RNA biomarkers discovered by RT-PCR-based gene expression profiling of archival formalin-fixed paraffin-embedded (FFPE) tissue form the basis for widely used clinical diagnostic tests; however, RT-PCR is practically constrained in the number of transcripts that can be interrogated. We have developed and optimized RNA-Seq library chemistry as well as bioinformatics and biostatistical methods for whole transcriptome profiling from FFPE tissue. The chemistry accommodates low RNA inputs and sample multiplexing. These methods both enable rediscovery of RNA biomarkers for disease recurrence risk that were previously identified by RT-PCR analysis of a cohort of 136 patients, and also identify a high percentage of recurrence risk markers that were previously discovered using DNA microarrays in a separate cohort of patients, evidence that this RNA-Seq technology has sufficient precision and sensitivity for biomarker discovery. More than two thousand RNAs are strongly associated with breast cancer recurrence risk in the 136 patient cohort (FDR <10%). Many of these are intronic RNAs for which corresponding exons are not also associated with disease recurrence. A number of the RNAs associated with recurrence risk belong to novel RNA networks. It will be important to test the validity of these novel associations in whole transcriptome RNA-Seq screens of other breast cancer cohorts. Recent major advances in DNA sequencing, “next generation sequencing (NGS)”, provide massively parallel throughput, and data volumes that eclipse the nucleic acid information content possible with other technologies, making feasible unprecedented extensive genome analyses of groups of individuals, including analyses of sequence differences, polymorphisms, mutations, copy number variations, epigenetic variations and transcript abundance (RNA-Seq) Application of older technologies, such as DNA microarray and RT-PCR platforms, have demonstrated that levels of RNA transcripts (“gene expression profiles”) stratify patients and predict outcomes in a variety of diseases, providing the basis for several important clinical tests The NGS methods described here enable transcriptome-wide cancer biomarker discovery with archival fixed paraffin-embedded (FFPE) tissue. Many thousands of FFPE tissue blocks associated with mature clinical records exist in hospital pathology archives. These can be used for tumor gene expression profiling and therefore enable rapid clinical biomarker discovery in studies that are statistically well-powered We have carried out RNA-Seq analysis on FFPE tumor RNA from a cohort of 136 breast cancer patients with tumor tissue at the time of resection and clinical outcomes for disease recurrence. This tumor RNA was originally screened by RT-PCR in the biomarker discovery phase of the development of the 21 gene breast cancer recurrence risk assay Although most current biological knowledge centers on the fraction of the genome that encodes proteins, most mammalian transcripts are non-protein-coding intronic and intergenic sequences Patient clinical characteristics are given in RNA-Seq results were successfully generated for all 136 patients, with an average of 43 million median reads per patient (86 million median reads per Illumina Hiseq 2000 flow cell lane). Sixty-nine percent of these uniquely mapped to the human genome: 19.2% to exons, 64.9% to introns, and 15.9% to intergenic regions. Ribosomal RNA accounted for less than 0.3% of the total reads. On average, 17,248 Refseq transcripts were detected per patient, 66% with greater than 10 counts, and 47% with greater than 100 counts .2 hazard ratios for each of the 185 genes screened in the historical RT-PCR-based study is shown in The spectrum of log baseSignificantly, RNA-Seq further associates many transcripts annotated in RefSeq with disease recurrence: a total of 1307 at FDR<10% . These aTo evaluate the impact of transcript abundance on initial biomarker discovery the 1307 Identified RefSeq RNAs were binned with respect to count abundance . About 3The performance of the RNA-Seq technology and resulting identified RefSeq RNAs was further evaluated using public gene expression data from an independent cohort of breast cancer patient tumors that had been assayed by DNA microarray technology. The microarray data set was assembled by merging patient sets published in two articles Among the 1307 identified RefSeq RNAs, many relate to recurrence with very high statistical significance . EstimatHierarchical clustering This analysis also reveals several novel RNA networks, three of which map to discrete cytogenetic bands : a netwoER status, which is often described in clinical practice in binary terms as ER+ and ER- via immunohistochemistry evaluation of breast tumors, dichotomizes breast cancer with respect to clinical outcome and gene expression profiles Reads mapping to intronic regions of the genome account for ∼65% of all of the sequence data. Introns tend to co-express with exons of the same genes (median R = 0.67), although these correlations vary over a wide range, from roughly zero to over 0.9. A large number (1698) of intronic RNAs associate with breast cancer recurrence  of the identified intronic transcripts, the corresponding assembled exons are not also discovered, as indicated by comparing We used two approaches to search for biomarkers within the population of intergenic RNAs, first by interrogating reads that map to 2,500 well-documented lincRNAs The present report is the result of our development of RNA-Seq methods suitable for biomarker discovery in fixed clinical tissue. The library chemistry described accommodates low amounts of archival FFPE tissue RNA, preserves strand-of-origin information, is compatible with sample indexing, and quantifies transcripts with sufficient sensitivity and precision for biomarker discovery in valuable clinical tissue specimens. Data analysis methods were selected from a number of tested options. Results generated using 5–12 year old FFPE tumor tissue from 136 breast cancer patients, are concordant with RT-PCR data. Study results also indicate that effective biomarker identification is possible with multiplexed samples.RNAs that are new putative markers of breast cancer recurrence risk are identified here, which, singly and in sets, frame hypotheses to test in later studies of other breast cancer patient cohorts. While we associated ∼1300 RefSeq RNAs (which are mostly mRNAs) with breast cancer prognosis (at FDR<10%), more than half of the total RNAs identified as prognostic lie in the ∼98% of the genome that does not code for proteins. It is noteworthy that, for most of the intronic RNAs identified as prognostic, their cognate assembled exons were not also identified as prognostic, consistent with the possibility that these intronic sequences carry biomarker information not captured in gene coding sequence. Most of the identified non-coding RNAs are very long sequences that have low counts per kilobase, and the power for identifying longer sequences is expected to be higher because of the increased counts. However, each evaluated RNA effect size (the hazard ratio for its association with recurrence) is effectively estimated by comparisons of sequence expression among patients. To the extent that shorter RNAs are handicapped in signal strength, they are handicapped equally within an RNA species, so they do not bias the analysis of each individual sequence. Future studies of other breast cancer cohorts will reveal whether rare transcripts identified here prove to be robust biomarkers.To analyze intergenic transcripts we evaluated a set of lincRNAs described in the recent literature −16). There is no significant inter-study concordance for the lower abundance quartile of Ref-Seq transcripts, plausibly attributable to the fact that signal-to- noise ratios in both technologies decrease as transcript numbers decrease.The 1307 RefSeq RNAs associated with prognosis were also examined for prognostic significance in tumors from an independent cohort of patients in which DNA microarray technology was used to profile gene expression (public NKI data set) A number of DNA microarray and RT-PCR