Factors affecting the yield of microRNAs from laser microdissectates of formalin-fixed tissue sections
© Patnaik et al; licensee BioMed Central Ltd. 2012
Received: 12 October 2011
Accepted: 19 January 2012
Published: 19 January 2012
Quantification of microRNAs in specific cell populations microdissected from tissues can be used to define their biological roles, and to develop and deploy biomarker assays. In this study, a number of variables were examined for their effect on the yield of microRNAs in samples obtained from formalin-fixed paraffin-embedded tissues by laser microdissection.
MicroRNA yield was improved by using cresyl violet instead of hematoxylin-eosin to stain tissue sections in preparation for microdissection, silicon carbide instead of glass fiber as matrix in RNA-binding columns, and overnight digestion of dissected samples with proteinase K. Storage of slides carrying stained tissue sections at room temperature for up to a week before microdissection, and storage of the microdissectates at room temperature for up to a day before RNA extraction did not adversely affect microRNA yield.
These observations should be of value for the efficient isolation of microRNAs from microdissected formalin-fixed tissues with a flexible workflow.
Laser microdissection (LMD)  is commonly used for the selective isolation of cell populations from tissues for molecular analyses. LMD is performed under microscopy, and cells are dissected out using a laser beam after they are identified by features such as histologic morphology. Quantification of the ultrashort, non-coding microRNAs in microdissected cells is an effective approach to understand the physiological roles of microRNAs [2–5] as well as to characterize microRNA dysregulation in diseases [6–10]. Unlike the much longer transcript mRNAs, microRNAs are resistant to fragmentation, and this permits the use of archived tissue material like formalin-fixed and paraffin-embedded (FFPE) specimens instead of fresh-frozen ones for reliable microRNA measurements for various studies [11–13]. Many of the variables that affect the recovery of microRNAs from macroscopic FFPE tissues have been identified [14–18]. However, the amount of cellular material obtained with LMD is minute, and the technique itself introduces conditions such as the presence of histologic dyes in the dissectates. In this study, we have examined some such factors of practical importance that can affect the yield and quality of microRNAs from LMD microdissectates of FFPE tissues for downstream analysis. One of the main advantages of developing biomarkers using microRNAs is the ability to use FFPE specimens. Therefore, our study focused completely on the use of FFPE specimens and no comparison to fresh frozen tissue was attempted.
Results and discussion
We obtained FFPE tissues of human lung cancers or their xenografts grown in mice for this work. Tissues were cut into 8 μm-thick sections, which were then placed on glass slides covered with polyethylene naphthalate (PEN) membrane. The sections were deparaffinized and stained with either hematoxylin and eosin (H&E), or cresyl violet (CV), and used for LMD within a day with a pulsed ultraviolet laser on a Leica® LMD6000 system. For some experiments, areas of tissue sections were dissected out along with PEN membrane by hand using a surgical blade. To obtain replicate samples, morphologically identical quadrants of stained serial sections were cut. Dissectates were lysed with proteinase K and total RNA was extracted by affinity chromatography using the Ambion® RecoverAll™ Total Nucleic Acid Isolation, or Norgen Biotek® FFPE RNA Purification kits that respectively use silica or glass fiber (GF), or carborundum or silicon carbide (SiC) as the RNA-binding matrix. Total RNA, with microRNA in an amount expected to be a constant proportion of that of total RNA, was eluted from columns using identical volumes of water, and quantified using RiboGreen dye in a fluorescence assay , or by measuring absorbance at 260 nm. Identical volumes of different RNA preparations were used for Applied Biosystems® TaqMan™ microRNA assays, based on reverse transcription-PCR (RT-PCR) , for microRNA miR-16, an abundant and ubiquitous microRNA (e.g., ), and RNU6-2 (U6B), a 45 base-long, housekeeping nucleolar RNA. Inter-group differences were analyzed using t tests assuming equal variances. P values determined in different statistical tests were two-tailed and a cut-off of 0.05 was used to appraise significance.
