The volume of RNA-Seq data sets in public repositories has been expanding exponentially, providing unprecedented opportunities to study gene expression regulation. Because degraded RNA samples, such as those collected from post-mortem tissues, can result in distinct expression profiles with potential biases, a particularly important step in mining these data is quality control. Here we develop a method named mRIN to directly assess mRNA integrity from RNA-Seq data at the sample and individual gene level. We systematically analyse large-scale RNA-Seq data sets of the human brain transcriptome generated by different consortia. Our analysis demonstrates that 3′ bias resulting from partial RNA fragmentation in post-mortem tissues has a marked impact on global expression profiles, and that mRIN effectively identifies samples with different levels of mRNA degradation. Unexpectedly, this process has a reproducible and gene-specific component, and transcripts with different stabilities are associated with distinct functions and structural features reminiscent of mRNA decay in living cells. With the rapid increase in the volume of publically available RNA-seq data, quality control is an increasingly important consideration. Here Feng et al. develop mRIN, a method to directly assess mRNA integrity, and show that RNA degradation in post-mortem samples has a strong impact on global expression profiles. Several quality control (QC) metrics have recently been developed to detect such bias, flag problematic samples and identify other technical issues12Several challenges are present in QC of RNA-Seq data derived from post-mortem tissues available in the public domain. First, there is currently no consensus with regard to the RIN required to generate high-quality data, and the criteria vary substantially depending on the specific studies used. Thus it is not unusual that re-analysis or meta-analysis of published data sets prefers more stringent filtering than the original studies that generated the data sets. Second, in public repositories, RINs of RNA samples used to generate RNA-Seq libraries are frequently not reported and thus re-analysis in follow-up studies has to perform QC on the RNA-Seq data directly. Third, the use of RINs as a measure of RNA-Seq data quality can have potential caveats. In particular, while a RIN is derived largely based on the integrity of ribosomal RNAs (rRNAs)To address these challenges, here we develop a method to estimate mRNA integrity number (mRIN) from RNA-Seq data sets directly based on quantitative modelling of the 3′ bias of read coverage. We applied this method to the large-scale RNA-Seq data sets generated by the BrainSpan and GTEx projects to demonstrate its effectiveness in quantifying mRNA degradation that markedly affected the global expression profiles. Unexpectedly, our method also suggests that the degree of degradation varies among different genes, and a substantial fraction of the variation can be explained by functional and structural features of the associated transcripts.The mechanism of RNA degradation in post-mortem samples during tissue necrosis is not well understood. The 3′ bias observed in RNA-Seq data could arise from RNA degradation by 5′ exonucleases15To demonstrate the importance of assessing mRNA integrity in RNA-Seq data derived from post-mortem tissue samples and to distinguish different types of mRNA degradation, we first used a large data set generated by the BrainSpan project. The dataset used in this analysis is composed of gene and exon quantifications of 578 samples derived from multiple brain structures of post-mortem human brains at different developmental stages . ImportaWe began with hierarchical clustering analysis of gene expression profilesOne possible cause of the observed skew in the RNA-Seq data is that RNA-Seq is more sensitive to RNA quality than microarrays are , due to the poly-dT selection step. This can also explain why postnatal brains were affected more severely than fetal brains, because for the latter, high-quality tissues are more accessible.Smg1 gene in samples derived from individual VIII_50, a 4-year-old male, which were profiled by both RNA-Seq and exon microarrays , and examined the gene expression profiles of each group as measured by the two platforms  have P>0.25. In contrast, among the 22 samples with RIN ≤6, only 6 (27%) have P>0.25. Therefore, the concordance of mRIN and RIN is remarkable, although the two methods estimate RNA integrity using very different approaches with different underlying assumptions.When we compared RIN and mRIN quantitatively, 24% of variance in RINs across samples can be explained by their mRINs  have minimal degradation and can be used as a surrogate of the ‘reference transcriptome'. The expression correlation of the remaining samples with the reference samples is expected to decrease depending on the severity of degradation.R between 0.24 and 0.50 for RIN and 0.34 and 0.48 for mRIN). Importantly, mRIN is more predictive than RIN in the cortex and cerebellum. Therefore, mRIN is very comparable to RIN in isolating degraded samples with altered expression profiles, although the two methods appear to capture both shared and distinct aspects of mRNA degradation, which can be explained in part by the source of degradation modelled by each method.To minimize the heterogeneity of gene expression among different brain regions, we analysed samples from the cortex, cerebellum and basal ganglia separately, as these regions display distinct expression profiles . When weBesides a per-sample summary of mRNA integrity by mRIN, our method has the advantage of being able to evaluate gene-specific degradation. In both the BrainSpan and GTEx data sets, not all genes appear to be affected by 3′ bias to the same degree, as one can tell from the expression profiles , but morρ=0.6, P<2.2 × 10−16, F-test; To investigate the mechanism of gene-specific degradation, we calculated a gene integrity score (GIS) by correlating mKS values of each gene and the associated mRINs across all samples . We foun−4 and <5 × 10−14 , respectively; We asked whether there is any functional bias of genes associated with transcript stability in post-mortem samples. Since the GIS scores show a continuum without obvious cutoffs to separate different groups of genes, we examined the top and bottom 1,000 genes and their gene ontologiesP<2.2 × 10−16, F-test); inclusion of 3′ untranslated region (UTR), coding sequence (CDS) and 5′ UTR sizes, as well as exon numbers together with the transcript length in a linear regression model only moderately increased the explained variance . Interestingly, while unstable transcripts tend to have a higher AU-content in 3′ UTR and CDS, they tend to have a higher GC content in the 5′ UTR . In this study, we developed a statistical method to evaluate mRNA integrity directly from RNA-Seq data by quantitatively modelling the 3′ bias of read coverage. Application of this method to two independent, large-scale RNA-Seq data sets profiling human post-mortem tissues demonstrated that degraded RNAs can result in strong 3′ bias that markedly skews global gene expression profiles with standard pipelines of data processing. Such bias may mask genuine biological differences such as brain region-specific expression or expression variation among different individuals, if it is not properly controlled. To our knowledge, this is the first systematic characterization of mRNA degradation in RNA-Seq data derived from post-mortem samples on such a large scale.post hoc assessment of mRNA quality directly from RNA-Seq data independent of RIN or other prior knowledge in the context of meta-analysis. We demonstrated the effectiveness of our method in comparison with RIN and other existing QC metrics in isolating degraded samples. It also has to be emphasized that a number of approaches and software tools have recently been developed to normalize biases in RNA-Seq data introduced by mRNA degradation and other technical issues121327282930Our analysis warrants additional caution in processing data from samples affected by degradation. Whether such samples should be excluded for analysis largely depends on specific studies and the extent to which the resulting bias can be minimized by computational normalization methods. In particular, the decision can differ between the original studies generating the data sets and follow-up studies that perform re-analysis or meta-analysis of published data. Unfortunately, the quality of RNA samples used for RNA-Seq could be somewhat obscure for follow-up analysis of published data, and sample RINs are sometimes not reported. Our intention was thus to develop a computational method to provide In living cells, control of mRNA stability is critical for both mRNA surveillance and post-transcriptional gene expression regulation. The molecular mechanisms underlying such controls are complex and only partially understood151532Through comparison of the expression profiles as measured by RNA-Seq and exon microarrays, our analysis suggests that partial fragmentation of mRNA is a major source of bias in RNA-Seq data derived from post-mortem tissue samples. How such fragments are generated and accumulated during tissue necrosis is not clear, but the process might be a combined consequence of several possible mechanismsAn unexpected finding of this study is that there seems to be a reproducible and gene-specific component of mRNA degradation in post-mortem samples, as recently noted in another tissue type3435373835403439mRIN provides a single-parameter measurement of mRNA degradation, or more specifically partial mRNA fragmentation, for each sample. This method is based on the global 3′ bias of RNA-Seq read coverage in libraries prepared from degraded RNAs using the standard protocols, including poly-dT selection. In this study, we used RefSeq transcripts as gene models to quantify 3′ bias; for genes with multiple RefSeq transcripts, the longest transcript was used as a representative. To avoid ambiguity in read assignment to genes, only those genes without an overlap with other neighbouring genes were included in this study.x (starting from the 3′ end of each transcript) as nx. The cumulative read coverage is denoted asAfter mapping RNA-Seq reads to the reference genome and exon junctions, the mapped reads are counted in each exonic nucleotide to obtain the read coverage profile along mRNA transcripts. The coverage profile is then transformed into a cumulative distribution at nucleotide resolution. Denote the number of reads at each exonic position and the total nucleotide coverage is thusL is the transcript length.where When there is no degradation of RNA or technical bias during library preparation and sequencing, RNA-Seq reads are expected to be uniformly distributed (the null distribution). In practice, certain amount of non-uniformity is always present, and a major source of such non-uniformity is the 3′ bias due to RNA degradation. Such deviation from the null distribution can be quantified by a modified KS statisticf(x) is the cumulative read coverage distribution at position x, f(x)=c(x)/h.where d, so that the KS statistic is positive in the presence of 3′ bias and negative in the presence of 5′ bias.Note that the direction of deviation is distinguished in To account for additional gene-specific bias  that cannot be explained by the 3′ bias, the KS statistics are median-centred across all samples for each gene to obtain mKS values.N genes:The mRIN of each sample is defined as the negative of average mKS statistics of all The negation is used so that degraded samples with 3′ bias have smaller mRINs analogous to RIN.