Correlations between RNA and protein expression profiles in 23 human cell lines
© Gry et al; licensee BioMed Central Ltd. 2009
Received: 3 March 2009
Accepted: 7 August 2009
Published: 7 August 2009
The Central Dogma of biology holds, in famously simplified terms, that DNA makes RNA makes proteins, but there is considerable uncertainty regarding the general, genome-wide correlation between levels of RNA and corresponding proteins. Therefore, to assess degrees of this correlation we compared the RNA profiles (determined using both cDNA- and oligo-based microarrays) and protein profiles (determined immunohistochemically in tissue microarrays) of 1066 gene products in 23 human cell lines.
A high mean correlation coefficient (0.52) was obtained from the pairwise comparison of RNA levels determined by the two platforms. Significant correlations, with correlation coefficients exceeding 0.445, between protein and RNA levels were also obtained for a third of the specific gene products. However, the correlation coefficients between levels of RNA and protein products of specific genes varied widely, and the mean correlations between the protein and corresponding RNA levels determined using the cDNA- and oligo-based microarrays were 0.25 and 0.20, respectively.
Significant correlations were found in one third of the examined RNA species and corresponding proteins. These results suggest that RNA profiling might provide indirect support to antibodies' specificity, since whenever a evident correlation between the RNA and protein profiles exists, this can sustain that the antibodies used in the immunoassay recognized their cognate antigens.
The Central Dogma of molecular biology, states that "DNA makes RNA makes proteins" suggesting there is a direct relationship between mRNA and protein levels. This assumed relationship is the basis for numerous transcript-profiling experiments, often based on microarray analysis to identify genes that are up- and down-regulated under normal or disease conditions. The underlying assumption is that differences in mRNA levels are manifested in different phenotypes as a result of differences in protein levels. Accordingly, correlations between the differential expression of specific mRNAs and corresponding proteins have been found in numerous studies , many of which have been shown to have clear biological relevance [2, 3]. Several studies have also found significant general correlations between RNA levels and protein levels [4–10], usually using data on RNA abundance acquired from platforms such as microarrays and Serial Analysis of Gene Expression (SAGE), in conjunction with data on the abundance of corresponding proteins derived from mass spectrometry (MS) analyses.
The major conclusions drawn from these studies have been that there are significant general correlations between levels of RNA species and corresponding protein products, but also considerable variation in these correlations. For instance, Lu et al found significant correlations between RNA and protein levels of 0.66 and 0.48 in two simple, unicellular organisms (yeast and Escherichia coli ), but indications that the number of proteins per transcript vary widely. In the cited study MS data and a trained classifier were used to obtain accurate estimates of protein abundance in the complex samples, microarrays were used to determine RNA levels, and products of 346 and 437 genes were used in the yeast and E. coli correlation analyses, respectively. Further, in a study published in 1999, Gygi et al investigated 150 genes using SAGE, 2D-gels and MS data, and found a correlation of 0.91 for all analyzed genes, but when a few highly expressed RNA and protein products were excluded the correlation decreased to 0.36 .
Similarly, in an analysis of NCI-60 cell lines based on RNA and reverse phase protein arrays, Shankavaram et al found a significant mean correlation between RNA and protein levels, and showed that the correlations were substantially stronger for some gene categories than others . They also found that the distribution of correlation coefficients is bimodal; one group of gene products had a mean correlation of 0.71, while another group had a mean correlation of 0.28. Further, Gene Ontology theme enrichment analysis indicated that the genes with high correlations were mainly involved in the maintenance of cellular processes and structural properties. Greenbaum et al have also shown that gene products associated with certain characteristics, such as high Codon Adaptation Indices (CAI) and/or ribosomal occupancy, seem to have significantly higher correlations with corresponding proteins than the main population of gene products .