The efficacies of the two types of RNA-binding columns that used wither GF or SiC as the RNA-binding matrix were also compared. For this, proteinase K lysates were prepared from dissectates from three xenografts and divided into two equal portions, each of which was used for the two types of columns. As shown in Figure 2B, with CV-stained dissectates, RNU6-2 and miR-16 levels respectively were an average of 3.9 and 7.0 times higher with SiC columns than GF columns. When H&E was the stain, RNU6-2 and miR-16 levels respectively were on average 3.7 and 7.9 times higher with SiC columns than GF columns. These improvements in RNU6-2 and miR-16 yields, significant in paired t tests disregarding the histologic stain (both P values < 0.01), could be because of differences in column design and not necessarily because of a better efficacy of the SiC matrix per se. Because of convenience during the staining step and with assessment of histological morphology, we decided to use H&E stain for the rest of the experiments of this study. As the goal of the experiments was to assess improvements in yield, the influence of downstream variables such as those during poteinase K digestion or storage of the microdissectates was expected to not deny superiority of CV stain over H&E.
As expected from previous studies on RNA extraction from FFPE tissues (e.g., ), RNA yield improved significantly when the duration of proteinase K treatment was extended (Figure 3C). RiboGreen, RNU6-2 and miR-16 measurements respectively were on average 1.5, 2.3 and 1.3 times higher when the duration was increased to 3 h at 55°C from 15 min at 55°C followed by 15 min at 80°C (P values of 0.02, 0.04 and 0.36, respectively). Extending treatment time from 3 h to 20 h resulted in 1.7, 3.8 and 1.8 times higher RiboGreen, RNU6-2 and miR-16 measurements, respectively (P values of < 0.01, < 0.01 and 0.03, respectively).
To summarize, this study suggests that microRNA yields from LMD samples obtained from FFPE tissues can be improved by using CV instead of H&E as histologic stain, SiC instead of GF as matrix in RNA-binding columns, and overnight digestion with proteinase K. Storage of stained slides at room temperature for up to a week before LMD, and storage of LMD samples at room temperature for up to a day before RNA extraction does not seem to adversely affect microRNA yield. RNA prepared as per the methods used in this study, though containing DNA as well, appear to be suitable for microRNA quantification by RT-PCR or microarray hybridization. These observations should allow for efficient isolation of microRNAs from microdissectates prepared from FFPE tissues with a more manageable and flexible workflow.
The research presented here was approved under protocol ID I129008 by the Institutional Review Board of the Roswell Park Cancer Institute (RPCI). Informed consent specifically for this study was not obtained from the participants as such a requirement was waived under the protocol.
Tissues and microdissection
FFPE tissues of human non-small cell lung cancer and their xenografts in immunodeficient mice were kindly provided by, respectively, the core pathology facility of RPCI, and Dr. Bonnie Hylander of the Department of Immunology, RPCI. Tissue blocks were cut on a CUT4055 rotary microtome (Triangle Biomedical Sciences®, Durham, NC) into 8 μm-thick sections, which were placed on glass slides covered with a PEN membrane (Leica®, Wetzlar, Germany). Slides were dried overnight, de-paraffinized with xylene and rehydrated using a graded ethanol series (100%, 99%, 75%, and 50%, by volume in water) for staining with either CV (5 mg/ml in 20% ethanol and 1.5% acetic acid at pH 2.5; Ambion®, Austin, TX), or H&E using Harris hematoxylin (Polysciences®, Warrington, PA) followed by eosin Y (5 mg/ml; Fisher Scientific®, Pittsburgh, PA) according to protocols provided by the manufacturers. Slides were then dehydrated using a reverse graded ethanol series and xylene, and used for LMD within a day. LMD was performed with a pulsed ultraviolet laser on an LMD6000 system (Leica®) at 50×-200× magnification with laser power, speed and specimen-balance settings of 98, 2 and 11, respectively, in a room with > 35% humidity. Dissectates were collected in 0.5 ml polypropylene tubes. The duration of LMD to obtain a dissectate sample varied from 15 to 120 min. Dissectates were also obtained by manually excising tissue sections along with the PEN membrane with a scalpel blade. Morphologically identical quadrants of serial sections were cut for replicate samples. All work was done with precautions to maintain an RNAse-free environment.