μ=0. This is based on the assumption that the mKS statistics of different genes in a sample without degradation are independently and identically distributed. mRIN of the sample, which is the negative average of the mKS statistics across a large number of genes, is expected to converge to a normal distribution based on the central limit theorem. On the other hand, degraded RNA samples are expected to have more negative mRINs, resulting in a heavier tail on the left side of the distribution.We note that mRINs of non-degraded samples should follow a normal distribution with approximately a zero mean σ, from samples with positive mRINs (which are least degraded) by the scaled median absolute deviation. For each sample, a z-score and P value can then be derived to estimate the probability of observing a specific mRIN under the null hypothesis (using a single-sided test):We can estimate the s.d. of mRINs of the null distribution (no degradation), denoted as φ(ω)=N is the probability density function of the standard normal distribution.where We also consider a scenario in which a relatively large number of samples in the data set might be degraded, such that the mean mRIN of non-degraded samples is shifted from zero. To address this concern, we sought to develop a more robust approach to estimating the null distribution. In brief, we assume samples with mRINs above a certain cutoff are non-degraded and these samples follow a truncated normal distribution, whose parameters can be estimated analytically.α and samples with mRIN>α are regarded as non-degraded samples. mRINs of the non-degraded samples follow a truncated normal distribution,For simplicity, suppose we already know the cutoff of the mRIN β=+∞. The mean and variance of the truncated normal distribution are42where andμ and σ can be derived from the moment estimator by solving the following equation system,The parameters M0 and where α, we perform a step search of the optimal α from −0.05 to 0.05, stepped by 0.0005 and calculate the estimated parameter μ and σ. For each α, the goodness of fit is evaluated by comparing the cumulative distribution of truncated mRINs and samples from the fit normal distribution using a KS statistic D . This was the latest version available at the time of analysis, but an updated version as since become available at the time of writing. This data set is composed of a large panel of post-mortem human brain samples that span 13 developmental stages including fetal, neonatal and adult brains. For each donor, samples were collected from 8 to 16 brain structures. A total of 578 samples were profiled by RNA-Seq using poly-dT-selected mRNAs. In parallel, the BrainSpan project also used Affymetrix HuEx ST exon microarrays to profile gene expression on a largely overlapping set of samples. In total, the exon array data set is composed of 492 samples, among which 479 have matched RNA-Seq data. Poly-dT selection was not used in preparation of cDNAs for exon array hybridization; instead, cDNAs were generated by random priming, because rRNAs are not expected to hybridize with probes designed for mRNAs.For this study, we used an RNA-Seq data set of developing brain transcriptomes generated as part of the BrainSpan project (http://www.brainspan.org/static/help. These processed files were downloaded from http://www.brainspan.org/static/download.html.The RNA-Seq data were available publicly as read per kilobase per million (RPKM) values at the exon and gene level (GENCODE V3C). These values were derived from read mapping using ELAND2, followed by quantification using RSEQtoolsh (average RPKM × L) reflects the abundance and length of each transcript, but not the sequencing depth of each sample. However, assuming the sequencing depth is relatively uniform across different samples, we do not expect this to have significant impact on the results. To reduce uncertainty, we estimated the KS statistic only for genes with RPKM >2 in each sample and assigned a missing value otherwise. After we obtained the mKS matrix, 7,783 genes with mKS estimated in >50% of samples were used to calculate mRINs. For this study, we used the mixture model to estimate the parameters of the null distribution .To perform mRIN analysis, we used read coverage profiles derived from exon RPKM values, because the nucleotide-resolution read coverage profiles or the raw data were not available. In this case, the total read coverage 2-transformation, s.d.>1 and then median centred) and 6,550 of 17,604 genes in the microarray data .Centroid linkage hierarchical clustering of gene expression profiles presented in P=0.1). In each group, a heat map was generated with the predetermined order using each data set.To determine the order of samples and genes described in https://dbgap.ncbi.nlm.nih.gov). Among these, 317 samples with a read mapping rate ⩾0.67 (based on RNA-SeQC results) were used in our analysis, because our initial examination of the gene expression profiles suggested that samples with a low mapping rate have lower correlations with the other samples. There appear to be a few additional ‘outlier' samples as judged from their expression profiles  included a total of 410 brain samples with RNA-Seq data, matched RINs, gene expression quantification and RNA-SeQC results, which were obtained from the GTEx project through dbGaP . To reduce potential noise in estimating mRIN, we applied filters of genes based on KS statistic and gene expression RPKM values calculated by RNA-SeQC. We first excluded the top 5% of genes with the smallest s.d. in their KS statistics across samples. To remove genes that show strong 5′ bias, typically due to errors in transcript annotation, alternative RNA processing or other technical issues, we excluded genes with a median KS statistic across samples <0. Finally, 9,168 genes with KS estimated in >50% samples were kept to derive the mKS matrix and calculate mRIN for each sample. The parameters of the null normal distribution were estimated to be α=0.0035, μ=0.0034 and σ=0.033.We estimated KS statistics for each gene and sample with average read coverage To evaluate gene-specific integrity, we focused on 8,493 of 9,168 filtered genes above that are protein-coding genes with annotated 5′ UTR, CDS (whose length is multiple of three) and 3′ UTR. GIS is defined as the Pearson correlation between mKS values across all samples and their associated mRINs.To correlate GIS estimated from the GTEx and BrainSpan data sets, we considered only genes with ⩾5 exons, because for the BrainSpan data set the read coverage profile is available only at the per-exon level, hence it is more difficult to obtain a precise estimate of KS values and thus GIS for genes with single or few exons.2-transformed and median centred to calculate correlations of each sample with a subset of high-quality reference samples (defined by RIN⩾8 and mRIN⩾0). For each sample, the median correlation with the reference was then calculated. For a sample belonging to the reference, the median was calculated using the remaining samples in the reference set. To generate the gene expression heat maps shown in To calculate the correlation of gene expression profiles across samples, as presented in Representative RefSeq transcripts used to estimate mRIN were used to define the number of exons, whole-transcript length as well as the lengths of 5′ UTR, CDS and 3′ UTR. Exonic sequences of each of these regions were extracted to calculate GC content and search for motif sites. For AREs, we initially searched different variations To relate transcript stability with transcript features, we first generated bins of 100 genes and calculated the averages of each feature in bins and their correlation with the average GIS in respective bins. For regression analysis, we first applied Fisher transformation on GIS, ln[(1+GIS)/(1−GIS)]/2, so that it follows approximately a normal distribution. For the same reason, we also applied log transformation on length of whole transcripts, as well as lengths of 5′ UTR, CDS and 3′UTR. Linear regression analyses were first performed using individual groups of variables and then in combination, as summarized in Gene ontology analysis was performed using the online tool DAVIDhttp://zhanglab.c2b2.columbia.edu/index.php/mRIN. Hierarchical clustering was performed using cluster (http://bonsai.hgc.jp/~mdehoon/software/cluster/) and java treeview (http://jtreeview.sourceforge.net).A set of perl and R scripts were implemented to calculate the exonic read coverage, cumulative distribution and KS statistics, mRINs and the statistical significance. The software package and the documentation are available through How to cite this article: Feng, H. et al. mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data. Nat. Commun. 6:7816 doi: 10.1038/ncomms8816 (2015).Supplementary Figures 1-8List of samples and the associated mRINs in the BrainSpan dataset.List of brain samples and the associated mRINs in the GTEx dataset.List of genes, their transcript features and GIS.Gene Ontology terms associated with the most unstable transcripts. Marginally significant terms (Benjamini FDR>0.05) are shown in gray.Gene Ontology terms associated with the most stable transcripts. Marginally significant terms (Benjamini FDR>0.05) are shown in gray.


MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays. Normalization is an essential step of microarray data preprocessing MicroRNAs (miRNAs) are a prevalent class of small single-stranded non-coding RNAs that negatively regulate gene expression by inducing mRNA degradation and translational repression, and are involved in a wide variety of cellular functions such as proliferation, differentiation, and apoptosis In this paper, we report the results from an empirical evaluation of normalization methods using a pair of miRNA array datasets generated at Memorial Sloan Kettering Cancer Center The use of human tumor tissues in our study was approved by the Memorial Sloan Kettering Cancer Center Institutional Review Board.Analysis of variance (ANOVA) has been successfully used to model the relation between mRNA gene expression and sample group, which attributes gene expression variation to factors such as marker effect, sample effect, and stochastic noise Let Let In the setting of marker-specific comparisons , we assume that (1) Different from normalization, randomization requires no modeling of array effects and it removes their confounding effect by balancing them between two sample groups. The mean of the observed expression Hence the difference in the observed average expression between cases and controls isThat is, the difference in observed means is biased by The variance of the observed expression Thus the presence of array effects influences both the accuracy and precision of the estimate of The randomized dataset in our study included 192 arrays. Agilent miRNA arrays have an 8-plex block design, with eight arrays on each slide arranged as two rows and four columns. When assigning arrays to sample groups we used the blocked randomization design with row and column balance in order to ensure the best balance. There are six possible configurations where the numbers of cases and controls are equal on any row and any column of a slide We carefully planned our study for generating the randomized dataset. All 192 arrays were ordered from the same manufacture batch. Their hybridization and production were processed in one run by a single experienced technician at the Memorial Sloan Kettering Cancer Center Genomics Core Lab. As a result of the uniform array handling, We analyzed the data both with and without background subtraction, and found similar results in terms of the relative performance of normalization methods. We report the results for the analyses without background subtraction here. There was minimum variation among replicates for the same probe We assessed evidence against the null hypothesis of equivalent expression, using the t statistic comparing two sample groups in the randomized data We preprocessed the non-randomized data with array normalization followed by probe summarization on the log2 scale. We applied commonly-used methods for normalization and used the same approach to probe summarization as for the randomized dataset. The normalization methods we used were median normalization, quantile normalization, cyclic loess normalization, and variance stabilizing normalization . BrieflyThe preprocessed data were analyzed for differential expression using the same approach as that for the randomized dataset. The resulting p-values and the differential expression status based on the non-randomized data were compared with the differential expression status derived from the randomized data using ROC curves and cross tabulation, respectively For the purpose of evaluating the effect of normalization on the accuracy of biomarker discovery, we called the randomized data as the benchmark data and the non-randomized data as the test data. Among the 3,523 markers on the Agilent array, 351 markers (10%) were identified to be differentially expressed in the benchmark data, indicating a moderately abundant level of asymmetric differential expression. Without normalization, 1934 markers (55%) were identified to be differentially expressed at a p-value cutoff of 0.01 in the test data, which was associated with a true positive rate (TPR) of 185/351 (53%), a false positive rate (FPR) of 1749/3172 (55%), and a false discovery rate (FDR) of 90%. Almost all of the false positive markers had very low expression levels and some of the false negative markers had medium to high expression levels in the benchmark data .With normalization, the number of differentially expressed markers was reduced to 639  for median normalization, 708  for quantile normalization, 732  for cyclic loess normalization, and 723  for variance stabilizing normalization. We also evaluated how accurately the test-data p-values ranked the markers using the ROC curve and observed similar results . NormaliWe next examined whether an addition of a batch adjustment step before array normalization can further improve the accuracy of differential expression detection. We compared the accuracy of calling differentially expressed markers with versus without batch adjustment, in combination with each normalization method tested in our study. It showed that normalization alone called highly similar markers positive to normalization combined with standardization, and moderately similar to normalization combined with ComBat .When comThere are two possible mechanisms through which false negative  markers can occur as a result of array effects and attempted removal of array effects by normalization: one is by introducing bias to the data, and another is by increasing (or decreasing) the variability of the data. In order to examine these two possibilities we looked at scatter plots of the following summary statistics: (1) mean expression differences between two tumor groups for the benchmark data versus that for the test data, in order to look at the level of biases in the test data; and (2) marker-specific standard deviations for the benchmark data versus that for the test data, in order to look at the change of data variability.We generated similar scatter plots for the test data after median normalization . Median Similar plots showed that quantile normalization effectively corrected the bias caused by array effects; however, it overly compressed the variability of the data . The falWe utilized a pair of miRNA array datasets on the same set of tumor samples to perform an objective and absolute assessment of normalization performance under genuine data characteristics. Comparing with previous reports on the assessment of normalization methods for miRNA arrays, our approach provides an important alternative evaluation that challenges the critical assumption of the all-gene methods and offers insights on their performance when the assumptions are violated In the presence of array effects, normalization and batch adjustment can improve the accuracy of detecting differentially expressed markers. However, the number of false positive markers can still be close to the number of true positive markers as demonstrated in our study.There is a critical need to develop efficient methods to normalize miRNA array data with both effective bias correction and proper variability reduction so that miRNAs having disease relevance can be identified in an accurate manner.Figure S1Scatter plot comparing group means in the randomized dataset. True positive markers, false negative markers, and false positive markers, as determined in the non-randomized data  in comparison with the randomized dataset as the benchmark, are indicated as “x” in black, blue, and red, respectively. True negative markers are indicated as black dots.(DOCX)Click here for additional data file.Figure S2ROC curves comparing the two-sample t-statistic p-values for the test dataset with (A) no normalization, (B) median normalization, (C) quantile normalization, (D) cyclic loess normalization, and (E) variance stabilizing normalization, with the gold standard .(DOCX)Click here for additional data file.Figure S3Comparison of differential expression analysis of the test data before versus after batch adjustment. Each Venn diagram compares differentially expressed markers identified in the test data after normalization following no BEC (yellow circle) versus those with BEC (using either standardization (green circle) or ComBat (blue circle)).(DOCX)Click here for additional data file.Figure S4Results of differential expression analysis of the test data before and after batch adjustment, in comparison with the gold standard derived from the benchmark data. Each Venn diagram compares differentially expressed markers identified in the test data after normalization following no BEC (yellow circle) or BEC (using standardization (green circle) or ComBat (blue circle)) versus those identified in the benchmark data (red circle).(DOCX)Click here for additional data file.Table S1List of normalization methods examined in our study.(DOCX)Click here for additional data file.