Thus, interesting data on the degrees of correlation between mRNA and protein levels in various organisms have been acquired, and intriguing variations in this respect between different sets of genes have been detected. However, although MS can provide quantitative data, it has been a bottleneck in analyses of large numbers of gene products. Hence, although several hundred gene products were analyzed in some of the cited studies they still covered small proportions of the total analyzed genomes, so the general conclusions should be treated with some caution. Thus, general patterns of correlation between mRNA and protein levels have not yet been fully established, raising questions about the validity of large-scale comparative mRNA and protein expression profiling, and true, global patterns of relationships between levels of mRNAs and proteins encoded by specific genes still remain to be elucidated. Therefore, in an attempt to compare mRNA and protein levels at a larger scale we have analyzed RNA and protein expression profiles, using cDNA and oligo array data in conjunction with immunohistochemical data, in 23 human cell lines
Examples of correlation coefficients
Selection of the analyzed genes
After strict quality filtering of the three expression datasets (see Materials and methods for details), data on levels of 1066 gene products with unique Ensembl gene IDs provided by all three platforms remained. The effects of applying different filtering criteria are illustrated by the oligo array data shown in Additional file 2, which indicates that the stringency of the filtration (in terms of permitted numbers of missing data points for specific genes and cell lines included in the analysis) has minor effects on the mean correlation coefficient. However, the highest mean correlation coefficients were obtained in all three comparisons when no missing values were accepted, so only profiles for which data from all cell lines were available from each of the platforms were used. Other tested options were to include average expression values for gene products that had more than one counterpart in the data yielded from another platform across all the cell lines, or the best matched pairs, based on either sequence similarities or correlation coefficients. However, the distributions of correlation coefficients obtained with these approaches did not significantly differ from those yielded by averaging the expression values obtained with multiple probes.
High proportions of the 1066 gene products for which data were available in all three of the filtered datasets were detected by a single probe or antibody (75%, 55% and 39% in the Protein, Oligo and cDNA datasets, respectively). Two or more representatives of the remaining gene products were detected, i.e. replicates resulting in multiple data points (Additional file 3).
Correlations between RNA and protein levels
Genes with correlated RNA and protein expression levels
Gene ontology analysis
Analysis of the cellular compartment and biological process Gene Ontology (GO) themes  of the gene products with correlation coefficients >0.445 identified in the three RNA-RNA and RNA-protein comparisons described above yielded varying results, and only a few significantly enriched GO themes were detected (after adjustment for multiple testing). It should be noted that since the dataset is rather small for such hypergeometric statistical tests the results are highly sensitive to relatively minor variations. However, among the 169 common Ensembl gene IDs, significant enrichment was found of genes associated with the cytoskeleton and adherent junctions in the cellular compartment ontology analysis, and of genes associated with cellular motility and other maintenance-related categories in the biological function ontology analysis (Additional file 5 and 6).
To assess the possibility that the correlation coefficients could be dependent on the RNA array signal intensity, linear regression was applied to the mean signal intensities and correlation coefficients. Using all 1066 gene product pairs a positive, but weak relationship was found (m = 0.034, p = 4.39e-05) in values obtained from the RNA oligo assay, indicating that an increase in signal intensity slightly increased the correlation. The corresponding relationship for the results from the cDNA assay was extremely weak (m = 0.15e-05, p = 0.98), indicating that variations in the signal intensity had virtually no effect on correlations of the data provided by the cDNA arrays. Hence, the oligo microarray analysis yielded larger differences in correlations obtained with high and low intensity probes than the cDNA analysis.
Global expression profiling
RNA assay validation using real time reverse transcription polymerase chain reactions
In order to validate the results from the two RNA microarray assays, Real time reverse transcription polymerase chain reaction (RT-PCR) analysis was applied, in which products of 14 genes were measured in duplicates across eight cell lines. The correlations obtained for all 14 genes in a subsequent RT-PCR-oligo RNA array comparison (which ranged from -0.26 to 1) and for 10 genes in an RT-PCR-cDNA comparison (which ranged from -0.6 to 0.97) are shown in Additional file 8. In addition, the correlation between 7 different genes and the corresponding protein levels was calculated (Additional file 8), where the mean correlation was 0.58. Further, for four genes per RNA assay linear regression was applied to the correlation coefficients of the RNA array versus RT-PCR results, and the RNA array versus protein data. The slope obtained from the regression analysis was significantly positive (p-value = 0.01), indicating that there was a significant relationship between these correlations (Additional file 9).