Isolation of RNA
Total RNA was isolated using protocols and reagents supplied with the RecoverAll™ Total Nucleic Acid Isolation (product number AM1975; Ambion®), miRCURY™ Cell and Plant Tissue RNA Isolation (product number 300110; Exiqon®, Vedbaek, Denmark), and FFPE RNA Purification (product number 25300; Norgen Biotek®, Thorold, Canada) kits. All three kits contain spin columns with an RNA-binding matrix: ~0.01 g silica in case of RecoverAll™, and ~0.1 g SiC powder in the other two. The columns provided with the kits of Exiqon® and Norgen Biotek® are identical as Exiqon® procures the columns from Norgen Biotek®. Lysis of tissues and treatment with proteinase K at 55°C before a lysate was loaded on columns were done using reagents and instructions provided with the FFPE RNA Purification or the High Pure™ miRNA Isolation (product number 05 080 576 001; Roche®, Indianapolis, IN) kits. The concentration of proteinase K in the reactions set up as per the methods recommended for the two kits were 0.65 and 5.7 μg/μl respectively. Loading of lysates on a column and column washes were done using solutions and protocols supplied with the kit for that column. RNA was eluted from a column using either 50 or 100 μl water with the same volume used for all elutions in any given experiment.
Semi-quantification of RNAs by RT-PCR
TaqMan™ MicroRNA RT-PCR assay (Applied Biosystems®, Foster City, CA), with identification number 391, was used to measure microRNA miR-16. A similar assay was designed as per principles outlined in previous studies [20, 28], validated (see Additional file 2: Figure S2), and used to quantify the small nucleolar RNA RNU6-2 (also known as U6B). Sequences (and final concentrations in reactions) of the RT, and forward and reverse PCR primers, and the TaqMan™ probe were, respectively, GTCGTA TCCAGT GCAGGG TCCGAG GTATTC GCACTG GATACG ACAAAA ATAT (50 nM), GTGCAG GGTCCG AGGT (1 μM), GCAAGG ATGACA CGCAAA T (1 μM) and TATGGA ACGCTT CACGA (200 nM). For the RT-PCR assays, 5 μl each of RNA preparations were reverse transcribed using RNA-specific primers and reagents provided with the TaqMan™ MicroRNA Reverse Transcription Kit (Applied Biosystems®). RT reactions were used as templates in 40 cycle-PCR reactions on a 7900HT real-time PCR machine (Applied Biosystems®). Quantification cycle (C q ) values, approximately inversely proportional to log2 values of analyte RNA concentrations, were obtained with SDS™ software (version 2.4; Applied Biosystems®). The average of C q values of triplicate PCR reactions was used for analysis. C q values were > 40 for negative controls, for which RT reactions were performed without RNA. C q values were subtracted from 40 to obtain measurements directly proportional to log2 values of analyte RNA concentrations.
RNA quantification using RiboGreen assay
Nucleic acid concentration in RNA preparations was quantified in duplicate with Quant-it™ RiboGreen RNA reagent (Invitrogen®) as per the method suggested by the manufacturer. Yeast tRNA (Ambion®) was used to prepare standards of known RNA concentration. RNA samples (1-4 μl) were diluted to 100 μl using 10 mM tris hydrochloride with 1 mM ethylenediaminetetraacetic acid at pH 7.5 (CellGro®, Manassas, VA), and mixed with 100 μl of the buffer with 200- or 2000-fold diluted RiboGreen (for high- and low-range assays, respectively). Fluorescence at 535 nm following excitation at 485 nm was measured for 0.1 s on a Victor Wallac™ 1420 plate reader (Perkin Elmer®, Waltham, MA). Unknown RNA concentrations were extrapolated from standard curves generated for yeast tRNA.
Nuclease treatment of RNA preparations
Bovine pancreas RNAse A (DNAse- and proteinase-free) and recombinant DNAse I (RNAse-free) were obtained from Fermentas® (Glen Burnie, MD). Ten μl of nuclease reactions were set up at 37°C for 1 h using 1 U of either enzyme, buffer provided by Fermentas® for use with DNAse I, and 8 μl of RNA preparation containing < 0.1 μg RNA as per RiboGreen assay. Control reactions using yeast tRNA (0.1-0.2 μg) confirmed completeness of the RNAse reactions and absence of RNAse activity in the DNAse I stock.
