-group protein (PRC2:Suz12, PRC1:YY1) and trithorax-group protein (Ash1) to study the epigenetic landscape or RNA polymerase II to study the transcription landscape. 与特定蛋白质结合的 DNA 序列可以通过免疫沉淀该蛋白质（ChIP）来分离，然后这些片段可以杂交到微阵列（如平铺阵列）上，从而确定蛋白质结合位点在整个基因组中的占据情况。例如，可以免疫沉淀组蛋白修饰（H3K27me3、H3K4me2、H3K9me3 等）、Polycomb-group 蛋白（PRC2:Suz12、PRC1:YY1）和 trithorax-group 蛋白（Ash1）来研究表观遗传景观，或免疫沉淀 RNA 聚合酶 II 来研究转录景观。
DamID	Analogously to ChIP, genomic regions bound by a protein of interest can be isolated and used to probe a microarray to determine binding site occupancy. Unlike ChIP, DamID does not require antibodies but makes use of adenine methylation near the protein's binding sites to selectively amplify those regions, introduced by expressing minute amounts of protein of interest fused to bacterial DNA adenine methyltransferase. 类似于 ChIP，可以通过分离与目标蛋白结合的基因组区域，并使用微阵列探测来确定结合位点的占据情况。与 ChIP 不同，DamID 不需要抗体，而是利用腺嘌呤甲基化在蛋白结合位点附近选择性扩增这些区域，通过表达微量与细菌 DNA 腺嘌呤甲基转移酶融合的目标蛋白来实现。
SNP detection  SNP 检测	Identifying single nucleotide polymorphism among alleles within or between populations.[5] Several applications of microarrays make use of SNP detection, including genotyping, forensic analysis, measuring predisposition to disease, identifying drug-candidates, evaluating germline mutations in individuals or somatic mutations in cancers, assessing loss of heterozygosity, or genetic linkage analysis. 识别群体内或群体间等位基因中的单核苷酸多态性。 [5] 微阵列的多种应用利用 SNP 检测，包括基因分型、法医分析、测量疾病易感性、识别候选药物、评估个体中的种系突变或癌症中的体细胞突变、评估杂合性丢失或遗传连锁分析。
Alternative splicing detection 选择性剪接检测	An exon junction array design uses probes specific to the expected or potential splice sites of predicted exons for a gene. It is of intermediate density, or coverage, to a typical gene expression array (with 1–3 probes per gene) and a genomic tiling array (with hundreds or thousands of probes per gene). It is used to assay the expression of alternative splice forms of a gene. Exon arrays have a different design, employing probes designed to detect each individual exon for known or predicted genes, and can be used for detecting different splicing isoforms. 外显子连接阵列设计使用针对预测基因外显子的预期或潜在剪接位点的特异性探针。其密度或覆盖范围介于典型的基因表达阵列（每个基因 1-3 个探针）和基因组平铺阵列（每个基因数百或数千个探针）之间。它用于检测基因的替代剪接形式的表达。外显子阵列具有不同的设计，采用设计用于检测已知或预测基因的每个单独外显子的探针，可用于检测不同的剪接异构体。
Fusion genes microarray 融合基因微阵列	A fusion gene microarray can detect fusion transcripts, e.g. from cancer specimens. The principle behind this is building on the alternative splicing microarrays. The oligo design strategy enables combined measurements of chimeric transcript junctions with exon-wise measurements of individual fusion partners. 融合基因微阵列能够检测融合转录本，例如来自癌症样本的转录本。其原理基于选择性剪接微阵列。寡核苷酸设计策略使得能够结合测量嵌合转录本连接点与对单个融合伙伴的外显子水平测量。
Tiling array  平铺阵列	Genome tiling arrays consist of overlapping probes designed to densely represent a genomic region of interest, sometimes as large as an entire human chromosome. The purpose is to empirically detect expression of transcripts or alternatively spliced forms which may not have been previously known or predicted. 基因组平铺阵列由重叠探针组成，旨在密集地代表感兴趣的基因组区域，有时甚至大到整个人类染色体。其目的是经验性地检测可能先前未知或未预测到的转录本或可变剪接形式的表达。
Double-stranded B-DNA microarrays 双链 B-DNA 微阵列	Right-handed double-stranded B-DNA microarrays can be used to characterize novel drugs and biologicals that can be employed to bind specific regions of immobilized, intact, double-stranded DNA. This approach can be used to inhibit gene expression.[6][7] They also allow for characterization of their structure under different environmental conditions. 右手双链 B-DNA 微阵列可用于表征可结合固定、完整双链 DNA 特定区域的新型药物和生物制剂。这种方法可用于抑制基因表达。 [6] [7] 它们还允许在不同环境条件下表征其结构。
Double-stranded Z-DNA microarrays 双链 Z-DNA 微阵列	Left-handed double-stranded Z-DNA microarrays can be used to identify short sequences of the alternative Z-DNA structure located within longer stretches of right-handed B-DNA genes (e.g., transcriptional enhancement, recombination, RNA editing).[6][7] The microarrays also allow for characterization of their structure under different environmental conditions. 左旋双链 Z-DNA 微阵列可用于识别位于右旋 B-DNA 基因较长片段中的替代 Z-DNA 结构的短序列（例如，转录增强、重组、RNA 编辑）。 [6] [7] 该微阵列还允许在不同环境条件下对其结构进行表征。
Multi-stranded DNA microarrays (triplex-DNA microarrays and quadruplex-DNA microarrays) 多链 DNA 微阵列（三链 DNA 微阵列和四链 DNA 微阵列）	Multi-stranded DNA and RNA microarrays can be used to identify novel drugs that bind to these multi-stranded nucleic acid sequences. This approach can be used to discover new drugs and biologicals that have the ability to inhibit gene expression.[6][7][8][9] These microarrays also allow for characterization of their structure under different environmental conditions. 多链 DNA 和 RNA 微阵列可用于识别与这些多链核酸序列结合的新型药物。这种方法可用于发现能够抑制基因表达的新药物和生物制剂。 [6] [7] [8] [9] 这些微阵列还允许在不同环境条件下对其结构进行表征。