To assess the fundamentally important correlation between levels of RNA species and corresponding proteins accurately, reliable estimates of their abundance are clearly required. Equally clearly, quantitative methods that yield highly accurate, absolute estimates of their levels would ideally be applied, and currently the method of choice for quantitative proteomics is mass spectrometry, following several purification and separation steps. This approach can provide high levels of accuracy, sensitivity and specificity, but as yet it is not suitable for large-scale analyses. Alternatively, as in this study, relative levels of proteins across samples of multiple cell lines in tissue microarrays can be determined immunohistochemically, minimizing inter-experimental variation by simultaneously staining samples of all of the lines by each antibody. In addition, various types of microarrays have been developed recently that are capable of providing reliable estimates, in conjunction with various statistical models, of absolute quantities of specific mRNA species in samples from spot intensities .
Thus, use of relative techniques like two-color microarray and immunohistochemistry allows levels of large numbers of gene products to be compared in multiple samples. However, it should be recognized that cell lines are model systems that differ in various respects from cells in the organisms from which they are derived, notably many of the regulatory pathways are not present and the chromosomal arrangements are beyond the normal patterns in healthy tissues . So, findings regarding correlations between RNA and corresponding protein levels in them should be interpreted with some caution. Furthermore, since the abundance of RNA and protein is analyzed in samples of cell lines containing several cells, the values used in subsequent correlation analysis are based on averages for the cell line populations, which may be in varying stages of the cell cycle.
Bearing in mind the above provisos, the distributions of the correlation coefficients obtained in both the cDNA and oligo microarray data comparisons with the protein dataset are approximately normal distributed, although when investigating the density function of the distribution there is a tendency towards a minor peak around a mean value of 0.65–0.75, implying that the gene products can may be divided into two major groups that have different degrees of correlation. Further, the minor peak is enhanced when the correlations are based on Pearson correlation coefficients. Shankavaram et al noticed a similar pattern in their study of NCI-60 mammalian cell lines . In contrast, the distribution of cDNA versus oligo microarray correlations had more of a beta shape, indicating that data generated from many pairs of corresponding probes in the two array systems strongly correlated, but some pairs yielded results that correlated poorly, which decreased the mean correlation coefficient. This may have been due to poor sequence overlap, i.e. the probes yielding poor correlations may have hybridized to different parts of transcripts that mapped to the same genes according to data in the Ensembl gene database. The degree of correlation between the cDNA and oligo microarray datasets is consistent with the degrees found in previous analyses , but further evaluation of variations between the results of this and previous studies in this respect is beyond the scope of this article. The oligo microarray assay yielded higher correlation coefficients with the protein data than the cDNA microarray assay, probably because the oligo probes had higher specificity, in accordance with expectations due to the lower degree of cross hybridization that generally occurs when shorter probes are used.
The major and minor peaks in the in the histograms of the correlation coefficients between the oligo microarray and protein profiles may correspond to two groups of genes that are regulated by different mechanisms. The genes with high correlations may be regulated solely, or almost solely, at the transcriptional level, in accordance with evidence from the ontological analysis that high proportions of these genes are involved in cellular processes and maintenance, for which there is likely to be little need for complex regulation. In contrast, the weak correlations of the other sets of genes may be due to the effects of complex regulatory mechanisms and/or noise generated in the assays masking subtle changes in mRNA transcripts and protein levels, thereby weakening the correlations.
The weak correlations for gene products with correlation coefficients lower than 0.445 probably have several causes, including various post-transcriptional processes that complicate attempts to obtain accurate estimates of quantities of corresponding mRNAs across the cell lines that are destined for translation. For instance, some mRNAs are strongly retained in the nucleus, which may lead to their levels being over-estimated relative to protein levels. Technical noise generated by the respective platforms (notably due to cross-hybridization in the DNA microarray analyses and variations in the affinity and specificity of the antibodies used in the immunoassays) may also weaken the correlations, and thus increase the proportions of genes with correlation coefficients lower than 0.445. The reason that no correlation was found for certain genes is probably related to the complexity of their regulatory mechanisms, which may weaken their correlations to levels that are not detectable with current techniques, while genes with strong correlations may be regulated solely at the transcriptional level.