studies of early breast cancer have identified as markers of poor prognosis a network of co-expressed mRNAs that regulate the cell cycle We have not analyzed these RNA-Seq data for either mutations or alternatively spliced isoforms. While we plan to do these evaluations, the laboratory protocols used here are not optimal for these assessments. The chemistry of our libraries is compatible with paired-end sequencing (not performed in the present study), which is highly desirable for analysis of splice variants and gene fusions In conclusion, this work describes the application of RNA-Seq methods with sufficient sensitivity and reproducibility to enable biomarker discovery in archival FFPE tissue. Whole transcriptome RNA-Seq reveals hundreds of new coding and non-coding transcripts, as well as heretofore unappreciated gene networks that strongly associate with breast cancer recurrence in this study cohort. Recognizing the challenges for development of robust gene signatures associated with clinical variables One hundred thirty-six primary breast cancer FFPE tumor specimens with clinical outcomes were provided by Providence St. Joseph Medical Center , with institutional review board approval Total RNA was prepared from three 10-µm-thick sections of FFPE tumor tissue as previously described using the MasterPure™ Purification Kit  TruSeq™ SR Cluster Kits v2  were used for cluster generation in an Illumina cBOT™ instrument following the manufacturer’s protocol. Two indexed libraries were loaded into each lane of flow cells. Sequencing was performed on an Illumina HiSeq®2000 instrument  by the manufacturer’s protocol. Multiplexed single-read runs were carried out with a total of 57 cycles per run (including 7 cycles for the index sequences).Each sequencing lane was duplexed with two patient sample libraries using a 6-base barcode to differentiate between them. The mean read ratio +/−SD between the two samples in each lane was 1.05±0.38 and the mean +/−SD percentage of un-discerned barcodes was 2.08%±1.63%. Using principal components analysis and other exploratory data analysis methods, we found no systematic differences among samples associated with flow cell or barcode.In a run-in phase of the study, we prepared duplicate libraries for 8 samples selected at random from the study set of 136. RefSeq RNA coverage for these libraries ranged between 3.1M and 6.7M uniquely mapped reads. Log count Pearson correlations among duplicate libraries ranged between 0.947 and 0.985. Single libraries were prepared for the remaining 128 samples and distributed in duplex mode among the lanes of 8 flow-cells. Sequencing in 3 lanes failed. Two libraries had low yield, resulting in low coverage. Three lanes were flagged by various Illumina process monitoring indices. New libraries for samples that had low yield were prepared and sequenced. Libraries in the failed and flagged lanes, as well as some of the low coverage samples were re-sequenced. Replicate correlations among all sequenced samples were very high, 0.985 for the samples with the high cluster density in the original run, and over 0.990 for all others. For data analysis, we used the data for one of each of the duplicate libraries from the run-in experiment. In cases in which new libraries were prepared, and for the samples in the failed and flagged lanes, we used the reads from the subsequent run. For the samples with low coverage for which we reprocessed the library, reads from the two runs were pooled. For the rest of the samples, we used the reads from the single lane. Results differed little when other data analysis procedures were used, for example, using only the second run when libraries were reprocessed.With the exception noted below, all primary analysis of sequence data was performed in CASAVA 1.7, the standard data processing package from Illumina. De-multiplexing of sample indices was set with 1 mismatch tolerance to separate the two samples within each lane. Raw FASTQ sequences were trimmed (6 bases from the 5′side and 8 bases from the 3′side) before mapping to the human genome , to address 3′ end adapter contamination, random RT primer artifacts, and 5′ end terminal-tagging oligonucleotide artifacts. Mapping started from a single seed of 32 base pairs with two mismatches allowed; gap penalties were allowed using ELAND2 provided by Illumina. The libraries as prepared contain strand-of-origin  sequence information. Annotated RNA counts (defined by refFlat.txt from UCSC) were calculated by CASAVA 1.7 both with and without consideration of strand-of-origin information. Although retained in the mapping process, CASAVA does not provide directional counts by default. These counts were obtained by splitting the mapped (export.txt) file into two parts, one with sense strand counts, the other with antisense strand counts, and processing them independently. Raw FASTQ sequence was mapped with Bowtie http://dx.doi.org/10.5061/dryad.q760h). RNAs for which none of the 136 specimens yielded 5 or more counts were excluded from analysis. Of 21,283 total RefSeq transcripts counted by CASAVA, 821 had a maximum count less than 5, leaving 20,462 RefSeq transcripts for analysis. Similar to a recently published procedure described by Bullard et al.2 raw RNA counts (setting the log2 for a 0 count to 0) were normalized by subtracting the 3rd quartile of the log2 RefSeq RNA counts and adding the cohort mean 3rd quartile . For normalization of RefSeq and intergenic RNA data, RefSeq transcript data were used. For normalization of intronic RNA data, intronic transcript data were used. Use of third quartile normalization effectively mitigated trends in overall coverage related to sample age and produced stable estimates of expression with relative log expression  values that were centered on zero and relatively tightly distributed around 0, an indicator of effective normalization.Data were analyzed in 3 categories: first, RefSeq RNAs, about 80% of which are exon sequences, consolidated for each gene; second, intronic RNA sequences, consolidated for each gene; third, intergenic sequences, operationally defined as non-RefSeq, non-intronic sequences  were assessed using the method of Storey Standardized hazard ratios for breast cancer recurrence for each RNA, that is, the proportional change in the hazard with a 1-standard deviation increase in the normalized expression of the RNA, were calculated using univariate Cox proportional hazard regression analyses T) from the cohort median CT) with cancer recurrence were computed using the same methods used for evaluation of the RNA-Seq data.Expression of 192 transcripts in the same tumor RNAs was measured using previously described RT-PCR methods Intergenic regions were identified by a novel program that evaluates intergenic regions having wide variations in length, and uses data from a population of subjects rather than an individual subject. Briefly, overlapping reads from all 136 patients were combined based on their human genome mapping coordinates, creating clusters of individual islands. Nearby islands were grouped by a merging tolerance criterion into regions of interest. Putative novel intergenic transcripts were then defined by filtering out transcripts with known refFlat.txt annotations.Figure S1Identification of ESR1-positive patients by RNA-Seq analysis. Normalized values of ESR1 and PGR in 136 breast cancer patients are represented in a scatter plot. Each symbol represents a different patient. Because in human breast cancer it is rare for a tumor to be both PGR positive and ER negative, or to be PGR negative and ER positive, the distribution of both PGR and ESR1 data were used to set cutoffs for calling patient ESR1 status. The vertical and horizontal cutoffs were set by visual inspection of the data.(TIFF)Click here for additional data file.Figure S2Box plots of normalized expression values of RNAs in breast cancer patients, stratified by recurrence status. Each point represents a patient tumor. The bottom and top of the box are the 25th and 75th percentiles and the horizontal band within the box is the 50th percentile (median) of the points in the group. The ends of the vertical lines represent the lowest datum still within 1.5 inter-quartile range of the lower quartile, and the highest datum still within 1.5 inter-quartile range of the upper quartile. Values from RNA-Seq (left panel) and RT-PCR (right panel) are shown. A. BAG1; B. BIRC5; C. CCNB1; D. CD68; E. ESR1; F. ERBB2; G. GRB7; H. MMP11; I. PGR.