Preeclampsia (PE) is a life-threatening hypertensive pathology of pregnancy affecting 3–5% of all pregnancies. To date, PE has no cure, early detection markers, or effective treatments short of the removal of what is thought to be the causative organ, the placenta, which may necessitate a preterm delivery. Additionally, numerous small placental microarray studies attempting to identify “PE-specific” genes have yielded inconsistent results. We therefore hypothesize that preeclampsia is a multifactorial disease encompassing several pathology subclasses, and that large cohort placental gene expression analysis will reveal these groups.To address our hypothesis, we utilized known bioinformatic methods to aggregate 7 microarray data sets across multiple platforms in order to generate a large data set of 173 patient samples, including 77 with preeclampsia. Unsupervised clustering of these patient samples revealed three distinct molecular subclasses of PE. This included a “canonical” PE subclass demonstrating elevated expression of known PE markers and genes associated with poor oxygenation and increased secretion, as well as two other subclasses potentially representing a poor maternal response to pregnancy and an immunological presentation of preeclampsia.Our analysis sheds new light on the heterogeneity of PE patients, and offers up additional avenues for future investigation. Hopefully, our subclassification of preeclampsia based on molecular diversity will finally lead to the development of robust diagnostics and patient-based treatments for this disorder. Preeclampsia (PE) is a multi-system disorder of pregnancy defined by the onset of maternal hypertension and proteinuria in the latter half of gestation. This pathology affects 3–5% of all pregnancies and is responsible for 63,000 maternal deaths worldwide each year . To dateThese challenges have led researchers to apply genome-wide profiling techniques, such as microarray analysis, in cases of PE in order to better understand the etiology of placental dysfunction in this disorder. The primary anticipated outcome of all microarray studies performed to date was the identification of differentially expressed genes in the PE placentae, as a cohesive group, compared to a control group. However, in the largest study performed   in other multi-factorial, heterogeneous diseases, such as cancer ,14, has Our literature search identified 38 previously published microarray studies examining gene expression within the PE placenta (as of March 2013), seven of which were found to meet our inclusion and exclusion criteria ,16–21 s Table 1Table 1. The combined set of PE samples and controls was treated as a single large data set and analyzed by unsupervised multivariate model-based clustering. This method employs the Bayesian Information Criterion to allow for the identification of an optimal number of sample clusters based solely on observed patterns of gene expression, independent of official clinical diagnosis, while preventing over fitting or under fitting of the data. In this case, clustering with the optimal model  revealed three distinct molecular groups of placental gene expression . SignifiDifferences in covariates may explain these unexpected results, as it has been reported that the occurrence of labor and fetal sex may alter placental gene expression ,23. We oTo determine why the control samples split into two clusters, we first tested for sampling bias in the placental biopsies themselves. The placenta is not a homogeneous structure and single samples may not accurately reflect the mean gene expression of the tissue ,25. Give. By assembling genes together into functional pathways, GSEA is capable of identifying potentially important collections of molecular changes (gene sets) that affect each group. This investigation revealed an over-representation of genes generally involved in reproduction and pregnancy in cluster 1 controls, along with genes associated with normal pregnancy processes such as intracellular transport, organelle function, and protein modification and activity . In cont. However. These rThere is great interest in the identification of biomarkers to predict preeclampsia prior to the onset of clinical symptoms. Several candidates have been proposed but all of these suffer from low sensitivity, in that not all PE patients are readily identified ,7. On thEncouraged by this result, we next investigated the ability of PE markers to distinguish between the controls and the preeclamptic samples as a cohesive group, as well as split into PE subclasses. Using a subjective binary comparison employed by most typical analyses of this disease, we obtained a list of the top 10 genes with increased expression in the preeclamptics compared to the controls, all of which had been previously identified as potential markers of PE , includiIn the absence of detailed patient and placental data, we used gene set enrichment analysis (GSEA) to characterize the differences in molecular pathology between the three subclasses of PE patients .Compared to cluster 2 and cluster 3 PE samples, the preeclamptics in cluster 1 were found to be enriched in very few gene sets, most of which were related to organelle membranes and envelopes, as well as protein catabolism . Down-reOn the other hand, the preeclamptics in cluster 2 displayed an over-representation of genes involved in feeding behaviour, B-cell activation, interferon-gamma production, and hormone activity and secretion, as well as an under-representation of genes associated with oxidative phosphorylation . AdditioDNA damage response signal transduction resulting in induction of apoptosis, response to other organism, and response to virus, which imply a potential pathogenic source of placental damage, and perhaps even preeclampsia, in this subclass. Down-regulated ontologies were involved in female pregnancy, organelle function and membranes, and intracellular transport  reaching statistical significance  in the preeclamptic samples compared to the control samples in both clusters . This waregulation of hormone secretion and feeding behaviour, which are terms previously observed as enriched to the PE samples in cluster 2  of 25% . On the luster 2 . Howeverluster 2 , Fig. 5,We hypothesized that previously observed heterogeneity in preeclampsia, leading to a lack of robust predictive biomarkers and effective treatments for this disorder, was due to the existence of multiple molecular forms of PE. The considerable differences between these masked molecular groups have made the identification of biomarkers difficult, as there is low correlation between gene expression and the clinical symptoms of hypertension and proteinuria. Additionally, the failure of large scale randomized controlled trials ,33 aimedThe surprising observation in our analysis was the discovery of both PE and control samples in clusters 1 and 3. The overarching question remains then: why are these PE patients and controls co-clustering? Genetically, these samples are grouping because they have very similar patterns of gene expression. Consequently, attempts at finding markers to separate the two phenotypes in each cluster identified very few genes with differential expression, while comparisons between clusters by GSEA unveiled comparable enrichments to the cluster 1 controls and PE samples  and to the cluster 3 controls and PE samples . We therefore propose the following potential explanations that, when combined, may account for this unexpected finding:First, it is possible that some of the PE patients, particularly in cluster 1, may have been misdiagnosed as preeclamptic, and were really afflicted with another maternal hypertensive disorder, such as gestational hypertension or chronic hypertension . This isAn additional explanation is poor or advantageous maternal adaptation to pregnancy. Pregnancy leads to many physiological changes in the mother , such asLastly, and with the strongest argument for the phenotype mixture in cluster 3, is the likelihood that despite the aggregation of 7 microarray data sets, our final sample size of 173 may still be too underpowered to identify all existing clusters. Evidence for this explanation is the cluster 2 PE-related gene sets found to be significantly enriched to the PE samples in cluster 3 compared to their co-clustered controls. This overlap may be anticipated from the PCA plot of cluster membership as most of the PE cluster 3 samples are near the border of cluster 2 while the control samples are farther away. Therefore, a further increase in sample size may allow for cluster 3 to resolve into a control subgroup and a preeclampsia subgroup, demonstrating a mild but still existent PE phenotype. Additional support for this theory exists in the enrichment of viral response genes to the cluster 3 PE samples only.response to virus ontology, most are known to be expressed in the placental trophoblast based on Protein Atlas database records [Of the 20 significant genes annotated to this  records  , compared to the samples in clusters 1 and 2. These ontologies have been previously linked to poor pregnancy outcome and the development of preeclampsia ,46. HoweRegardless, our observation of multiple molecular PE subclasses is significant as it confirms that standard clinical tests for preeclampsia are not sufficient to distinguish these