The concordance between estimates of RNA levels obtained from the array analyses and the RT-PCR analyses was found to be positively correlated to the correlations found between the RNA and protein levels, but the quantities of transcripts estimated by the RT-PCR assay was not similarly related to the RNA-protein correlations. The number of samples is too small to draw definitive conclusions, but these results suggest that if the accuracy of RNA estimations is increased (based on the correlation with the RT-PCR assay), the correlation between RNA and protein levels is more likely to be high (Additional file 9). In addition, the analysis of RNA levels estimated by RT-PCR showed that the mean correlation is higher than the array based platforms, albeit the number of samples in the analysis is also small. This implies that a more accurate estimate of the RNA levels is likely to increase the overall correlation, and that the cause of low correlation are mainly caused by variable accuracy on the RNA level and not the protein estimates.
We have shown here that the correlation coefficients between RNA and protein profiles for 1066 gene products across 23 cell lines vary widely. The mean correlation coefficient is ~0.3, but the groups of genes represented by a major peak at mean value ~0.3 and a minor peak at mean value 0.65–0.75 have significantly different mean values, which may reflect differences in their regulatory mechanisms. Utilizing RNA data from two independent microarray formats, and immunohistochemical data obtained using antibodies applied in the Human Protein Atlas initiative, we found significant correlations between the RNA and protein profiles of 33% of the gene products. Although transcriptional profiling cannot be considered a high-throughput approach for the validation of affinity reagents, when correlation measurements between RNA and protein levels are available they provide additional information regarding the performance of employed antibodies. Further, when the RNA estimates are highly accurate the correlation between RNA and protein levels has a tendency to increase. However, while high correlation values might support antibody specificities, observed discrepancies between RNA and protein levels do not necessarily imply that the antibodies perform poorly, since they could be due to various biological factors, such as complex gene regulatory mechanisms.
The data on gene expression at the RNA level were acquired using microarray technology, as follows. RNA from each of the 23 cell lines was hybridized to internally produced oligo arrays and cDNA microarrays, spotted onto UltraGAPS slides (Corning). The oligos were the human 3.0 set from Operon (Array Express: A-MEXP-706), containing ~37000 probes, representing ~24600 unique genes, while the cDNA microarrays contained ~30000 probes representing ~11800 unique genes (Array Express: A-MEXP-250). The cell lines were hybridized on duplicate (oligo) and duplicate/triplicate (cDNA) arrays. Each cell line was hybridized with Stratagene universal reference RNA, and for each sample 20 μg of RNA was primed with 5 μg random hexamers (Invitrogen). The volume of each sample was adjusted to 18.4 μl using DEPC-treated water. The RNA was denatured at 70°C for 10 minutes, and then renatured on ice for 5 minutes. Reverse-transcription reaction mixture (Invitrogen) and 400 units of Superscript III RT-polymerase were added to yield a final volume of 30 μl containing 1× first-strand buffer (Invitrogen), 0.01 mM DDT (Invitrogen) and 0.5 mM dNTPs (Sigma-Aldrich). The ratio of aminoallyl-modified dUTP to dTTP was 4:1 in the dNTP mixture. The samples were incubated at 25°C for 10 minutes followed by 46°C for 2 hours. The cDNA synthesis was halted by adding 3 μl 0.2 M EDTA (pH 8.0).
Template RNA was removed by adding 4.5 μl 1 M NaOH. The samples were incubated at 70°C for 15 minutes, and then chilled to room temperature, neutralized with 4.5 μl 1 M HCl and purified using the MinElute Reaction Cleanup system (Qiagen), following the manufacturer's recommendations, except that the wash and elution buffers provided with the system were replaced by 80% ethanol and 100 mM NaHCO3 (pH 9.0), respectively. The elution step from the column was repeated, generating an eluate of 20 μl. This was mixed with a tenth of the contents of a monofunctional NHS-ester Cy3 or Cy5 dye tube (GE Healthcare), which had been dissolved in DMSO and subsequently dried in a vacuum centrifuge. After 30 minutes incubation in darkness at room temperature, the samples to be hybridized were purified using MinElute columns as instructed by the manufacturer.