Specialised arrays tailored to particular crops are becoming increasingly popular in molecular breeding applications. In the future they could be used to screen seedlings at early stages to lower the number of unneeded seedlings tried out in breeding operations.^[10]
针对特定作物定制的专用阵列在分子育种应用中越来越受欢迎。未来，它们可以用于在早期筛选幼苗，以减少在育种操作中尝试的不必要幼苗数量。 ^[10]

Microarrays can be manufactured in different ways, depending on the number of probes under examination, costs, customization requirements, and the type of scientific question being asked. Arrays from commercial vendors may have as few as 10 probes or as many as 5 million or more micrometre-scale probes.

Duration: 1 minute and 11 seconds.1:11

Microarrays can be fabricated using a variety of technologies, including printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing,^[11][12] or electrochemistry on microelectrode arrays.

In spotted microarrays, the probes are oligonucleotides, cDNA or small fragments of PCR products that correspond to mRNAs. The probes are synthesized prior to deposition on the array surface and are then "spotted" onto glass. A common approach utilizes an array of fine pins or needles controlled by a robotic arm that is dipped into wells containing DNA probes and then depositing each probe at designated locations on the array surface. The resulting "grid" of probes represents the nucleic acid profiles of the prepared probes and is ready to receive complementary cDNA or cRNA "targets" derived from experimental or clinical samples. This technique is used by research scientists around the world to produce "in-house" printed microarrays in their own labs. These arrays may be easily customized for each experiment, because researchers can choose the probes and printing locations on the arrays, synthesize the probes in their own lab (or collaborating facility), and spot the arrays. They can then generate their own labeled samples for hybridization, hybridize the samples to the array, and finally scan the arrays with their own equipment. This provides a relatively low-cost microarray that may be customized for each study, and avoids the costs of purchasing often more expensive commercial arrays that may represent vast numbers of genes that are not of interest to the investigator. Publications exist which indicate in-house spotted microarrays may not provide the same level of sensitivity compared to commercial oligonucleotide arrays,^[13] possibly owing to the small batch sizes and reduced printing efficiencies when compared to industrial manufactures of oligo arrays.