different groups. Specifically, we have demonstrated that current PE biomarkers are excellent at identifying cluster 2 patients, but are inadequate for the recognition of cluster 1 and cluster 3 PE samples. Additionally, each of these subclasses display different previously published phenotypes of PE: cluster 2 PE samples demonstrate an over-representation of genes associated with HIF signalling, and hormone production and secretion; PE samples of cluster 3 are enriched in genes related to immune response; and cluster 1 PE samples likely represent a poor maternal response to pregnancy that presents without overt placental pathology, a group that is often overlooked in the literature ,53. WhatUnfortunately, the delivered placenta samples used in this analysis represent end stage disease and cannot directly identify the origin of pathology. This is a common issue to many diseased states, such as cancer, where the study of a tumour does not immediately inform the tumour-initiating event. Nevertheless, tumour analysis has led to great advances in identifying diagnostic and prognostic markers of cancer, and importantly, the identification of molecular heterogeneity of tumours has led to optimized treatment strategies. Discovering the true cause of a disease will require the creation of cellular and animal models, which are informed by unbiased genome wide analysis. Future work will determine if our observation of multiple outcomes (clusters) is really due to multiple initial causative insults or one insult that is modified by maternal and environmental agents to different end stages. Thus, while our results do not definitively solve PE, they do present a new path towards its solution using molecular approaches and individualized medicine.Although the use of deposited and archived gene expression data is an excellent resource, our analysis also highlights the necessity of having detailed clinical records available for all human patient studies such that a more complete covariate examination can be performed and gene to phenotype relationships can be tested. Additionally, given the mild sampling bias observed within the control placentas, this work emphasizes the importance of obtaining multiple biopsies per placenta in order to control for the high degree of variability in gene expression frequently observed across the same tissue . FinallyOverall, our analysis represents a significant advancement towards understanding the underlying molecular heterogeneity of preeclampsia. Similar large-scale approaches could also be taken for other major pathologies of pregnancy, such as intra-uterine growth restriction and gestational diabetes myelitis. Moving forward, it will be essential to confirm and expand on these results with additional large-scale studies, an effort under way in our groups. Future emphasis should be on linking histopathology with gene expression data, and identifying and classifying patients early in diagnosis, likely through testing serum samples for panels of genes unique to each cluster. It is likely that current molecular diagnostics, such as sFLT1 and sENG, may efficiently select the “canonical” PE subclass. There is therefore a need to focus on the other two clusters, as placental gene expression cannot readily separate the preeclamptics from controls.th week of pregnancy. Proteinuria was not consistently defined between studies; values ranged from 300mg-2g of protein in a 24 hour period or >2+ on a dipstick test. As all the patient data was de-identified and obtained from previously published reports, the authors obtained an ethics waiver from the University of Toronto Office of Research Ethics.Previously published preeclampsia-associated placental gene expression data sets were reviewed using a set of inclusion and exclusion criteria. Exclusion criteria were sampling at earlier stages of gestation (1st trimester), sampling of non-placental tissue, or sample set redundancy. While early sampling may reveal molecular origins, without longitudinal samples of the same patient over time, a correlation cannot be made between early gene expression patterns and end-stage expression patterns. Inclusion criteria were a minimum of three or more patient samples of a preeclamptic pathology (both early and late onset types), an array platform with more than 15,000 gene features, and the availability of raw data tables. A diagnosis of preeclampsia was defined as two or more episodes of hypertension (>140/90 mmHg) with proteinuria after the 20GEOquery library. Gene expression values were extracted from each GEO series and converted into log2 intensities. For most arrays, the deposited annotation files could be used with the exception of three studies  where the files were built using the annotationforge package from Bioconductor. The GSE25906 data set was batch corrected for the two indicated batches in the supplied annotation files, and 8 samples with fetal growth restriction (FGR) were removed from the GSE24129 data set. The individual sample sets were then aggregated into one array using the virtualArray package [The placental microarray data sets were loaded into R 3.0.1 from NCBI’s Gene Expression Omnibus (GEO)  using th package  , gestational age , nationality , and occurrence of labor  was added to the array for each sample. Fetal sex was typically not reported but was predicted based on the expression of two Y-chromosome genes: UTY and USP9Y. Chi-squared analysis was employed to test the significance of these patient variables on cluster membership. Lastly, principal variance component analysis (PVCA), using the pvca library, was performed on the full aggregate data set in order to determine the main sources of variability within the data.The control and PE samples were treated as a single dataset and subjected to unsupervised multivariate model-based clustering, using the mclust  package heatmap function and reversed heat colours such that the sample with the lowest mean endothelial-enriched or trophoblast-enriched expression value was coloured white and the sample with the highest value in each was coloured red. This was also done for each gene individually. Further inquiry into the splitting of the control samples was performed using the Molecular Signatures Database (MsigDB) collections associated with GSEA v2.1.0 [Given that many of the original studies reported obtaining their placenta samples from a single biopsy, the possible existence of a sampling bias was investigated as a possible cause for the splitting of the controls. This was done by calculating the mean expression of 35 genes known to be significantly up-regulated in endothelial cells as well as the mean expression of 20 genes significantly enriched in trophoblasts, across each of the controls. A scaled (0 mean and 1 variance) heatmap of these mean expression values was then produced using the A v2.1.0  against A v2.1.0 , with a limma [sm.density.compare function from the sm library, split by phenotype as well as PE subclass. The WEKA machine learning software package [The differential expression of FLT1 and ENG was visualized with a 3-dimensional PCA plot, using colour gradients to demonstrate increasing expression of FLT1 (green to orange) and ENG (green to blue). Samples with low levels of both markers were therefore colored green, while those with elevated expression of both colored pink. A list of the top 10 genes with significantly increased expression in the PE samples compared to the controls was obtained from limma . Two data set, two-colour enrichment maps were employed to simultaneously determine gene sets over- and under-represented in a given PE subclass compared to both remaining PE subclasses. Nodes were re-coloured to reflect the PE subclass in question, and networks of related ontologies were circled and assigned a group label.limma, with an adjusted p-value cut-off of 0.01. The PE samples in cluster 2 were compared to both subclasses of controls simultaneously using limma, and only genes demonstrating adjusted p-values < 0.01 and the same direction of differential expression (either up- or down-regulated) in both comparisons were deemed significant. The mean expression of the top 5 genes up-regulated in each PE subclass, compared to either their co-clustered controls or all controls, was calculated across each sample involved in the original comparison and 3 density plots of these values were produced using the sm.density.compare function. The WEKA machine learning software package was also used to evaluate the ability of these three sets of 5 genes to discriminate the PE subclasses from either their co-clustered controls or all controls, using a Naive Bayes classifier and 10-fold cross-validation. Marker performance was assessed by ROC plots for the PE samples only. Additionally, GSEA was employed to investigate pathway-level differences between the co-clustering preeclamptics and controls, using the same settings described above in the assessment of the splitting of the controls. Placental trophoblast expression for each gene found to be significantly enriched to the response to virus GO ontology was assessed using Human Protein Atlas [Lists of differentially expressed genes between the preeclamptics and controls in each of clusters 1 and 3 were obtained from in Atlas , and thein Atlas .S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.


Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to classify if a sample contains a bacterial, fungal, or mock-infection. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy  for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional test dataset comprising Cryptococcus neoformans infections. Furthermore, the classifier is robust to Gaussian noise, indicating correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances.Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens  E. coli  and PCR-based assays are currently the standard diagnosis techniques to detect causative pathogens. While BCs aim for the isolation, identification, and susceptibility tests of microorganisms , the Gram-positive bacterium Staphylococcus aureus ATCC25923 (1 × 106/ml) and the Gram-negative bacterium Escherichia coli ATCC25922 (4 × 103/ml) were added to anti-coagulated blood of healthy human donors  and incubated at 37°C with gentle rotation for 4 or 8 h. The samples of all pathogens cover three or four different donors with one or two samples each. Infected blood was collected and stored in PAXgene Blood RNA Tubes  to stabilize intracellular RNA until further use. RNA isolation was performed using the PAXgene Blood RNA Kit  corresponding to the manufacturer's instruction. The Illumina°ledR TotalPrep™ RNA Amplification Kit (Ambion) was used for RNA amplification and cRNA transcription. RNA concentrations and quality were assessed by NanoDrop 1000 (Thermo Scientific) and Agilent 2100 Bioanalyzer (Agilent Technologies). Expression levels of RNA samples were analyzed with Illumina°ledR HumanHT-12 v4 Expression BeadChip Kit (Illumina) following manufacturer's protocol. The chip data was background corrected and log-transformed by applying the functions “lumiR” and “lumiT” of the R package “lumi”  were removed, leaving 10449 genes for analysis. The microarray data have been deposited in NCBI's Gene Expression Omnibus .A human whole-blood model was used as described previously —as a vector for all samples.The normalization followed the approach of Vandesompele et al.  which is3) were used to determine the optimal number of genes for NF calculation. Then, more genes were successively included  as long as the inclusion leads to significant changes on the normalization factor. To quantify these changes, the pairwise variations of each two consecutive NFs were computed. As threshold, 0.15 was used as recommended by Vandesompele et al. A value surpassing this threshold indicate that the inclusion of another gene into calculation is necessary.Initially, the three most stable genes . Thus, we kept the parameter mtry on its default value, which is g is the number of genes of the input dataset.The random forest classifier was built using the “randomForest” package  by building random forests with 100,000 trees. The normalized dataset, which was reduced to the data of differentially expressed genes, was used as input.p be this proportion and let N be the number of possible classes , then the certainty score is calculated asWe scaled the certainty score to a range from 0 to 1. Before scaling, the score represents the proportion of class predictions from all trees of the random forest, which yield the same class as the final classification by the classifier. Let C. neoformans  dataset was generated identical to the other fungi data and quantified using the same chip technology. Expression levels were measured 4 h (3 donors) and 8 h (3 donors) post infection. Mock-infected control samples were simultaneously produced. Before classification, the expression intensities were normalized based on the reference genes which were determined previously without the C. neoformans data  was performed using the “cmdscale” function of R. After determining the Spearman correlation of the samples of the normalized Our first step in building a classifier which discriminates between bacterial and fungal infection is to normalize gene expression values with help of reference genes Figure . The motCTBP1, TBP, and CRY2 as the most stable ones. When comparing the pairwise variations of all successive NFs, we found that using only the three most stably expressed genes is sufficient for producing an accurate NF  and then selecting genes which are most important for accurate classification Figure . To idenTo identify transcriptional responses related to blood infection by fungi or bacteria we determined DEGs for the three classes. A gene is regarded as a DEG for one class, if its expression levels are significantly different to both other classes merged together . In this way, we found 204 DEGs for the fungal class, 184 for the bacterial class, and 150 for the mock-infected class. Of these genes, 68 were identified as differentially expressed in all 3 classes simultaneously. The union of the three sets of DEGs comprises a total of 402 genes.We further reduced the set of DEGs to genes being most important for accurate classification. To identify these genes, we used the variable importance measure integrated in the random forest algorithm . We selected the top 11, 6, and 21 genes for the classes fungal, bacterial, and mock-infected, respectively, as these genes form groups covering the highest importance values  , vesicle transport (VPS18), cell proliferation , cell adhesion (VCAN), ion transport (FXYD6), or iron uptake (TFRC).At a significance level of 0.05 we found 32 enriched GO terms connected to the identified biomarker genes  was down-regulated, while the other five genes showed up-regulation. For the fungal class, all 11 selected genes were up-regulated. We observed different patterns for the genes of the mock-infected class. Twenty of the 21 genes were down-regulated in the control samples and one gene (VCAN) was up-regulated.Examining the expression signatures of the selected genes Figure , we discTaken together, our feature selection approach was able to identify biomarker genes, which have been shown to be involved in sepsis and also cover a broad range of biological processes.To determine if an infecting pathogen of an unknown whole-blood sample is of fungal or bacterial origin, the sample is classified using the expression data of the selected biomarkers. We accomplish the classification by a random forest Breiman,  classifiC. neoformans. An additional approach to assess a classifiers performance is cross-validation. Cross-validation emulates independent test sets in an iterative technique and in this way resolves the need for true test data. Furthermore, we evaluate the ability of the classifier to handle fluctuations in the expression values by classifying samples after adding random noise to the data .Having built our classifier, we next studied its performance in distinguishing between fungal or bacterial blood infection. Our aim was to accurately classify new samples by the given classification model. Therefore, the performance assessment methods have to yield unbiased accuracy rates. To get unbiased estimates of accuracy, the samples for testing the classifier should be independent from the samples for training the classifier. We fulfilled this requirement with additionally independently created data comprising RNA expression measurements of human whole-blood samples infected with C. neoformans. The data comprises 6 samples of fungal infection and 6 mock-infected controls . Being part of the phylum of Basidiomycota, C. neoformans is a phylogenetically and morphologically very different fungus compared to C. albicans and A. fumigatus, both belonging to the phylum of Ascomycota . One sample was wrongly classified as mock-infected. All classifications of the mock-infected samples were performed correctly. In this way, we achieved an overall accuracy rate of 91.7%. The sensitivities are 83.3 and 100%, while the specificities are 100 and 83.3% for fungal and mock-infected class, respectively Table . We examThe difference in the accuracy values between the two classes is also reflected in the certainty scores. We obtained an average certainty of 0.475 (± 0.190) for all fungal samples, whereas for the mock-infected samples we achieved an average score of 0.810 (± 0.165). When splitting the fungal specimen into falsely and correctly classified ones, the observed certainty value for the misclassified sample is higher, 0.654, than for the right classifications, 0.439.When the sample size of a study is relatively small, it is preferred to use all available samples in feature selection and training. However, this leads to a lack of test data. Cross-validation is a widely used method to overcome this problem by emulating independent test sets without using additional datasets. It works by iteratively setting aside samples for testing, while the remaining samples are used to train the model. The split is performed in the way that each sample of the data is exactly once in the test set. In this way, cross-validation guards against overfitting.To estimate how accurate the classifier will perform on independent data, we carried out a stratified 10-fold cross-validation (CV). It is important that CV encompasses all feature selection steps, as otherwise a selection bias is induced . The support vector machine as well as the naïve Bayes method yielded the same classifications of all samples as the random forest model. The fact that the three classification methods are very different in their functional principles and the results are unaffected by the choice of the model indicates that the selected biomarker genes are robust.Using the biomarker genes, we trained a random forest classifier to classify the pathogen type in whole-blood samples. Random forest provides several advantages making it suitable for this study. It is fast in training and testing, supports multiclass classifications and provides the variable importance for evaluating the input features. With this embedded measure, we were able to select the best class-separating genes leading to a small set of biomarkers. There are further classification methods like support vector machines or naïve Bayes classifiers, which were successfully applied on microarray data in other studies (Kelemen et al., The certainty score based on the votes of the trees provides an easy-to-compare measure for assessing the classification quality. It directly reflects the ability of the classification model to properly classify the input data. This means, a class prediction with a high certainty score is more likely to be correct, than one with a low score. One possible application case for this measure is the introduction of a threshold, followed by the removal of low-scoring classifications.C. neoformans, is phylogenetically very different from C. albicans and A. fumigatus. These differences can lead to varieties in the transcriptional response of the host. However, the accuracy value of about 92% indicate that the selected biomarker genes are largely unaffected. Therefore, these genes are general indicators for whole-blood infections caused by fungi. The MDS analysis revealed that the misclassified fungal sample shows a greater similarity to the specimen of the mock-infected class than to the fungal cases. Although the divergence with the other fungal samples is only small, the differences are sufficient for wrong classification. Consequently, the correct classifications of the C. neoformans samples are possibly unsure. Indeed, the certainty values are much lower for the fungal