Hybridization of samples
The microarray slides were pre-hybridized for 30 minutes at 42°C in a pre-hybridization solution consisting of 5× SSC, 0.1% SDS (Sigma-Aldrich) and 1% BSA (Sigma-Aldrich) to avoid unspecific hybridization to the glass surface. The slides were subsequently washed in water and isopropanol (Sigma-Aldrich), then dried using a slide centrifuge. The labelled (Cy5) and reference (Cy3) samples were pooled and denatured (3 minutes at 95°C) in a hybridization mixture containing 25% formamide (Sigma-Aldrich), 5× SSC and 0.1% SDS. The mixture was introduced under a lifter-slip cover slip (Erie Scientific) placed on top of the printed array and hybridized for 18–24 hours at 42°C in a water bath. Following hybridization the slides were washed with increasing stringency using 2× SSC and 0.1% SDS at 42°C, followed by 0.1× SSC and 0.1% SDS at room temperature and finally by five repeated washes with 0.1× SSC at room temperature.
Following hybridization the arrays were scanned at 10 μm resolution using an Agilent G2565BA scanner (Agilent Technologies, Santa Clara, CA, USA), with the photomultiplier set to 100% for each laser. The acquired images were analyzed using the irregular gridding algorithm in GenePix Pro 5.1 (Molecular Devices), and the resulting data were imported into the R environment for statistical processing and visualization . The intensities were extracted from the median foreground intensity in the 532 nm and 635 nm channels. The features were filtered based on the data from GenePix and manual inspection of the slides, by removing spots that were either not found by the image software or were marked as bad spots due to the presence of dust particles or contact with adjacent spots on the array.
The intensities of signals from features within each array were normalized by print-tip Lowess normalization [21, 22]. The log2 values of the ratios of the two normalized intensities (abbreviated M value, 1/2(log2(F635/F532)) and the product of the intensities (abbreviated A value, 1/2(log2(F532 * F635))) for all features were then calculated. The intensities were also normalized across arrays using a median absolute deviation scaling method .
The DNA microarray data have been submitted to Array Express.
Cell microarray production
The cell samples were assembled in a cell microarray (CMA) as previously described by Andersson et al. . Briefly, cells were fixed in formalin and dispersed in agarose. The generated cell pellets were then histoprocessed and embedded in paraffin, resulting in donor blocks for CMA production. From each cell donor block duplicate 0.6 mm punches were taken and placed in one recipient CMA.
Immunohistochemistry and image analysis
As previously described by Stromberg et al., antibodies generated in the Human Protein Atlas project were used for immunohistochemical staining of CMA sections . All stained CMA sections were scanned using a Scanscope T2 automated slide-scanning system (Aperio Technology) and generated TIFF images representing separated cell spots were analyzed using TMAx automated image analysis software (Beecher Instruments). The software automatically identifies cells and detects immunostaining, generating an output file containing information about staining intensity, fractions of positive cells, numbers of cells present per spot etc.
Protein quantification scores were calculated using TMAx output parameters of staining intensity per unit area and the number of cells present in each cell spot. Spots with insufficient cells (<20) were excluded from further analysis. Since the staining intensity reflects the amount of protein present in a cell, signals from areas in each cell with weak, moderate and strong staining were summed, weighting moderate and strong signals with arbitrary coefficients of 2 and 3, respectively. The parameters used for determining the cut-off levels for each staining category were jointly determined by experienced pathologists and the software developer. The summed values were then divided by the number of cells present in the respective spots, generating average values of protein expression level per cell. In order to correct for bias introduced by the correlation between cell size and the level of protein expression, as described by Lundberg et al. , the protein expression levels obtained per cell were adjusted with respect to cell size. Using image analysis data, the average cross-sectional area for each cell line was calculated from 100 CMAs, and by setting the cell size of the largest cell to 1, a relative average size for each cell type was computed. Finally, values of protein expression level were divided by the relative average cell sizes, yielding apparent protein concentration values.