In oligonucleotide microarrays, the probes are short sequences designed to match parts of the sequence of known or predicted open reading frames. Although oligonucleotide probes are often used in "spotted" microarrays, the term "oligonucleotide array" most often refers to a specific technique of manufacturing. Oligonucleotide arrays are produced by printing short oligonucleotide sequences designed to represent a single gene or family of gene splice-variants by synthesizing this sequence directly onto the array surface instead of depositing intact sequences. Sequences may be longer (60-mer probes such as the Agilent design) or shorter (25-mer probes produced by Affymetrix) depending on the desired purpose; longer probes are more specific to individual target genes, shorter probes may be spotted in higher density across the array and are cheaper to manufacture. One technique used to produce oligonucleotide arrays include photolithographic synthesis (Affymetrix) on a silica substrate where light and light-sensitive masking agents are used to "build" a sequence one nucleotide at a time across the entire array.^[14] Each applicable probe is selectively "unmasked" prior to bathing the array in a solution of a single nucleotide, then a masking reaction takes place and the next set of probes are unmasked in preparation for a different nucleotide exposure. After many repetitions, the sequences of every probe become fully constructed. More recently, Maskless Array Synthesis from NimbleGen Systems has combined flexibility with large numbers of probes.^[15]

Two-color microarrays or two-channel microarrays are typically hybridized with cDNA prepared from two samples to be compared (e.g. diseased tissue versus healthy tissue) and that are labeled with two different fluorophores.^[16] Fluorescent dyes commonly used for cDNA labeling include Cy3, which has a fluorescence emission wavelength of 570 nm (corresponding to the green part of the light spectrum), and Cy5 with a fluorescence emission wavelength of 670 nm (corresponding to the red part of the light spectrum). The two Cy-labeled cDNA samples are mixed and hybridized to a single microarray that is then scanned in a microarray scanner to visualize fluorescence of the two fluorophores after excitation with a laser beam of a defined wavelength. Relative intensities of each fluorophore may then be used in ratio-based analysis to identify up-regulated and down-regulated genes.^[17]

Oligonucleotide microarrays often carry control probes designed to hybridize with RNA spike-ins. The degree of hybridization between the spike-ins and the control probes is used to normalize the hybridization measurements for the target probes. Although absolute levels of gene expression may be determined in the two-color array in rare instances, the relative differences in expression among different spots within a sample and between samples is the preferred method of data analysis for the two-color system. Examples of providers for such microarrays includes Agilent with their Dual-Mode platform, Eppendorf with their DualChip platform for colorimetric Silverquant labeling, and TeleChem International with Arrayit.

In single-channel microarrays or one-color microarrays, the arrays provide intensity data for each probe or probe set indicating a relative level of hybridization with the labeled target. However, they do not truly indicate abundance levels of a gene but rather relative abundance when compared to other samples or conditions when processed in the same experiment. Each RNA molecule encounters protocol and batch-specific bias during amplification, labeling, and hybridization phases of the experiment making comparisons between genes for the same microarray uninformative. The comparison of two conditions for the same gene requires two separate single-dye hybridizations. Several popular single-channel systems are the Affymetrix "Gene Chip", Illumina "Bead Chip", Agilent single-channel arrays, the Applied Microarrays "CodeLink" arrays, and the Eppendorf "DualChip & Silverquant". One strength of the single-dye system lies in the fact that an aberrant sample cannot affect the raw data derived from other samples, because each array chip is exposed to only one sample (as opposed to a two-color system in which a single low-quality sample may drastically impinge on overall data precision even if the other sample was of high quality). Another benefit is that data are more easily compared to arrays from different experiments as long as batch effects have been accounted for.

One channel microarray may be the only choice in some situations. Suppose ${\displaystyle i}$ samples need to be compared: then the number of experiments required using the two channel arrays quickly becomes unfeasible, unless a sample is used as a reference.

number of samples	one-channel microarray	two channel microarray	two channel microarray (with reference)
1	1	1	1
2	2	1	1
3	3	3	2
4	4	6	3
${\displaystyle i}$	${\displaystyle i}$	${\displaystyle i(i-1)/2}$	${\displaystyle i-1}$

This is an example of a DNA microarray experiment which includes details for a particular case to better explain DNA microarray experiments, while listing modifications for RNA or other alternative experiments.