class, compared to the mock-infected controls. Furthermore, we were surprised to find the certainty score of the misclassified sample being higher than the average score of the remaining fungal specimen. This observation confirms the assumption that the prediction of C. neoformans as fungal infected blood sample is a difficult task for the classifier, but still leads to mostly correct results.We tested the classifier with an additional dataset comprising whole-blood samples of fungal infection and mock-infected controls. The medically important fungus used for these additional samples, C. neoformans dataset, but also when testing it with stratified 10-fold CV. This broadly used performance assessment technique iteratively estimates the accuracy of a prediction model without an independent dataset. The two misclassifications in this test appeared for fungal and bacterial class. The predictions of the fungal and the bacterial class also exhibit the lowest values and the largest fluctuations of the certainty scores. However, it should be noted that the average scores are still high, as 0.795 is the smallest of them.High accuracy values were not only achieved when validating the classifier with the additional In summary, the results of the assessments by using an additional dataset of fungal infection, i.e., the external validation, as well as by performing a CV, i.e., the internal validation, are promising. Most of the tested samples were correctly classified, although in some cases right classifications were accompanied by low certainty scores.We also performed a noise-robustness test to examine whether the classifier can compansate fluctuations in the expression data. The high accuracy rates indicate that the indentified biomarkers are robust with respect to changes in their expression intensities. This robustness is important for a potential clinical application, where patients are of different age, sex, medication, and health condition and thus expression intensities of the same genes will vary between these patients.The experimental model of this work comprises the infection of blood from healthy human donors with typical sepsis causing microorganisms. Although we gained important insights into the transcriptional response on the pathogens, our findings possibly can not be directly utilized for clinical application. To achieve that, further analyses on gene expression data from septic patients as well as functional follow-up studies have to be performed. Unfortunately, whole-genome expression data from septic patients where the causing pathogen is known is rare in publicly accessible databases. Especially, datasets comprising the transcriptional response to fungal induced sepsis are scarce. Thus, we lack the basis for more clinical relevant investigations, which is why it remains an open task for future research. Furthermore, it should be noted that the presented classifier can not be used to identify the infecting species. Rather it is supposed to answer the question if the pathogen is of bacterial or fungal origin and whether or not it is necessary to administer antimycotics instead of antibiotics. To initiate a species dependent therapy, more requirements have to be fulfilled, e.g., in case of a bacterial infection, the appropriate antibiotic has to be determined by an antibiogram.In this study we present an effective selection of genes showing characteristic expression patterns depending on the type of the infectious organism. The resulting small gene set was used to train a fast and accurate random forest classifier, which performs well in predicting the class of the pathogen. Examining the transcriptional footprint of the sepsis causing microorganism in the blood of the host is a promising approach for quick pathogen identification. With the presented classification model we meet the increasing challenge of fungal induced septic infections requiring novel detection methods.AD did the bioinformatic analysis and co-wrote the manuscript. KH performed the experiments, generated the data, and co-wrote the manuscript. MW discussed the analysis and co-wrote the manuscript. RG, OK, and JL designed the research and co-wrote the manuscript.The Associate Editor, Tunahan Cakir, declares that, despite collaborating on the Frontiers Research Topic “Endothelial cell dysfunction in pathogen-induced hemorrhagic fevers” with the author Reinhard Guthke, the review process was handled objectively and no conflict of interest exists. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Epigenetic alterations are known to correlate with changes in gene expression among various diseases including cancers. However, quantitative models that accurately predict the up or down regulation of gene expression are currently lacking.A new machine learning-based method of gene expression prediction is developed in the context of lung cancer. This method uses the Illumina Infinium HumanMethylation450K Beadchip CpG methylation array data from paired lung cancer and adjacent normal tissues in The Cancer Genome Atlas (TCGA) and histone modification marker CHIP-Seq data from the ENCODE project, to predict the differential expression of RNA-Seq data in TCGA lung cancers. It considers a comprehensive list of 1424 features spanning the four categories of CpG methylation, histone H3 methylation modification, nucleotide composition, and conservation. Various feature selection and classification methods are compared to select the best model over 10-fold cross-validation in the training data set.A best model comprising 67 features is chosen by ReliefF based feature selection and random forest classification method, with AUC = 0.864 from the 10-fold cross-validation of the training set and AUC = 0.836 from the testing set. The selected features cover all four data types, with histone H3 methylation modification (32 features) and CpG methylation (15 features) being most abundant. Among the dropping-off tests of individual data-type based features, removal of CpG methylation feature leads to the most reduction in model performance. In the best model, 19 selected features are from the promoter regions (TSS200 and TSS1500), highest among all locations relative to transcripts. Sequential dropping-off of CpG methylation features relative to different regions on the protein coding transcripts shows that promoter regions contribute most significantly to the accurate prediction of gene expression.By considering a comprehensive list of epigenomic and genomic features, we have constructed an accurate model to predict transcriptomic differential expression, exemplified in lung cancer. Epigenetics is a rapidly expanding biological field recently. Aberrant epigenetic modifications are associated with many different diseases including cancers and neurodevelopmental disorders . Much woDNA methylation and histone modification are two major mechanisms of epigenetic regulation. The most widely researched type of DNA methylation in human is the cytosine methylation of CpG islands and their associated regions, such as CpG shores . CpG metHistone modification is another type of important epigenetic modification . HistoneAlthough integrative analyses on gene expression and epigenetics regulation abound throughout the literature ,9, it isSeveral types of high throughput data were used to extract features or classification responses. These include the CpG methylation array data from 50 paired cancer and adjacent normal tissues, three types of histone marker CHIP-Seq data from cancer and normal cell lines, genomic nucleotide sequence and conservation data, and RNA-Seq expression data from samples that have coupled methylation data.The Cancer Genome Atlas (TCGA) Methylation data from Illumina's Infinium HumanMethylation450 Beadchip (Illumina 450k) were used to extract CpG methylation related features, according to their annotation file. The genomic coordinates of CpG, their exons and coding regions were obtained from the Illumina annotation file. Since the annotation file only provided information of transcripts, exons, and coding DNA sequences (CDS), we re-annotated the protein coding genes using the Illumina iGenomes hg19 Refseq annotation in order to extract more comprehensive information from other regions of the transcripts: all introns , as well as first and last exons, untranslated regions in the 5' and 3' direction , and a "single exon" or "single intron" designation for transcripts that only had a single exon or single intron.http://genome.ucsc.edu. [Three sets of histone marker CHIP-Seq data, H3k4me3, H3k27me3, and H3k36me3, were considered from two cell lines: A549 cell line (0.2% EtOH treatment) from the lung carcinoma tissue, and SAEC normal lung epithelial cell line (no treatment). Raw CHIP-Seq data were downloaded from the Broad Institute/Bernstein Lab at the Massachusetts General Hospital/Harvard Medical School and the University of Washington in collaboration with the ENCODE project via the UCSC genome browser at csc.edu. ,11. The csc.edu. , followecsc.edu. . The alicsc.edu. . A custoNucleotide composition data were extracted from hg19 genome FASTA files downloaded from the UCSC genome browser. Conservation scores across three classes of species: vertebrates, primates, and placental animals, were also considered. PhastCons46Way scores were downloaded from the UCSC genome browser ,15. Conshttp://cancergenome.nih.gov. Lung adenocarcinoma and lung squamous cell carcinoma data were combined for this project, as they are two subtypes of non-small cell lung cancer. Differential expression analysis was done with the DESeq2 package in R [RNA-Seq gene expression data from lung cancer samples with coupled CpG methylation data were downloaded from TCGA Research Network: age in R . In caseage in R  and 2) hData Processing), unless noted otherwise.The extracted features are categorized into four major sub-groups. All features were considered on a segment-wise basis  between the cancer and normal tissues as well as the average methylation  of each CpG site across the two types of tissue [numHyper) and hypomethylated probes (numHypo) on a segment of a given transcript. For example, first_exon_numHyper refers to the number of hypermethylated probes on the first exon. Two other types of features are the average of logFC and avgMval of all CpG probes on a segment of the transcript, e.g. the average logFC of all probes on the first exon of a given transcript (first_exon_avglogFC).Differential expression of the methylated CpG sites was processed using the f tissue . A positmaxHypoTo5, minHypoTo5, avgHypoTo5, maxHypoTo3, minHypoTo3, avgHypoTo3, maxHyperTo5, minHyperTo5, avgHyperTo5, maxHyperTo3, minHyperTo3, and avgHyperTo3).Special effort was paid to compute distances of CpG probes to exon-exon junctions. Given that one or more CpG sites may exist on the individual exon segments of a transcript (including the first and last exons), transcript-level maximum, minimum and average distances of any hyper/hypo-methylated probe to the nearest 5' or 3' exon-exon junction were computed . In order to compare histone modification between the cancer and non-cancer cell types, the differences of the reads between them were divided by the average of the two (e.g. a feature named first_exon_A4_minus_S4_divavg).After the alignment of raw histone marker data (see Data Processing), the aligned histone marker reads were intersected with the segments