MicroRNA profiling using LNA microarrays
This work was performed as a commercial service by Exiqon® (Vedbaek, Denmark) using their 6th generation miRCURY™ LNA™ microarrays. Each array had more than 2383 LNA capture probes for multiple RNAs of human, mouse, rat, and some viruses printed in quadruplicate on randomly distributed spots of 100 μm diameter with an inter-spot distance of 210 μm. A total of 1304 probes targeted 1291 human microRNAs, including 66 proprietary ones (miRPlus™, Exiqon®), and 23 non-microRNA human small RNAs with < 200 nucleotides, including the 5S ribosomal RNA and the RNU6-2 small nucleolar RNA (U6B). Every microRNA was recognized by only one of the 1276 probes for microRNAs. Eight probes recognized two microRNAs each, and three and six microRNAs were recognized by one probe each. For simplicity, the signals from such probes were considered as representing single microRNAs. Before hybridization to a microarray, 0.25 or 0.4 μg of an RNA sample, reduced in volume at room temperature in a speed-vacuum apparatus, and a human 'universal reference' total RNA preparation made by mixing the RNA pools provided in the FirstChoice® Human Total RNA Survey Panel (product number AM6000, Ambion®, Austin, TX) were 3'- or 5'-end-labeled with Cy3-like Hy3™ or Cy5-like Hy5™ (Exiqon®) dyes, respectively, using miRCURY™ LNA™ microRNA Hi-Power Labeling kits (Exiqon®). Microarrays were scanned for analysis using ImaGene® software (version 9; BioDiscovery®, Los Angeles, CA). Examinations of the scans and analyses of microarray signal values for 52 spiked-in synthetic, small RNAs showed that all labeling reactions and hybridizations were of good quality. Hy3™ and Hy5™ signal values were processed with the limma  Bioconductor package (version 3.6.9) for R (version 2.12). Correction for background noise was done using the normexp method  with an 'offset' value of 10, and was followed by within-array normalization using the global loess regression method with a 'span' value of 1/3 . Microarray signal values were then identified as summarized Hy3™ values which were the means of values from the quadruplicate probe-spots when the maximum was < 1.5 times the minimum, or the medians if otherwise. MicroRNAs recognized by probes for which the microarray signal values were > 3 times the summarized microarray signal value for probe-less empty microarray spots (1108 total) were considered as expressed.
Unless specified otherwise, statistical analyses and graphical plotting were done in Prism™ software (version 5.0 d; GraphPad Software®, La Jolla, CA), P value of 0.05 was the cut-off for deciding significance, and t tests were two-tailed, assumed equal variances, and used paired samples when possible.
Availability of supporting data
Both raw and processed microarray data can be obtained from the Gene Expression Omnibus repository of the National Center for Biotechnology Information, USA, with accession number GSE31946.
This work was supported by a Lung Cancer Promising Clinician Research Award (W81XWH-10-1-0614) from the United States Department of Defense, and a Buswell Foundation fellowship to SY. We thank Wiam Bshara, Zahra Fayazi, Angela Omilian and Melanie Kresin of the Department of Pathology, RPCI for slide preparation and assistance with LMD.
- Emmert-Buck MR, Bonner RF, Smith PD, Chuaqui RF, Zhuang Z, Goldstein SR, Weiss RA, Liotta LA: Laser capture microdissection. Science. 1996, 274 (5289): 998-1001. 10.1126/science.274.5289.998.PubMedView ArticleGoogle Scholar
- Balakrishnan A, Stearns AT, Park PJ, Dreyfuss JM, Ashley SW, Rhoads DB, Tavakkolizadeh A: MicroRNA mir-16 is anti-proliferative in enterocytes and exhibits diurnal rhythmicity in intestinal crypts. Exp Cell Res. 2010, 316 (20): 3512-3521. 10.1016/j.yexcr.2010.07.007.PubMedPubMed CentralView ArticleGoogle Scholar