1. The two samples to be compared (pairwise comparison) are grown/acquired. In this example treated sample (case) and untreated sample (control).
2. The nucleic acid of interest is purified: this can be RNA for expression profiling, DNA for comparative hybridization, or DNA/RNA bound to a particular protein which is immunoprecipitated (ChIP-on-chip) for epigenetic or regulation studies. In this example total RNA is isolated (both nuclear and cytoplasmic) by guanidinium thiocyanate-phenol-chloroform extraction (e.g. Trizol) which isolates most RNA (whereas column methods have a cut off of 200 nucleotides) and if done correctly has a better purity.
3. The purified RNA is analysed for quality (by capillary electrophoresis) and quantity (for example, by using a NanoDrop or NanoPhotometer spectrometer). If the material is of acceptable quality and sufficient quantity is present (e.g., >1μg, although the required amount varies by microarray platform), the experiment can proceed.
4. The labeled product is generated via reverse transcription and followed by an optional PCR amplification. The RNA is reverse transcribed with either polyT primers (which amplify only mRNA) or random primers (which amplify all RNA, most of which is rRNA). miRNA microarrays ligate an oligonucleotide to the purified small RNA (isolated with a fractionator), which is then reverse transcribed and amplified.
   • The label is added either during the reverse transcription step, or following amplification if it is performed. The sense labeling is dependent on the microarray; e.g. if the label is added with the RT mix, the cDNA is antisense and the microarray probe is sense, except in the case of negative controls.
   • The label is typically fluorescent; only one machine uses radiolabels.
   • The labeling can be direct (not used) or indirect (requires a coupling stage). For two-channel arrays, the coupling stage occurs before hybridization, using aminoallyl uridine triphosphate (aminoallyl-UTP, or aaUTP) and NHS amino-reactive dyes (such as cyanine dyes); for single-channel arrays, the coupling stage occurs after hybridization, using biotin and labeled streptavidin. The modified nucleotides (usually in a ratio of 1 aaUTP: 4 TTP (thymidine triphosphate)) are added enzymatically in a low ratio to normal nucleotides, typically resulting in 1 every 60 bases. The aaDNA is then purified with a column (using a phosphate buffer solution, as Tris contains amine groups). The aminoallyl group is an amine group on a long linker attached to the nucleobase, which reacts with a reactive dye.
     • A form of replicate known as a dye flip can be performed to control for dye artifacts in two-channel experiments; for a dye flip, a second slide is used, with the labels swapped (the sample that was labeled with Cy3 in the first slide is labeled with Cy5, and vice versa). In this example, aminoallyl-UTP is present in the reverse-transcribed mixture.
5. The labeled samples are then mixed with a proprietary hybridization solution which can consist of SDS, SSC, dextran sulfate, a blocking agent (such as Cot-1 DNA, salmon sperm DNA, calf thymus DNA, PolyA, or PolyT), Denhardt's solution, or formamine.
6. The mixture is denatured and added to the pinholes of the microarray. The holes are sealed and the microarray hybridized, either in a hyb oven, where the microarray is mixed by rotation, or in a mixer, where the microarray is mixed by alternating pressure at the pinholes.
7. After an overnight hybridization, all nonspecific binding is washed off (SDS and SSC).
8. The microarray is dried and scanned by a machine that uses a laser to excite the dye and measures the emission levels with a detector.
9. The image is gridded with a template and the intensities of each feature (composed of several pixels) is quantified.
10. The raw data is normalized; the simplest normalization method is to subtract background intensity and scale so that the total intensities of the features of the two channels are equal, or to use the intensity of a reference gene to calculate the t-value for all of the intensities. More sophisticated methods include z-ratio, loess and lowess regression and RMA (robust multichip analysis) for Affymetrix chips (single-channel, silicon chip, in situ synthesized short oligonucleotides).

The advent of inexpensive microarray experiments created several specific bioinformatics challenges:^[19] the multiple levels of replication in experimental design (Experimental design); the number of platforms and independent groups and data format (Standardization); the statistical treatment of the data (Data analysis); mapping each probe to the mRNA transcript that it measures (Annotation); the sheer volume of data and the ability to share it (Data warehousing).