(PDF)Click here for additional data file.Figure S3Heat map for expression of Identified RefSeq RNAs in 136 breast cancer patients. All 1307 Normalized expression values of RefSeq RNAs that were found to be associated with risk of breast cancer recurrence (currence  are repr(TIFF)Click here for additional data file.Table S1Patient case characteristics and outcomes.(TIFF)Click here for additional data file.Table S2Providence RNA-Seq 136 patient run report.(TIFF)Click here for additional data file.Table S3Assembled RefSeq RNAs Identified as Associated with Risk of Breast Cancer Recurrence in 136 Breast Cancer Patients.(XLSX)Click here for additional data file.Table S4Agreement between genes identified using public microarray data and the NGS data.(TIFF)Click here for additional data file.Table S5Assembled RefSeq RNAs Identified as Associated with Risk of Breast Cancer Recurrence in 111 Breast Cancer Patients Designated as ESR1 Positive.(XLSX)Click here for additional data file.Table S6Assembled Intron RNAs Identified as Associated with Risk of Breast Cancer Recurrence in 136 Breast Cancer Patients.(XLSX)Click here for additional data file.Table S7LincRNAs Identified as Associated with Breast Cancer Recurrence in 136 Breast Cancer Patients.(TIFF)Click here for additional data file.Table S8Intergenic Region Locations Identified as Associated with Risk of Breast Cancer Recurrence in 136 Breast Cancer Patients.(XLSX)Click here for additional data file.Method S1The uniquely mapped reads from all (136) patients were filtered to a depth of 1 read to eliminate potential noise from mis-mapping, and other sources. These reads were clustered into individual read islands based on the overlap of their human genome map coordinates, yielding 12,750,071 islands. Nearby islands were consolidated by a merging distance cutoff that was calculated by a maximum likelihood estimation (MLE) to maximize overlap of identified regions with known genes. This yielded a cutoff of 30 base pairs (bp) and 6,633,258 regions of interest (ROIs). These were then filtered by the three retention criteria: 1) average read count ≥5 across the tested patient population, 2) ROI length ≥100 base pairs, 3) read depth (average read number divided by the length of the ROI) ≥0.075. ROIs among the remaining 23,024 were classified as intergenic if they did not overlap with the transcripts annotated in the UCSC refFalt.txt file. A total of 2101 intergenic region ROIs were obtained from this process.(DOCX)Click here for additional data file.


Microarray-based comparative genomic hybridization (aCGH) is used for rapid comparison of genomes of different bacterial strains. The purpose is to evaluate the distribution of genes from sequenced bacterial strains (control) among unsequenced strains (test). We previously compared the use of single strain versus multiple strain control with arrays covering multiple genomes. The conclusion was that a multiple strain control promoted a better separation of signals between present and absent genes.We now extend our previous study by applying the Expectation-Maximization (EM) algorithm to fit a mixture model to the signal distribution in order to classify each gene as present or absent and by comparing different methods for analyzing aCGH data, using combinations of different control strain choices, two different statistical mixture models, with or without normalization, with or without logarithm transformation and with test-over-control or inverse signal ratio calculation. We also assessed the impact of replication on classification accuracy. Higher values of accuracy have been achieved using the ratio of control-over-test intensities, without logarithmic transformation and with a strain mix control. Normalization and the type of mixture model fitted by the EM algorithm did not have a significant impact on classification accuracy. Similarly, using the average of replicate arrays to perform the classification does not significantly improve the results.Our work provides a guiding benchmark comparison of alternative methods to analyze aCGH results that can impact on the analysis of currently ongoing comparative genomic projects or in the re-analysis of published studies. This manuscript is an update of a previous work  on the aThe idea behind this technology is to generate microarrays from sequenced genomes, and then hybridize genomic DNA from other sources to these arrays. The aim is to detect similarities and differences in genomic content through the characterization of test and control samples given the genes common to both samples and those that are specific to the control sample .In two color aCGH experiments, the control sample is composed of DNA from one or more strains with sequenced genomes represented in the array. The test strain is composed of DNA from a non-sequenced strain. Each sample is labeled with a different fluorochrome and hybridized to the microarray .2 (T/C)) of both fluorescent signals (test (T) and control (C)). Genes present in both genomes will give a clear fluorescence signal in both channels (LR close to 0), and absent genes in the test sample are expected only to have a signal in the control channel (negative LR).The analysis of aCGH experiments aims to classify the genes as present or absent. Many approaches have been applied ,4-7, butStreptococcus pneumoniae[2 (T/C)) provides better results with the EM algorithm. In the traditional ratio the present signals distribute around 1 and the absent signals are compressed between 0 and 1. The logarithm transformation alleviates this compression, but we hypothesize that the two classes of signals can be further separated if the C/T ratio is used.Concerning the control used when arrays are designed with multiple sequenced genomes, there is no consensus about the best option. Some studies have used a single strain control -11 whileStreptococcus pneumoniae CGH microarrays already analyzed in a previous study [Streptococcus pneumoniae Version 5), with 3425 oligonucleotide probes covering the genomes of three pneumococcal strains: R6, T4 and G54. Each probe was 70 base long and replicated four times in each array. The annotation file provided by the array manufacturer was used to define which gene, from any of the three reference genomes, was represented by which probe. For each different pair of test strain and control, four replicates were done, resulting in a total of 16 hybridizations. The four different hybridizations were R6 versus T4, R6 versus MIX (T4 + R6 + G54), G54 versus T4 and G54 versus MIX (T4 + R6 + G54). Dye swaps were applied in each set of four replicates. Microarray hybridization raw data was deposited in the ArrayExpress public database with accession number E-MEXP-1390.Part of the data analyzed in this study was obtained with a multi-strain us study . The arrThe images of the microarrays were analyzed using Feature Extraction 9.1 software . For each spot the signal was background corrected by subtracting the minimum feature signal in the array. The intensity-specific bias was removed through loess global normalization. The resulting spot average pixel intensities were used to compute several signal ratio measures: the test/control signal ratio (T/C) and the corresponding logarithm signal ratio (log (T/C)), and the control/test signal ratio (C/T), before and after normalization. Since each gene was spotted four times per array, data retrieved from each of the valid spots were averaged for a particular gene.Staphylococcus aureus array (PFGRC/JCVI Staphylococcus aureus Version 5). Raw data files were imported from ArrayExpress (E-MEXP-2007). The hybridizations with a test strain represented in the array design were selected. This selection allowed the definition of which gene was present or absent in the test sample by using the information available