of each transcript using the  package . The hisIn each segment of the transcript, four different types of nucleotide features were extracted: single nucleotide composition, dinucleotide composition, trinucleotide composition, and the length of each segment. Nucleotide sequences of Hg19 reference genome were processed using the Biostrings library in R .first_exon_vertebrate emerge from this set.Conservation score per segment was calculated as the arithmetic mean of the conservation score per nucleotide in that segment. Three separate sets of conservation scores with different comparative species were extracted from UCSC genome browser - vertebrate, primate, or placental. Thus, features such as Three feature selection methods were considered: Correlation Feature Selection (CFS) , Gain RaCFS is based on mutual information, a non-linear measure of correlation. CFS selects an approximately optimal set of features to maximize the relevance and minimize redundancy. Relevance is the correlation of a feature to the class (up-regulated or down-regulated gene expression) measured by mutual information, whereas redundancy is the correlation between two features. Redundancy between selected features is minimized to keep the number of selected feature small.The Gain Ratio is an improved method of Information Gain (IG). Both feature selection methods employ a decision tree in their respective algorithms. The Gain Ratio, by name, is a ratio of IG, but it overcomes the bias of IG which favors features with more data.k near-hits and near-misses and takes their averages, as opposed to one nearest hit or miss from Relief. k =10 was sufficient to obtain satisfactory results [ReliefF is an improved feature selection method from Relief. Relief uses the Manhattan distance of its nearest hit and miss from a random instance to continuously update a weight vector, which is then used to calculate a relevance score. Features above a certain relevance threshold are considered "selected" . ReliefF results .CFS is the only method that has a built-in system for selecting the number of features. Gain Ratio and ReliefF both work as ranker systems, meaning every input has a matching respective ranked output. In order to ensure fairness between feature selection methods, we matched the numbers of selected features from Gain Ratio and ReliefF to be the same as determined by CFS.The data were split into training and testing sets. The training set constituted 80% of the up-regulated and down-regulated genes, and the testing set constituted the remaining 20% genes. The training data set underwent 10-fold cross validation on various combinations of feature selection and classification methods, in order to obtain the best model.Feature Selection section.After determining the best model, two sets of drop-off tests were conducted. The first set of tests considered the effect of data types, including nucleotide composition, histone markers and methylation data, on the performances of sub-models. The second set of drop-off tests considered the effects of different segments on transcripts, including gene body, exons, introns, UTRs, TSS1500 and TSS200, on the methylation CpG methylation data based sub-models. For each drop-off test, a set of features was removed from the original input features prior to the feature selection and classification. Subsequently the same ReliefF feature selection and RF classification for the drop-off tests were performed as described in the previous https://github.com/lanagarmire/epiPredictorWeka 3 data mining software  was usedFour types of input data were used to extract the features including the Illumina 450K CpG methylation array data from cancer and normal tissues, three types of histone H3 marker CHIP-Seq data from cancer and normal cell lines, genomic nucleotide sequence and conservation data, and RNA-Seq gene expression data from samples with coupled CpG methylation data. In total, we calculated 1424 features and summarized the features by column. These features can be divided into two categories  are clustered with histone modification features, suggesting collinearity between these two types of features, as shown by others [The largest group of selected features is the histone modification features (32 features), followed by the methylation features (15 features)  Figure . DroppinWe also compared the effect of removing both nucleotide and histone features on model performance, as compared to removing either of them alone. As expected, removing both nucleotide and histone features gives the lowest AUCs, lowest accuracies and lowest F-measures in both training and testing sets. However, it leads to higher MCC than removing just histones does in the testing set. This suggests that there might be some overfitting with regards to the nucleotide feature set, which accounts for the majority (83%) of features prior to feature selection.Given that removing methylation features causes the most reduction of model performance among the single feature set drop-off Figure , we nextAll categories of genomic locations provide relevant useful information that contributes to better prediction of gene expression, as each of the sequential feature set drop-off decreased the performance of the model in both training and testing sets. Compared to the removal of first exon and intron regions, and removal of the UTRs and the rest of the gene body, removal of TSS1500 leads to the largest reduction in all four metrics, confirming the importance of the promoter region in regulating gene expression. Even when only TSS200 features are considered, an AUC of 0.638 and 0.636 are obtained in the testing and training sets respectively, suggesting that CpG methylation status in TSS200 is still somewhat predictive of gene expression. However, a more accurate prediction using methylation features should arise from all locations associated with the transcripts.Although currently integrative analyses between gene expression and epigenetic modification exist, we have found that quantitative models using epigenetic information to accurately predict the up or down regulation of gene expression are currently lacking. There are often cases where researchers can only obtain reliable epigenetics data, but not gene expression data. For example, if the samples are archived and processed by FFPE , one can still perform epigenomics measurements, but not the gene expression experiments due to the degradation of mRNA in the samples. More importantly, a predictive method such as ours can efficiently facilitate the bench scientists to narrow down the candidate lists and conduct gene expression validation, especially when the epigenetics information is the only data handy.All four types of data  exist in the 67 features that are selected by the best model, indicating that all of them contribute to the accurate prediction of gene expression. Moreover, selected features of the same data type tend to cluster together on the correlation matrix among the features, suggesting that the relationship within the same data type is closer than the relationship between different data types. As expected, histone modification and CpG methylation features are the largest two groups among the four types of data, signifying their importance to predict gene expression. Since nonlinear classification methods perform slightly better than linear classification methods, it suggests that interactions do exist between different types of data. This is supported by numerous literatures that enzymes responsible for CpG methylation also interact with histone modification events ,33.Besides the value of predicting gene expression, our models also provide insights into the relative importance of different epigenomics/genome data, as well as the genomic locations. We found that CpG methylation features have more predictive values for differential gene expression, compared to the three types of histone H3 modification data. Although other kinds of histone modification data can also be obtained to increase the predictive values of histone modification data, it is much more costly to obtain them relative to the CpG methylation data (the cost of CHIP-Seq on each of the histone modification marker is similar to an entire CpG methylation array). Therefore, practically speaking, when the budget is a constraining factor, we suggest that assays on CpG methylation should be considered with priority in predicting differential gene expression. Moreover, the results of our models demonstrate that all genomic locations relative to each transcript, including promoters, exons and gene bodies, provide useful information to predict gene expression alternation. Although the CpG methylation signals from the promoters region are more important, the methylation signals from other regions, such as exons, introns and UTRs are indicative of changes in the gene expression as well.Worth noticing, a lot of features that are extracted on methylation and histone modification are naturally based on the annotations from Illumina 450K array platform for DNA methylation. There may be bias on the number of features that are hand coded in the model. To address potential issue, we changed TSS200_GC to TSS150_GC in our model and obtained an AUC = .861 (compared to 0.864) for cross fold validation on the training set and an AUC = .834 (compared to 0.836) for the testing set. Therefore, we think the bias due to relying on the nomenclatures from Illumina's annotation is small.We should point out that our current model does not include all histone modification data, but only three widely used methylation markers on histone H3 . Moreover, the histone H3 data are drawn from ENCODE cell lines, since the TCGA samples do not have such data. The heterogeneity of the sample resources could affect the accuracy of the model. When more histone marker data coupled with DNA methylation and RNA-Seq data become publicly available for lung cancer, we can include them to achieve a better model. In the ideal setting, we would like to build a predictive model that has multiple types of epigenomics data obtained from the same samples. Another potential concern is overfitting in the classification model. However, we split the dataset into training and independent testing subsets and show the model performs comparably well on the holdout testing subset. We believe that the model can be replicated if we can identify paired RNA-Seq and methylation data. In fact, we had originally built this model on a private data set, which also achieved an AUC of more than 0.80. Additionally, Figure A new model based on epigenomics data is proposed to predict transcriptome-level differential gene expression in lung cancers. Dropping-off feature sets by data type shows that CpG methylation features are most important for the prediction. Furthermore, methylation features on all genomic regions relative to protein coding genes contribute to the differential gene expression, within which promoter regions are most important.The authors declare that they have no competing interests.LXG envisioned the project and supervised the work. TC initiated the project. JL and TC designed and implemented the project. SJ assisted the project. JL, TC and LXG wrote the manuscript. All authors have read, revised and approved the final manuscript.Table S1. Selected 67 features in the best model sorted by category and their frequencyClick here for file