Due to the biological complexity of gene expression, the considerations of experimental design that are discussed in the expression profiling article are of critical importance if statistically and biologically valid conclusions are to be drawn from the data.

There are three main elements to consider when designing a microarray experiment. First, replication of the biological samples is essential for drawing conclusions from the experiment. Second, technical replicates (e.g. two RNA samples obtained from each experimental unit) may help to quantitate precision. The biological replicates include independent RNA extractions. Technical replicates may be two aliquots of the same extraction. Third, spots of each cDNA clone or oligonucleotide are present as replicates (at least duplicates) on the microarray slide, to provide a measure of technical precision in each hybridization. It is critical that information about the sample preparation and handling is discussed, in order to help identify the independent units in the experiment and to avoid inflated estimates of statistical significance.^[20]

Microarray data is difficult to exchange due to the lack of standardization in platform fabrication, assay protocols, and analysis methods. This presents an interoperability problem in bioinformatics. Various grass-roots open-source projects are trying to ease the exchange and analysis of data produced with non-proprietary chips:

For example, the "Minimum Information About a Microarray Experiment" (MIAME) checklist helps define the level of detail that should exist and is being adopted by many journals as a requirement for the submission of papers incorporating microarray results. But MIAME does not describe the format for the information, so while many formats can support the MIAME requirements, as of 2007^[update] no format permits verification of complete semantic compliance. The "MicroArray Quality Control (MAQC) Project" is being conducted by the US Food and Drug Administration (FDA) to develop standards and quality control metrics which will eventually allow the use of MicroArray data in drug discovery, clinical practice and regulatory decision-making.^[21] The MGED Society has developed standards for the representation of gene expression experiment results and relevant annotations.

Microarray data sets are commonly very large, and analytical precision is influenced by a number of variables. Statistical challenges include taking into account effects of background noise and appropriate normalization of the data. Normalization methods may be suited to specific platforms and, in the case of commercial platforms, the analysis may be proprietary.^[22] Algorithms that affect statistical analysis include:

• Image analysis: gridding, spot recognition of the scanned image (segmentation algorithm), removal or marking of poor-quality and low-intensity features (called flagging).
• Data processing: background subtraction (based on global or local background), determination of spot intensities and intensity ratios, visualisation of data (e.g. see MA plot), and log-transformation of ratios, global or local normalization of intensity ratios, and segmentation into different copy number regions using step detection algorithms.^[23]
• Class discovery analysis: This analytic approach, sometimes called unsupervised classification or knowledge discovery, tries to identify whether microarrays (objects, patients, mice, etc.) or genes cluster together in groups. Identifying naturally existing groups of objects (microarrays or genes) which cluster together can enable the discovery of new groups that otherwise were not previously known to exist. During knowledge discovery analysis, various unsupervised classification techniques can be employed with DNA microarray data to identify novel clusters (classes) of arrays.^[24] This type of approach is not hypothesis-driven, but rather is based on iterative pattern recognition or statistical learning methods to find an "optimal" number of clusters in the data. Examples of unsupervised analyses methods include self-organizing maps, neural gas, k-means cluster analyses,^[25] hierarchical cluster analysis, Genomic Signal Processing based clustering and model-based cluster analysis. For some of these methods the user also has to define a distance measure between pairs of objects. Although the Pearson correlation coefficient is usually employed, several other measures have been proposed and evaluated in the literature.^[26] The input data used in class discovery analyses are commonly based on lists of genes having high informativeness (low noise) based on low values of the coefficient of variation or high values of Shannon entropy, etc. The determination of the most likely or optimal number of clusters obtained from an unsupervised analysis is called cluster validity. Some commonly used metrics for cluster validity are the silhouette index, Davies-Bouldin index,^[27] Dunn's index, or Hubert's ${\displaystyle \Gamma }$ statistic.
• Class prediction analysis: This approach, called supervised classification, establishes the basis for developing a predictive model into which future unknown test objects can be input in order to predict the most likely class membership of the test objects. Supervised analysis^[24] for class prediction involves use of techniques such as linear regression, k-nearest neighbor, learning vector quantization, decision tree analysis, random forests, naive Bayes, logistic regression, kernel regression, artificial neural networks, support vector machines, mixture of experts, and supervised neural gas. In addition, various metaheuristic methods are employed, such as genetic algorithms, covariance matrix self-adaptation, particle swarm optimization, and ant colony optimization. Input data for class prediction are usually based on filtered lists of genes which are predictive of class, determined using classical hypothesis tests (next section), Gini diversity index, or information gain (entropy).
• Hypothesis-driven statistical analysis: Identification of statistically significant changes in gene expression are commonly identified using the t-test, ANOVA, Bayesian method^[28] Mann–Whitney test methods tailored to microarray data sets, which take into account multiple comparisons^[29] or cluster analysis.^[30] These methods assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize type I and type II errors in the analyses.^[31]
• Dimensional reduction: Analysts often reduce the number of dimensions (genes) prior to data analysis.^[24] This may involve linear approaches such as principal components analysis (PCA), or non-linear manifold learning (distance metric learning) using kernel PCA, diffusion maps, Laplacian eigenmaps, local linear embedding, locally preserving projections, and Sammon's mapping.
• Network-based methods: Statistical methods that take the underlying structure of gene networks into account, representing either associative or causative interactions or dependencies among gene products.^[32] Weighted gene co-expression network analysis is widely used for identifying co-expression modules and intramodular hub genes. Modules may corresponds to cell types or pathways. Highly connected intramodular hubs best represent their respective modules.