in the array annotation files provided by the manufacturer. All the hybridizations used a single strain control. The analysis was conducted using only the probes representing a gene present in the control sample. Raw data files were generated by GenePix image analysis software. For each probe, the F633 median and F532 median were used to define the T and C signals.Additional data analyzed in this study was obtained from a published dataset  using a Instead of hybridizations where the test strain is sequenced, we could have tested the different analysis methods with microarray results of genes that were confirmed by PCR assays. Although these assays are common in published studies, they are not well suited for our purpose. In each dataset the number of genes confirmed with PCR is normally low. We would have to analyze a high number of experiments to achieve a reasonable statistical confidence on the results. Additionally, PCR results may disagree with microarray result, even if both are physically correct. If the array is based on short oligos, that sequence can be conserved while the regions targeted by the PCR primers can be divergent (or the other way around). Moreover, the selection of genes for PCR verification is not random. Genes can be selected because they are biologically more interesting, or because they are more likely to confirm the microarray results. This bias would make it more difficult to see any accuracy differences between C/T and T/C ratios using genes verified by PCR.Streptococcus pneumoniae microarrays were designed with three reference strains. The control sample can be a mixture of DNA of the same three strains. Thus, the microarrays where the samples are hybridized contain sequences that identify genes in one strain (group 1), two strains (group 2) or three strains (group 3). According to our previous study [The in group , two strFor each different signal ratio measure we applied the Estimation-Maximization (EM) algorithm to fit mixture models and to classify genes as present or absent. A mixture model is a convex combination of probability distributions that allows modeling data sets composed by different subsets, each of which is modeled by its own distribution. The mixture models used in this study were: the Normal-Uniform (NU) model and Gamma-Gamma (G) model. The first considers that signal ratios from present genes are shaped by the Normal distribution, while the missing genes are modeled by the Uniform distribution . The GamThe EM algorithm is an efficient tool in the estimation of mixture model parameters and classification of each gene in one of two distinct groups. Iteratively, it finds the parameters of both distributions in the mixture model that maximize the likelihood of obtaining the given observations. At each step it computes for each gene the probability that it belongs to the present gene distribution. After the algorithm converges to a stable parameter set, each gene is considered present if the probability that it belongs to the present gene distribution is greater than 0.5.In this work the EM algorithm successfully converged in all occasions. The G model was not fitted to log(T/C) ratios because they present negative values, for which the Gamma distribution is not defined. Log(C/T) ratios were not evaluated because they have the symmetrical value of the log(T/C) ratios. As such, similar but symmetric fits would be expected for both ratios, yielding the same classification accuracies.The produced classifications were compared with the known classification. For each classification we identified each gene as a true positive (TP) or true negative (TN) if the EM classification agreed with the known genome, and as a false positive (FP) or false negative (FN) if the EM classification did not agree. A gene is considered positive (either TP or FP) when the EM classification predicted that gene to be present. With the number of TP, TN, FP and FN we can calculate the accuracy (Acc), that is, the proportion of correct results.To compare the results obtained with the mix and the single strain control, Wilcoxon rank sum tests for independent samples were used to detect significant differences in the respective average accuracies, obtained from 8 independent arrays for each control choice. To compare the results obtained with different signal ratios of mixture models, Wilcoxon signed rank tests for paired samples were used, since the variant methods were applied to the same replicate arrays. Differences were considered significant when p <0.05.Figure To compare the performance of both control types, the best analysis options for each kind of control were used. The mixed strain control  reaches accuracies that are on average 0.05 superior (p < 0.001) to the single strain control . This accuracy difference may translate in more 50 to 350 correctly classified genes, considering bacterial genomes with 1000 to 7000 genes.The impact of normalization and the mixture model choice are not clear from the analysis of Figure Staphylococcus aureus strains was hibridyzed with a S. aureus microarray using a single strain control. Although these arrays are also based on 70-mer probes, they were designed for a different species and analyzed with different image analysis software. Figure S. aureus microarrays the accuracies obtained by using the T/C, Log(T/C) or C/T ratio. The pattern observed in Figure The choice of the ratio has a clear impact in the resulting accuracies. Independently of the control type, C/T ratios perform better than T/C ratios, and log(T/C) ratios present intermediate accuracies Figure . To confTo understand the impact of the ratio choice in the mixture model fit, we analyzed the distribution of ratio values in one of the pneumococcal arrays Figure . It is pAiming to quantify the advantage in averaging replicate arrays, we combined the four available replicates of each hybridization into the possible sets of 2, 3 or 4 replicates and evaluated the accuracy of the resulting classifications, shown in Figure Other authors ,3 have pLogarithm transformation also has a negative impact when compared with the C/T ratio. In the analysis of gene expression, the aim is to identify genes that are either over or under expressed. The logarithm transformation is useful to make both types of changes quantitatively comparable. In aCGH experiments applied to bacterial genomes, genes are either present or absent (which includes genes with divergent sequences). The use of the C/T ratio expands the signal scale in the most interesting range for this type of analysis, facilitating the statistical segregation of signal distributions between present and absent genes. We should note that using the C/T ratio instead of log(T/C) (or of T/C ratio) only reverses the order of observed gene signals, without ranking changes. This implies that the optimal threshold separating present and absent genes with all three ratio variants will produce the same maximum accuracy. What changes is the capacity of the EM algorithm to adjust the mixture model to the available data, leading to better results with the C/T ratio. Previous studies have showed that fitting mixture models to aCGH data provided better result than several other methods ,15. TheiThe present results also confirm our previous study , in whicS. aureus arrays.Nowadays, comparative genomic studies in bacteria are migrating from the microarray technology to high throughput sequencing methods. This happens due to the decreases in the costs associated with sequencing, but also due to a gain in information: from present-absence status to a detection of diverging sequences. Still, some ongoing projects keep using microarrays, justifying the usefulness of the methodological comparison we are presenting -22. FurtOur results must be interpreted taking into account some limitations of our study. We tested arrays from two species using a similar array design strategy and manufacturer. Additionally, only one normalization algorithm and two different mixture models were compared. Although we are confident that the observations are sufficiently general to apply in other contexts, we cannot anticipate the