In this study, datasets from two RNA platforms (oligo and cDNA microarrays), and one protein (immunohistochemical) dataset were obtained. To enable RNA and protein levels to be compared the gene products must have corresponding identities. Therefore, matrices containing the intensity data for corresponding gene products, based on shared Ensembl Gene IDs, were compiled. In some cases the overlap of the IDs was not 1:1, but instead one Ensembl gene ID identified in the data obtained from one platform had multiple counterparts in the data obtained from another platform. In such cases values for the multiple hits were averaged. In addition, an analysis was performed in which only the most strongly correlating Ensembl pairs, from a collection of pairs with multiple, matching Ensembl IDs, were utilized. This analysis did not yield any significant differences in overall mean correlation coefficients compared to the method in which averages were used. For the respective platform, the intensity values were measured, more specifically, log 2 ratios (M-values) of the intensity values from the cDNA and oligo microarray datasets, and the intensities quantified by the TMAx software for the immunohistochemical data, were used in the correlation analysis
All datasets were filtered based on Non Available values (NAs), in which each Ensembl gene ID had to have representative values for each cell line, or else it was discarded in the subsequent analysis. The effect of the filtering is illustrated in Additional file 2.
Hence, three matrices were constructed with data for 1066 Ensembl gene IDs in 23 cell lines that were present in the datasets obtained from all three platforms. The Spearman's Rho correlation was then calculated for each Ensembl gene ID pair between the RNAcDNA-RNAoligo, RNAcDNA -protein and RNAcDNA -protein datasets.
To investigate whether there were artifacts in the data, or if the different cell lines differed in overall expression levels, randomly sampled gene products were picked to check their correlation coefficients. The mean correlation coefficient was zero, indicating that the dataset was robust.
Using the three datasets, a subset of 169 gene products for which data were available from all three platforms were chosen that had higher correlation coefficients than 0.5 in all comparisons to construct three dendrograms, by applying a 1 – Spearman correlation metric and a top-down hierarchical method with average agglomeration.
Expression levels for 14 mRNA gene products were analyzed in eight selected cell lines by quantitative real-time PCR using a BioRad iCycler (BioRad Laboratories, Hercules, CA, USA) and SYBR Green-labeling of amplicons. Pairs of genes were analyzed simultaneously, and for each gene a nontemplate control was added. For each run, a general set-up was used consisting of three independent dilution series of a gene-specific plasmid template with known copy number to construct a standard curve as well as triplicates of cell line cDNA templates for quantitative analysis. The standard curve was generated using iCycler software (Optical System Software Version 3.0a), in which the obtained threshold cycles values (C t) were plotted against the logarithmic copy numbers of the plasmid dilution series. The C t values of the cell lines were fitted to this plot and thus the copy numbers were determined.
The specificity of the priming and amplification was verified with a melt curve for every amplicon. The quantitative real-time PCR was performed in duplicates, and copy number results were averaged, resulting in eight mean copy numbers for each cell line.
We are grateful to Annelie Waldén and Anna Westring at the KTH Microarray Center for DNA microarray production and experimental performance. This work was supported by grants from the Knut and Alice Wallenberg Foundation.
- Asplund A, Gry Bjorklund M, Sundquist C, Stromberg S, Edlund K, Ostman A, Nilsson P, Ponten F, Lundeberg J: Expression profiling of microdissected cell populations selected from basal cells in normal epidermis and basal cell carcinoma. The British journal of dermatology. 2008, 158 (3): 527-538. 10.1111/j.1365-2133.2007.08418.x.View ArticlePubMedGoogle Scholar
- Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature. 2001, 412 (6849): 822-826. 10.1038/35090585.View ArticlePubMedGoogle Scholar
- Mousses S, Bubendorf L, Wagner U, Hostetter G, Kononen J, Cornelison R, Goldberger N, Elkahloun AG, Willi N, Koivisto P, et al: Clinical validation of candidate genes associated with prostate cancer progression in the CWR22 model system using tissue microarrays. Cancer research. 2002, 62 (5): 1256-1260.PubMedGoogle Scholar