Microarray data may require further processing aimed at reducing the dimensionality of the data to aid comprehension and more focused analysis.^[33] Other methods permit analysis of data consisting of a low number of biological or technical replicates; for example, the Local Pooled Error (LPE) test pools standard deviations of genes with similar expression levels in an effort to compensate for insufficient replication.^[34]

The relation between a probe and the mRNA that it is expected to detect is not trivial.^[35] Some mRNAs may cross-hybridize probes in the array that are supposed to detect another mRNA. In addition, mRNAs may experience amplification bias that is sequence or molecule-specific. Thirdly, probes that are designed to detect the mRNA of a particular gene may be relying on genomic EST information that is incorrectly associated with that gene.

Microarray data was found to be more useful when compared to other similar datasets. The sheer volume of data, specialized formats (such as MIAME), and curation efforts associated with the datasets require specialized databases to store the data. A number of open-source data warehousing solutions, such as InterMine and BioMart, have been created for the specific purpose of integrating diverse biological datasets, and also support analysis.

Advances in massively parallel sequencing has led to the development of RNA-Seq technology, that enables a whole transcriptome shotgun approach to characterize and quantify gene expression.^[36][37] Unlike microarrays, which need a reference genome and transcriptome to be available before the microarray itself can be designed, RNA-Seq can also be used for new model organisms whose genome has not been sequenced yet.^[37]

• An array or slide is a collection of features spatially arranged in a two dimensional grid, arranged in columns and rows.
• Block or subarray: a group of spots, typically made in one print round; several subarrays/ blocks form an array.
• Case/control: an experimental design paradigm especially suited to the two-colour array system, in which a condition chosen as control (such as healthy tissue or state) is compared to an altered condition (such as a diseased tissue or state).
• Channel: the fluorescence output recorded in the scanner for an individual fluorophore and can even be ultraviolet.
• Dye flip or dye swap or fluor reversal: reciprocal labelling of DNA targets with the two dyes to account for dye bias in experiments.
• Scanner: an instrument used to detect and quantify the intensity of fluorescence of spots on a microarray slide, by selectively exciting fluorophores with a laser and measuring the fluorescence with a filter (optics) photomultiplier system.
• Spot or feature: a small area on an array slide that contains picomoles of specific DNA samples.
• For other relevant terms see:
  • Glossary of gene expression terms
  • Protocol (natural sciences)

• Microarray Animation 1Lec.com
• PLoS Biology Primer: Microarray Analysis
• Rundown of microarray technology
• ArrayMining.net – a free web-server for online microarray analysis
• Microarray – How does it work?
• PNAS Commentary: Discovery of Principles of Nature from Mathematical Modeling of DNA Microarray Data
• DNA microarray virtual experiment