effect of different genomes, microarray platforms, normalization procedures or mixture models using different distributions. We could, for example, expand our study to include a higher number of different organisms, in order to generalize our conclusions. The advantages of the C/T ratio are not necessarily valid for every organism. Nevertheless, as our study shows that for two species the choice of ratio has a significant impact in classification accuracy, we can conclude that, when working with a different organism, it is worthy to conduct a similar study to optimize the signal ratio used for analysis.Two aCGH analysis problems were not approached in our work. One is the detection of multiple gene copies. If there are more copies of a given gene in the genome of control strains than in the test strains, the gene can be erroneously classified as absent. From the array design it should be possible to identify probes targeting genes with multiple copies in the control strains, and perform a separate analysis in case they where classified as absent. Probes with a single copy in the control strains and multiple copies in the test strain will most likely be classified as present. A subsequent analysis of probes classified as present can be developed to quantify the number of copies, but it is expectable that as the copy number grows, there should be saturation in the fluorescence signal.The second analysis problem is the quantification of sequence divergence. Our analysis returns a probability of gene presence in the test strain. When that probability has intermediate values (around 0.5), one can suspect of a divergent gene. However, the same values can be equally originated by excessive noise. Even if there are very low noise levels, a sequence divergence determined by microarray just implies a sequence difference along the region targeted by the microarray probe. The remaining sequence of the gene can be highly conserved or highly divergent. The method proposed by Snipen and colleagues  uses theWe performed a comparison of several alternative methods to analyze bacterial aCGH datasets. All alternatives had in common the use of the EM algorithm to adjust a mixture model to the observed signal distributions. The results validated a previous study advocating the use of a strain mix as a control in aCGH experiments with multi-genome arrays, instead of a single strain control. Additionally, we found that loess normalization or the choice of mixture model distributions did not have a clear impact on the results. The method variant that induced a greater improvement in achieved classification accuracies was the use of a control-over-test signal ratio, which is the inverse of the ratio traditionally used to analyze this type of results.The microarray datasets analyzed in this work are deposited in the Array Express public database with the following accession numbers: E-MEXP-1390, E-MEXP-2007.The authors declare that they have no competing interests.CES, FRP and LSC have performed the data analysis. MA, MR and FRP have conceived the study. All authors wrote, revised and approved the final manuscript.


We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR) of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients. Copy number aberration (CNA) refers to cytogenetic events in which the DNA replication process is disrupted such that the gene either is replicated multiple times (copy number gains) or loses one or both copies (copy number loss) in newly generated cells. Comparative Genomic Hybridization (CGH) has emerged as a dominant technique for detecting CNA test sample of interest is labeled with a dye (say Cy3) and then mixed with a diploid reference sample labeled with a different dye (say Cy5). The combined sample is then hybridized to the microarrays and intensities of both colors are measured through an imaging process. The quantity of interest is the amplifications, and the corresponding theoretical intensity ratios are Human beings have two copies of each gene, defined as a segment of DNA. The Expression microarrays measure RNA expression which, by the central dogma of molecular biology, are resulted from the transcription of DNAs. Microarray technology for measuring RNA gene expression has been well known to the statistical community, and its review is omitted here. Naturally, we are prone to think that CNAs impact the intensities of the relative RNA expressions in that more copies of DNA should lead to higher levels of RNA expression. It is therefore of great interest to study the intensity of such interaction, if there is any, between aCGH and RNA expression measurements on different genes.Gene expression and copy number variation data have been broadly studied, to assess differential expression of genes An important biological premise to the description of the model is that by integrating DNA copy number and RNA expression data, we will gain more knowledge about the underlying biological process. For example, a high or low correlation between a copy number aberration (CNA) for a gene marker and its abnormal RNA expression would indicate different carcinogenic mechanism and therefore different treatment selections We describe a Bayesian Mixture Model that converts the noisy raw intensity measurement of the DNA and RNAs into probability of expression, which are subsequently modeled as latent parameters. Thus the integration of the two platforms is realized by joint modeling the probabilities of expression through a probit regression. Our aim, however, is not only to evaluate the relative contribution of large genetic variants such as CNAs, to gene expression but also make inference using both differential expression of the genes and differential copy number variations of the same set of genes. Moreover our full model-based approach allows us, after new information on the patients in the study are acquired, to exploit the latent integrated structure of our model and achieve better predictive performances for the clinical outcome of new patients coming into the study.differential behaviour, which takes into account both differential gene expression and differential CNA, and prediction of the pCR of patients after treatment. A final remark is provided in the discussion section.In the next paragraph we present a motivating example with matched arrayCGH and microarray samples from breast cancer patients. In the materials and methods section we introduce probability models with a particular focus on the probit regression that allows for integration of both platforms, along with some simulation studies. Thus, in the result section, the focus is on posterior inference of the interaction between the two platforms, We consider data in breast cancer consisting of 121 patients from three disease subgroups, ER+, HER2+, and triple negative (TN). ER+ patients have present estrogen receptors – a protein related to hormone and regulation of gene expression – in their cancer cells. HER2+ patients are instead those whose tumor cells test positive for a protein called human epidermal growth factor receptor 2. Finally TN patients lack three “receptors” in their cancer cells: ER, HER2, and progesterone receptors. ER+ and HER2+ patients were therefore collapsed in the same group, in order to compare TN patients versus others.On a slightly reduced set of 116 patients we have a measure, formalized as a dichotomous variable, on their positive or negative pCR to treatment. Numerosities are specified in the The mRNA expression data was obtained with Affymetrix U133A gene chips. The data was normalized with MAS5 algorithm, scaled to target intensity of 600 and log2 transformed. The expression profiles of the cancers are available at GEO accession number GSE22093 ArrayCGH and microarray RNA experiments have been performed using the 121 breast samples to obtain the copy number data on 22,944 probes and RNA expression data for 11,306 genes. We then mapped 22,944 probes to the 11,306 genes, which gave us a matching between the probe ids on the aCGH and the gene ids on the microarrays.All the research used public data, published in 2009 in the following paper: “Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array” written by Andre F. et al. and published in Clinical Cancer Research b at sample t, and g at sample t, On arrayCGH, the experimental unit is probe We use a mixture model Anticipating the integration of both platforms using a regression model, we further introduce latent Gaussian variables Before we introduce the probit regression for integration, we present a prior for The integration of the two platforms is easily done using the latent probit scores and a linear model. First, we introduce a gene-level score for the aCGH data, defined as A Markov dependence is assumed across the probes and it is defined in the following conditional prior on the probe specific effect. Define The last step is the specification of the priors for the set of parameters that index the sampling model. We assume conditionally conjugate priors. Denoting Particular attention is given to the formulation of the prior for the tail over-dispersion parametersA summary of the model is given in the upper part of The idea of this section raises from the question of whether or not we could use the same latent structure underneath gene expression and copy number variation data to make inference on a clinical outcome of new patients in the study, in particular The chosen approach is to state a model for The advantages of our model with respect to, for example, a simple logistic regression In summary, as a new patient comes into a study and we have measurements of his gene expression and copy number variation, we run the model In this particular case the outcome refers to the pCR to the treatment of patients in a breast cancer study, which is defined as a complete disappearance of the tumor with no more than a few scattered tumor cells detected by the pathologist in the resection specimen As before we use a mixture model  and 2 [1The next two equations embody our assumption that positive or negative clinical response of patients could be related to differential behaviour of a small subgroups of the 11,306 genes, i.e. copy number variation and gene expression. We assumeThe integration of the two platforms is implemented as a regression with the probit scores,As new patients so that we can learn about Priors are defined as in section 2.4, with the only exception of the regression parameters A summary of the model is given in the lower part of Posterior inference for the proposed model is carried out using MCMC simulations by a Gibbs sampling scheme, iterating from the complete set of full conditionals reported in the appendix.Since the analysis deals with high throughput gene expression data and our final aim is that of selecting A useful generalization of frequentist Type-I error rates to multiple hypothesis testing is the false discovery rate (FDR) introduced in Benjamini and Hochberg Let Let It was proved by MAn algorithm that allows us to compute FDR levels for number of discoveries, and therefore to select differentially expressed genes so that the FDR level is controlled at level We perform a small simulation study and generate data in a way that the last 50  genes show joint differential behaviour in copy number and RNA expression. We firstly generated two matrices for gene expression for RNA expression, we generated for copy number, we generated The second simulation study generates data from the proposed mixture model. We started from setting In both cases roughly 2000 iterations were needed for convergence of the MCMC chain.For the sake of simplicity, we report only results for the second simulation. In While these simulations merely show that our proposed models achieve what is expected, we direct attention to selection of differentially behaved genes with multiplicity control and then data analysis based on breast cancer samples.We applied our model to the breast cancer data set. As comparison, we also applied a simpler version of our models by setting In the upper plot of A simple model checking was achieved plotting posterior probabilities of differential gene expression and difference in means of the gene expression measurements for TN and non TN group. Following the same criteria, we plotted posterior probabilities of positive interaction between platforms and sample correlations. Lower plots of interesting genes: under (over)-expressed genes which jointly showed DNA copy number deletion (amplification) in TN subgroup, under (over)-expressed genes conditional on DNA copy number aberration only in TN subgroup and genes which showed positive interaction between the two platforms. We therefore respectively definedOur main focus was on five lists of • • ••g.•FDR levels were computed with the algorithm presented in the previous section for the distinct On the other hand, for prediction of pCR, we split the data sets into a training set and a test set; the training set, consisting of 94 patients, was used to obtain samples from the posterior distribution of the parameters while the test set, consisting of 22, to check for prediction performances through the ROC curve. Both sets were randomly selected, and numerosities with respect to pCR of training and test samples are reported in The adopted method for the estimation of the smoothed ROC curve is LLoyd and Yong's one As mentioned above, the tests we performed were done on a sample of 22 patients, for which we had previously measured their pCR, and are based on the posterior probabilities of the clinical outcome being 1, The ROC curves are compared in glmnet in R, and set the elastic net mixing parameter We finally tried and compared our method with a simple logistic regression with LASSO variable selection (LLR) We therefore plotted in We have introduced a Bayesian hierarchical model to integrate two types of genomics data, copy number and RNA expression. The proposed model can be easily extended to multiple platforms, with modification to the modeling of latent probit scores. Since the entire statistical inference is based on a coherent probability model, scientific questions can be addressed with probability statements, allowing for reporting uncertainty measures such as FDR. This is the main advantage of the proposed models over existing ones.In Breast cancer has been classified into 5 or more subtypes based on gene expression profiles, and each subtype has distinct biological features and clinical outcomes. Among these subtypes, basal-like tumor is associated with a poor prognosis and has a lack of therapeutic targets. MYC is overexpressed in the basal-like subtype and may serve as a target for this aggressive subtype of breast cancer. Tumor suppressor BRCA1 inhibits MYC's transcriptional and transforming activity As far as the model is concerned, there are a few possible weaknesses in the procedure, mainly related to the prior specification for parameters Also, it was very hard to compare our models' performances with other methods, either due to the lack of codes or to the scarcity of works on the specific topic of prediction using integrated genomic platforms; we therefore chose a simple LASSO logistic regression which showed to be a poor fit for this particular data and this is mainly due to the high correlation between them.Future work includes the development of models for integration of three or more platforms, and the extension to new type of genomics data, such as next-generation sequencing (NGS) data. In the latter case, the main challenge is the inclusion of a model for the count data from the NGS experiment. The intuitive statistical method for such an extension would be a graphical model, where network priors will be considered treating each platform as a node, and edges among the nodes will be interpreted as dependence between platforms.Finally, all this project was focused on a specific data set, with rather particular features. The natural hierarchical structure and correlation between DNA and RNA makes very hard to think of the application of our methodology to different problems, though an interesting path to follow could be that of demographical sciences, where this hierarchical structure could be found for example in data at country level and regional level.


CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields.The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data ( In order to fully understand pathological processes in clinical settings at the genomic level, it is necessary to compare the transcriptomes of pathological processes in individual patients with those of the physiological processes of normal counterparts. Although transcriptomic analysis has been extensively applied to various diseases, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes and physiological and pathological processes . It is anticipated, if successful, this approach will reveal hidden relationships between pathogenesis, developmental mechanisms, and gene regulation.Gene signature has been a most commonly employed approach to address this type of problem. A number of methods have been proposed to measure the degree of inclination towards a certain signature in individual disease samples: correlation coefficient to the average gene expression of the signature genes Among multidimensional analysis methods, principal component analysis (PCA) is most commonly used to analyse the relationships between samples, although PCA is vulnerable to the addition of subtle phenotypes and aberrant samples, and is more suitable for visualising data structure etal reported a method using a variant of CCA for the analysis of microarray expression data with respect to binary response data CCA on Microarray data (CCAM).Thus, in order to reveal the cross-level relationships between diseases, genes, and normal cells, we have adapted a multidimensional approach, Canonical Correspondence Analysis (CCA), to microarray data. Currently, CCA is widely used in ecology and social science, as it can simultaneously analyse two totally different types of data – one as response data and another as explanatory data, revealing the relationships between these two data In order to examine the validity and efficiency of our method, we have analysed two haematological disorders: leukaemias and myelodysplastic syndromes (MDS). Haematological disorders are classified and understood by referring to normal haematopoietic cell differentiation. Leukaemias are classified on the basis of the cell type involved and the state of maturity of the leukaemic cells, and categorized into major four groups: acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), chronic lymphocytic leukaemia (CLL), and chronic myelogenous leukaemia (CML) MDS are a group of clonal haematopoetic disorders marked by ineffective haematopoiesis, peripheral cytopenias, and an increased risk of transformation to AML Genome-wide gene expression analysis  has been extensively used for improved understanding of the diagnosis, prognosis, and pathogenesis of these haematological diseases Based on the assumption that leukaemia is classified by referring to normal haematopoietic cell differentiation, we attempted to analyse using transcriptomic data the relationships between leukaemia disease samples and normal haematopoietic lineage cells. We aimed in this analysis to determine the transcriptomic identities of individual leukaemia patients by analysing a transcriptomic dataset of leukaemia GSE13159  and thatFirst, we employed a univariate approach to address this problem, using provisional gene signatures of haematopoietic cell populations (see Methods). Here, we aim to score individual disease samples by the degree of maturation into each cell population. Given that some haematopoietic cell populations may be too similar to each other to provide meaningful results that discriminate disease samples, hierarchical clustering was used to cluster haematopoietic cell gene signatures based on their correlations to individual disease samples . Based oCCA on Microarray data, CCAM)  are apparently enriched in quadrant III  are found in quadrant II , CD180 (RP105), and CXCR5  scores in axis 1, while genes related to myeloid cell differentiation and myeloid leukaemia have low (negative) scores . Myeloidnd CXCR5 . In factinertiaThus, the map analysis in MLL and AML with inv(16)/t were more associated with CFU-Monocyte than other granulocyte lineage cells, which is consistent with the facts that these subtypes are morphologically more correlated with the monocytic lineage MLL showed an involvement of the monocytic lineage CBF, and is morphologically associated with the French-American-British (FAB) AML-M4 subtype PML-RARA fusion gene and corresponds to the FAB M3 subtype (acute promyeolocytic leukaemia) AML1(RUNX1)/ETO and corresponds to the AML-M2 (acute myeloid leukaemia with maturation) Next, we further analysed the phenotypes of AML subtypes by CCAM . We inclThe analyses above showed that CCAM successfully revealed known relationships between leukaemia, haematopoietic cell differentiation, and genes in a concise and transparent way. In this section, we examined whether the approach was effective in generating and testing hypotheses, and questioned whether this method could provide meaningful insights into clinical problems. Accordingly, we analysed two independent MDS datasets. The first analysis was carried out in order to generate a new hypothesis. We then tested the hypothesis by analysis of another independent dataset.We analysed the transcriptomic data by Sternberg et al GSE2779  along wiKIT and NPM1, the mutations in which are suggested to play roles in leukaemic transformation POU2AF1, PAX5, and CD19, were associated with normal BM as reported CCA triplot showed that axis 1, which was composed of the largest variation in the dataset, is primarily represented by the difference between MDS and normal BM as well as that between HSC/MEP and CMP/Pro-B. As Sternberg et al reported, MDS samples had negative correlations with Pro-B . In addiBased on these findings, we generated a hypothesis that MDS patients with higher correlations with both HSC and MEP (and negative correlation with Pro-B) had a higher risk for leukaemic transformation. To test this hypothesis, we have applied CCAM to another independent dataset of MDS that is composed of only MDS patients , and analysed the results in conjunction with the clinical data GSE15061 .First, using CCAM, we determined the relationships between individual MDS patients and haematopoietic cells based on their transcriptomes . Next, wpatients , demonstwa score of CCAM, see Method), we analysed the relationships between individual patients and their associations with the four haematopoietic cell populations, and thereby established a scoring system for MDS patients (designated as the HSC-CMP score). As expected, genes with high HSC-CMP scores were specific to HSC/MEP, while those with low scores were specific to CMP/GMP , which are most often multidimensional in nature and are often not straightforward. CCAM has effectively solved this type of problems by analysing two independent transcriptomic data. Visualisation of the analysed results has made it transparent which cell populations are being compared for the relationship with disease samples. In addition, CCAM allows the exploration of novel molecular mechanisms that are highly associated with particular cell and/or disease. For example, in Consistent with a previous report As RNA for hybridization was extracted from unsorted, mononuclear BM cells from MDS patients in this dataset (GSE15061) Importantly, the HSC-CMP score showed a prognostic value for overall survival . Given tOur analyses using transcriptomic data obtained from batch samples have revealed the transcriptomic identities of the phenotype of dominant cells or ‘average’ cells in the context of normal haematopoietic cell differentiation. It is known that variations can occur within individual cancers, in which the cancer cells often have a range of functional properties and diverse expression of markers i.e. %Explained is very low [e.g. CCAM does not produce ready-to-go results, but provides a platform where the existing hypotheses are examined and new hypotheses are formed and generated. Depending on how explanatory variables are set and used, CCAM can be used for exploratory purposes in a data-oriented way c.f.  or for eImportantly, the effective use of CCAM requires deep knowledge of both