- Fu N, Drinnenberg I, Kelso J, Wu JR, Paabo S, Zeng R, Khaitovich P: Comparison of protein and mRNA expression evolution in humans and chimpanzees. PLoS ONE. 2007, 2 (2): e216-10.1371/journal.pone.0000216.PubMed CentralView ArticlePubMedGoogle Scholar
- Greenbaum D, Colangelo C, Williams K, Gerstein M: Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 2003, 4 (9): 117-10.1186/gb-2003-4-9-117.PubMed CentralView ArticlePubMedGoogle Scholar
- Greenbaum D, Jansen R, Gerstein M: Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics. 2002, 18 (4): 585-596. 10.1093/bioinformatics/18.4.585.View ArticlePubMedGoogle Scholar
- Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999, 19 (3): 1720-1730.PubMed CentralView ArticlePubMedGoogle Scholar
- Lu P, Vogel C, Wang R, Yao X, Marcotte EM: Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol. 2007, 25 (1): 117-124. 10.1038/nbt1270.View ArticlePubMedGoogle Scholar
- Shankavaram UT, Reinhold WC, Nishizuka S, Major S, Morita D, Chary KK, Reimers MA, Scherf U, Kahn A, Dolginow D, et al: Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study. Mol Cancer Ther. 2007, 6 (3): 820-832. 10.1158/1535-7163.MCT-06-0650.View ArticlePubMedGoogle Scholar
- Trauger SA, Kalisak E, Kalisiak J, Morita H, Weinberg MV, Menon AL, Poole FL, Adams MW, Siuzdak G: Correlating the transcriptome, proteome, and metabolome in the environmental adaptation of a hyperthermophile. J Proteome Res. 2008, 7 (3): 1027-1035. 10.1021/pr700609j.View ArticlePubMedGoogle Scholar
- Sharp PM, Li WH: The codon Adaptation Index – a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987, 15 (3): 1281-1295. 10.1093/nar/15.3.1281.PubMed CentralView ArticlePubMedGoogle Scholar
- Andersson AC, Stromberg S, Backvall H, Kampf C, Uhlen M, Wester K, Ponten F: Analysis of protein expression in cell microarrays: a tool for antibody-based proteomics. J Histochem Cytochem. 2006, 54 (12): 1413-1423. 10.1369/jhc.6A7001.2006.PubMed CentralView ArticlePubMedGoogle Scholar
- Stromberg S, Bjorklund MG, Asplund C, Skollermo A, Persson A, Wester K, Kampf C, Nilsson P, Andersson AC, Uhlen M, et al: A high-throughput strategy for protein profiling in cell microarrays using automated image analysis. Proteomics. 2007, 7 (13): 2142-2150. 10.1002/pmic.200700199.View ArticlePubMedGoogle Scholar
- Cross SS: Observer accuracy in estimating proportions in images: implications for the semiquantitative assessment of staining reactions and a proposal for a new system. Journal of clinical pathology. 2001, 54 (5): 385-390. 10.1136/jcp.54.5.385.PubMed CentralView ArticlePubMedGoogle Scholar
- Lehr HA, Jacobs TW, Yaziji H, Schnitt SJ, Gown AM: Quantitative evaluation of HER-2/neu status in breast cancer by fluorescence in situ hybridization and by immunohistochemistry with image analysis. American journal of clinical pathology. 2001, 115 (6): 814-822. 10.1309/AJ84-50AK-1X1B-1Q4C.View ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMed CentralView ArticlePubMedGoogle Scholar
- CSH Protocols: Calculation of Absolute Expression Values for DNA Microarray Data. 2008Google Scholar
- Carlson MW, Iyer VR, Marcotte EM: Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways. BMC genomics. 2007, 8: 117-10.1186/1471-2164-8-117.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee JK, Bussey KJ, Gwadry FG, Reinhold W, Riddick G, Pelletier SL, Nishizuka S, Szakacs G, Annereau JP, Shankavaram U, et al: Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells. Genome Biol. 2003, 4 (12): R82-10.1186/gb-2003-4-12-r82.PubMed CentralView ArticlePubMedGoogle Scholar
- R Development Core Team: R: A Language and Environment for Statistical Computing. 2009, R foundation for Statistical Computing, Vienna Austria, [http://www.R-project.org]Google Scholar
- Bengtsson H: aroma: An R Object-oriented Microarray Analysis Package. R package version 0.94. 2008, [http://www.braju.com/R/]Google Scholar
- Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30 (4): e15-10.1093/nar/30.4.e15.PubMed CentralView ArticlePubMedGoogle Scholar
- Lundberg E, Gry M, Oksvold P, Kononen J, Andersson-Svahn H, Ponten F, Uhlen M, Asplund A: The correlation between cellular size and protein expression levels – Normalization for global protein profiling. Journal of proteomics. 2008, 71 (4): 448-60. 10.1016/j.jprot.2008.06.014.View ArticlePubMedGoogle Scholar
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