- Junker A, Krumbholz M, Eisele S, Mohan H, Augstein F, Bittner R, Lassmann H, Wekerle H, Hohlfeld R, Meinl E: MicroRNA profiling of multiple sclerosis lesions identifies modulators of the regulatory protein CD47. Brain. 2009, 132 (Pt 12): 3342-3352.PubMedView ArticleGoogle Scholar
- Katakowski M, Zheng X, Jiang F, Rogers T, Szalad A, Chopp M: MiR-146b-5p suppresses EGFR expression and reduces in vitro migration and invasion of glioma. Cancer Invest. 2011, 28 (10): 1024-1030.View ArticleGoogle Scholar
- Zhang X, Ladd A, Dragoescu E, Budd WT, Ware JL, Zehner ZE: MicroRNA-17-3p is a prostate tumor suppressor in vitro and in vivo, and is decreased in high grade prostate tumors analyzed by laser capture microdissection. Clin Exp Metastasis. 2009, 26 (8): 965-979. 10.1007/s10585-009-9287-2.PubMedView ArticleGoogle Scholar
- Hannafon BN, Sebastiani P, de Las Morenas A, Lu J, Rosenberg CL: Expression of microRNA and their gene targets are dysregulated in preinvasive breast cancer. Breast Cancer Res. 2011, 13 (2): R24-10.1186/bcr2839.PubMedPubMed CentralView ArticleGoogle Scholar
- Buller B, Liu X, Wang X, Zhang RL, Zhang L, Hozeska-Solgot A, Chopp M, Zhang ZG: MicroRNA-21 protects neurons from ischemic death. FEBS J. 2010, 277 (20): 4299-4307. 10.1111/j.1742-4658.2010.07818.x.PubMedPubMed CentralView ArticleGoogle Scholar
- du Rieu MC, Torrisani J, Selves J, Al Saati T, Souque A, Dufresne M, Tsongalis GJ, Suriawinata AA, Carrere N, Buscail L, et al: MicroRNA-21 is induced early in pancreatic ductal adenocarcinoma precursor lesions. Clin Chem. 2010, 56 (4): 603-612. 10.1373/clinchem.2009.137364.PubMedView ArticleGoogle Scholar
- Glud M, Rossing M, Hother C, Holst L, Hastrup N, Nielsen FC, Gniadecki R, Drzewiecki KT: Downregulation of miR-125b in metastatic cutaneous malignant melanoma. Melanoma Res. 2010, 20 (6): 479-484. 10.1097/CMR.0b013e32833e32a1.PubMedView ArticleGoogle Scholar
- Gregg JL, Brown KE, Mintz EM, Piontkivska H, Fraizer GC: Analysis of gene expression in prostate cancer epithelial and interstitial stromal cells using laser capture microdissection. BMC Cancer. 2010, 10: 165-10.1186/1471-2407-10-165.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu A, Tetzlaff MT, Vanbelle P, Elder D, Feldman M, Tobias JW, Sepulveda AR, Xu X: MicroRNA expression profiling outperforms mRNA expression profiling in formalin-fixed paraffin-embedded tissues. Int J Clin Exp Pathol. 2009, 2 (6): 519-527.PubMedPubMed CentralGoogle Scholar
- Nonn L, Vaishnav A, Gallagher L, Gann PH: mRNA and micro-RNA expression analysis in laser-capture microdissected prostate biopsies: valuable tool for risk assessment and prevention trials. Exp Mol Pathol. 2010, 88 (1): 45-51. 10.1016/j.yexmp.2009.10.005.PubMedPubMed CentralView ArticleGoogle Scholar
- Li J, Smyth P, Flavin R, Cahill S, Denning K, Aherne S, Guenther SM, O'Leary JJ, Sheils O: Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 2007, 7: 36-10.1186/1472-6750-7-36.PubMedPubMed CentralView ArticleGoogle Scholar
- Doleshal M, Magotra AA, Choudhury B, Cannon BD, Labourier E, Szafranska AE: Evaluation and validation of total RNA extraction methods for microRNA expression analyses in formalin-fixed, paraffin-embedded tissues. J Mol Diagn. 2008, 10 (3): 203-211. 10.2353/jmoldx.2008.070153.PubMedPubMed CentralView ArticleGoogle Scholar
- Xi Y, Nakajima G, Gavin E, Morris CG, Kudo K, Hayashi K, Ju J: Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalin-fixed paraffin-embedded samples. RNA. 2007, 13 (10): 1668-1674. 10.1261/rna.642907.PubMedPubMed CentralView ArticleGoogle Scholar
- Bonin S, Hlubek F, Benhattar J, Denkert C, Dietel M, Fernandez PL, Hofler G, Kothmaier H, Kruslin B, Mazzanti CM, et al: Multicentre validation study of nucleic acids extraction from FFPE tissues. Virchows Arch. 2010, 457 (3): 309-317. 10.1007/s00428-010-0917-5.PubMedPubMed CentralView ArticleGoogle Scholar