biology/medical science and multidimensional analysis. Because CCAM can help the process of hypothesis generation and testing, this method is best performed when biologists/medical scientists actively participate in the analysis. Readers with biological backgrounds are encouraged to understand the procedures of CCAM in i.e. gene expression).Notably, CCAM has clarified in the analysis exactly what is compared and analysed, with the power of the map approach. Although it might be obvious to haematologists that HSC is correlated with the progression of MDS, CCAM revealed this without prior knowledge, and more importantly, showed that this statement was true only when patient samples were compared with not only HSC but also some more differentiated cells. CCAM identified that CMP was an appropriate cell population to be compared with HSC c.f. . In addiCCAM provides a practical solution for comparing multiple groups of samples with multiple cellular phenotypes. In addition, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing novel clinical insights into haematological diseases. In fact, CCAM provided new insights such as the correlation of the severe phenotype of MDS and MEP, in addition to the known correlation with HSC. Furthermore, CCAM can be effectively used for exploring the genes that are correlated with cellular phenotypes of interest using the map approach c.f. .The strength of CCAM is its ability to explore datasets for hypotheses-generation using the map approach. Once a hypothesis is generated, we can test the hypothesis by this method using other datasets c.f.  or by otgeological locations, species, and environmental variables, respectively, by ter Braak geological locations by ter Braak) and cellular phenotypes in columns .CCA is a variant of Correspondence Analysis (CA), which is a key method in sociology, and is developed in ecology for identifying the relationships between ecological data at three different levels environmental variables that define the phenotypes of each cell population. Thus, CCAM analyses the interpretable part of the original disease dataset by haematopoietic cell data %Explained, see below) was 10–20% of the original data in the presented analyses Briefly, first, CCAM projects the dataset of disease samples onto the data of haematopoietic cell differentiation, which are averaged for each cell population using scaled data with an average of zero and standard deviation of one for each gene . Importainertia, which plays the same role as the total variance in PCA. Technically, inertia is the sum of total Pearson environmental variables) is Suppose that the normalised gene expression of reference cell subsets (ng steps : (1) Usirma of the Bioconductor package, affy. Cross-platform comparisons were made by a commonly employed algorithm dudi.pca of the CRAN package ade4.We have used GSE24759 for haematopoietic cell differentiation vegan, for the computation of CCA wa score, and biplot arrows are based on weighted correlation of sample scores and haematopoietic cell differentiation rgl and lattice, respectively.We used the CRAN package, limmatopTable of limma were designated as the provisional signature genes of each cellular population. Gene signature score was derived by analysing the averaged expression of signature genes and the gene expression of each disease sample by Pearson's correlation coefficients. Obtained scores were further scaled so as to have an average of zero and standard deviation of one, because the primary interest of the analysis is to compare between disease samples.Provisional gene signatures were identified by a moderated, empirical Bayes t-statistic implemented in the Bioconductor package survival, was used for these computations.Log-rank test was used for analysing the survival data of the stratified groups of patients. Cox proportional hazard models were used for analysis of survival data. The CRAN package, Figure S1Hierarchical clustering of gene expression signatures of haematopoietic cells by correlation to a leukaemia disease dataset. All the haematopoietic cell populations in GSE24759 were analysed. A gene signature was identified for each haematopoietic cell population, and gene signature score was calculated as a correlation between each signature and individual leukaemia sample, and shown by a heatmap. Yellow indicates positive correlation, and red indicates negative ones. Identified clusters of haematopoietic cell signatures are shown by colours of cell population names and dendrograms. Rows are individual patients. The disease categories are shown by colours on the left side of the heatmap. This analysis did not aim to classify disease samples, therefore reasonably, hierarchical clustering of rows did not classify disease samples. Abbreviations: CFU, colony forming unit; HSC, haematopoietic stem cell; CMP, common myeloid progenitor; GMP, granulocyte monocyte progenitor; MEP, megakaryocyte-erythroid progenitor.(EPS)Click here for additional data file.Figure S2Boxplots showing the distributions of gene signature score in leukaemic disease samples for indicated haematopoietic cell populations: (a) Mature B cell class switched; (b) Neutrophilic metamyelocyte; (c) Granulocyte-monocyte progenitor (GMP); and (d) Pro B cell. Positive values indicate that samples showed positive correlations with the corresponding gene signature.(EPS)Click here for additional data file.Figure S3CCAM results of AML patient samples using CMP, CFU-monocyte, Neutrophilic metamyelocyte, and mature Neutrophils as explanatory variables. (a) All the AML subtypes are displayed with haematopoietic cell data. (b) Individual disease subtypes are separately displayed with haematopoietic cell data.(EPS)Click here for additional data file.Figure S4CCAM results of haematopoietic cell population data explained by major leukaemic phenotypes. CCAM was performed to analyse 38 hametopoietic cell populations using ‘average’ leukaemic cells  as explanatory variables. (a) Disease and Cell plot. CCAM result of 38 haematopoietic cell populations (211 samples) is displayed with the average phenotypes of four major leukaemia subtypes. (b) Baricentres of haematopoietic cell populations are shown. Developmental pathways for erythrocyte, megakaryocyte, granulocyte, monocyte, and B cell lineages are shown by arrows. (c) Spider plots of selected cell lineages. The barycentre of each cell population is connected with individual cell samples, showing the distributions of each cell population. Dashed arrows show the developmental pathways of each cell lineage. It is not sound to use too many explanatory variables in CCAM, as CCAM regresses main data on explanatory variables, which is one of the major limitations of this method. Thus, in order to analyse the differentiation pathways of many normal haematopoietic cell populations in terms of the features of leukaemias, haematopoietic cell data has to be treated as main data, using leukaemic data as explanatory variables (note that this is the opposite way of (EPS)Click here for additional data file.Figure S5Comparison of the proposed method using CCA and a conventional multivariate analysis, principal component analysis (PCA). The CCA results are from the analysis in (EPS)Click here for additional data file.Figure S6Three-dimensional plot of the HSC-CMP score, gene expression (average expression in each cell population), and four haematopoietic populations used for making the HSC-CMP score. CCAM assigned scores to both MDS disease samples and these genes. High scores were associated with MDS patients with poor prognosis c.f.  and 5, w(EPS)Click here for additional data file.Figure S7S, and those of well-characterized cell subsets are preanalysed by PCA and Canonical Variate Criterion to produce Graphical representation of the proposed method. Microarray data of undefined cells are standardized and designated as (EPS)Click here for additional data file.Table S1Excel file of the list of genes that were used for constructing the HSC-CMP score, the gene scores of the CCAM result, and the biological/haematological features of these genes.(XLS)Click here for additional data file.File S1R script for CCAM.(R)Click here for additional data file.