- Okello JB, Zurek J, Devault AM, Kuch M, Okwi AL, Sewankambo NK, Bimenya GS, Poinar D, Poinar HN: Comparison of methods in the recovery of nucleic acids from archival formalin-fixed paraffin-embedded autopsy tissues. Anal Biochem. 2010, 400 (1): 110-117. 10.1016/j.ab.2010.01.014.PubMedView ArticleGoogle Scholar
- Arzt L, Kothmaier H, Quehenberger F, Halbwedl I, Wagner K, Maierhofer T, Popper HH: Evaluation of formalin-free tissue fixation for RNA and microRNA studies. Exp Mol Pathol. 2011, 91 (2): 490-495. 10.1016/j.yexmp.2011.05.007.PubMedView ArticleGoogle Scholar
- Jones LJ, Yue ST, Cheung CY, Singer VL: RNA quantitation by fluorescence-based solution assay: RiboGreen reagent characterization. Anal Biochem. 1998, 265 (2): 368-374. 10.1006/abio.1998.2914.PubMedView ArticleGoogle Scholar
- Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, et al: Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005, 33 (20): e179-10.1093/nar/gni178.PubMedPubMed CentralView ArticleGoogle Scholar
- Liang H, Li WH: Lowly expressed human microRNA genes evolve rapidly. Mol Biol Evol. 2009, 26 (6): 1195-1198. 10.1093/molbev/msp053.PubMedPubMed CentralView ArticleGoogle Scholar
- Rt A, Dineen SM, Craig RL, Guerrieri RA, Robertson JM: Comparison and evaluation of RNA quantification methods using viral, prokaryotic, and eukaryotic RNA over a 10(4) concentration range. Anal Biochem. 2009, 387 (1): 122-127. 10.1016/j.ab.2009.01.003.View ArticleGoogle Scholar
- Bevilacqua C, Makhzami S, Helbling JC, Defrenaix P, Martin P: Maintaining RNA integrity in a homogeneous population of mammary epithelial cells isolated by Laser Capture Microdissection. BMC Cell Biol. 2010, 11: 95-10.1186/1471-2121-11-95.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang WZ, Oeschger FM, Lee S, Molnar Z: High quality RNA from multiple brain regions simultaneously acquired by laser capture microdissection. BMC Mol Biol. 2009, 10: 69-10.1186/1471-2199-10-69.PubMedPubMed CentralView ArticleGoogle Scholar
- Clement-Ziza M, Munnich A, Lyonnet S, Jaubert F, Besmond C: Stabilization of RNA during laser capture microdissection by performing experiments under argon atmosphere or using ethanol as a solvent in staining solutions. RNA. 2008, 14 (12): 2698-2704. 10.1261/rna.1261708.PubMedPubMed CentralView ArticleGoogle Scholar
- Cummings M, McGinley CV, Wilkinson N, Field SL, Duffy SR, Orsi NM: A robust RNA integrity-preserving staining protocol for laser capture microdissection of endometrial cancer tissue. Anal Biochem. 2011, 416 (1): 123-125. 10.1016/j.ab.2011.05.009.PubMedView ArticleGoogle Scholar
- Okuducu AF, Janzen V, Hahne JC, Ko Y, Wernert N: Influence of histochemical stains on quantitative gene expression analysis after laser-assisted microdissection. Int J Mol Med. 2003, 11 (4): 449-453.PubMedGoogle Scholar
- Tang F, Hajkova P, Barton SC, Lao K, Surani MA: MicroRNA expression profiling of single whole embryonic stem cells. Nucleic Acids Res. 2006, 34 (2): e9-10.1093/nar/gnj009.PubMedPubMed CentralView ArticleGoogle Scholar
- Smyth G: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. edn. Edited by: Gentleman R, Carey VJ, Huber W, Dudoit S, Irizarry RA. 2005, New York: Springer, 397-420.View ArticleGoogle Scholar
- Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, Holloway A, Smyth GK: A comparison of background correction methods for two-colour microarrays. Bioinformatics. 2007, 23 (20): 2700-2707. 10.1093/bioinformatics/btm412.PubMedView ArticleGoogle Scholar
- Berger JA, Hautaniemi S, Jarvinen AK, Edgren H, Mitra SK, Astola J: Optimized LOWESS normalization parameter selection for DNA microarray data. BMC Bioinformatics. 2004, 5: 194-10.1186/1471-2105-5-194.PubMedPubMed CentralView ArticleGoogle Scholar
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