MLL2, and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status (p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of  Bladder cancer is the 9th most prevalent cause of cancer worldwide and the 2nd most common genitourinary malignancy, with transitional cell carcinomas comprising about 90% of primary bladder tumors . In 2019https://portal.gdc.cancr.gov/) using the R package “TCGAbiolinks” . For the purpose of the present study, 196 samples were selected as the TCGA cohort including 49 samples with LN metastasis only (LN+) and 147 without any metastasis (LN–). Two independent cohorts were gathered for validation including one obtained from Gene Expression Omnibus (https://www/ncbi.nlm.nih.gov/geo/) (GEO cohort) by using R package “GEOquery” with a query of GSE106534 and another that is publicly available through the Memorial Sloan-Kettering Cancer Center (MSKCC cohort) cBioPortal for Cancer Genomics. The GEO cohort contains five LN+ and five LN– bladder tissues, of which RNA was extracted and hybridized on an Illumina Hiseq 2500 (Molecular data were obtained from The Cancer Genome Atlas Project (TCGA) patients diagnosed with bladder urothelial carcinoma. Transcriptome HTSeq-counts data of TCGA-BLCA project was obtained from the Genomic Data Commons (iolinks” . Somaticseq 2500 . The MSKseq 2500 . SurvivaEnsembl ID for genes (protein-coding mRNAs) was annotated in GENCODE27 to generate Gene Symbol name. Gene type of protein-coding was selected for mRNAs. We calculated the number of fragments per kilobase of non-overlapped exons per million fragments mapped (FPKM) first and subsequently transferred FPKM into transcripts per kilobase million (TPM) values, which are more similar to those resulting from microarrays and more comparable between samples . To reduDESeq2” with the standard comparison mode between the two experimental conditions  was performed by R package “clusterProfiler”  to infer significant cancer mutated genes (q < 0.05) across the two classes currently identified with default parameters. Tumor mutation burden was computed by summing all kinds of non-silent mutation. Mutation landscape oncoprint was drawn by R package “ComplexHeatmap”  regression, which is often applied as a dimensionally reduction technique, was performed on the training set to select primary predicative features. Ten-fold cross validation was performed to tune the optimal value of lambda (λ) that gives the minimum mean cross-validated error. A score was calculated for each sample via a linear combination of the selected features, namely LNM signature, weighted by the corresponding coefficients. The potential association of the LNM signature with LN status was first assessed in the training set and then validated in the testing set by using the Mann-Whitney p < 0.05 were retained in the prognostic model. A LNM nomogram was built by R package “regplot” as a quantitative tool for clinicians for individualized prediction of LN metastasis probability.Multivariable logistic regression analysis on the training set began with the following candidate predictors: LNM signature and significant frequent mutations. Those with respective pROC” and “rms”. Independent validation for LNM-score was tested in the GEO and MSKCC cohorts. Since the mutation data were absent and several genes of the LNM signature failed to be mapped in independent cohorts, we harnessed unsupervised clustering to determine if LNM signature could help distinguish LN metastasis status in the GEO cohort and whether it was associated with overall survival (OS) or progression-free survival (PFS) in the MKSCC cohort. Supervised hierarchical clustering based on mapped LNM signature was performed by using R function hclust via the Ward.D clustering method 1-Pearson's correlation distance, with k = 2 as the number of clusters. Expression profiling of mRNAs was transformed by log2(x+1) and median-centered before clustering.Internal validation was performed using the testing set of the TCGA cohort with 20 samples. The logistic regression formula formed in the training set was applied to all samples in the testing set, with total points for each sample calculated. Logistic regression in the testing cohort was then performed by using the total points as a factor. Finally, the receiver operating characteristic curve (ROC) with area under the curve (AUC) and calibration curve were derived based on the regression analysis by using R packages “2 or Fisher's exact test for categorical data when appropriate, a two-sample student's t-Test or Mann-Whitney U-test for continuous data when appropriate, a log-rank test Kaplan-Meier curve  test. Fisher's exact test of independence was used to statistically test the association between categorical clinical information and LN metastasis status. For all statistical analysis, a two-sided p < 0.05 was considered statistically significant. Decision curve analysis was conducted to determine the clinical usefulness of the LNM nomogram by quantifying the net benefits at different threshold probabilities by using R package “rmda” , LN category (p = 1.30–13), metastasis category (p = 0.035), pathological stage (p = 7.04–11), lymph node examined number , and tumor status (p = 0.0004) were significantly associated with LN metastasis status , BMI (trichotomized by WHO body mass index cut-off), pack-year of smoking history (dichotomized by median pack of 29), papillary type and histological grade were not different between LN+ and LN– samples. Race  <0.05, Supervised differential expression analysis using LN metastasis status as the group variable identified 180 differentially expressed genes (p = 0.008) (q < 0.05) for the present 193 samples with available mutation data  and PSIP1 were identified for constructing a predictive model.After filtering out non-silent mutation, tumors with LN+ exhibited a significant lower burden of mutation load as compared to LN– tumors (= 0.008) . MutSigCion data , all of ion data   and testing sets (p = 0.005) of the TCGA cohort  by multivariate Cox regression integrating LNM signature, gender, age and histological grade of the entire TCGA cohort.Of 180 differentially expressed genes, 48 features were selected on the basis of the training set of the TCGA cohort , including 22 up-regulated genes and 26 down-regulated genes, as they were features with non-zero coefficients in the LASSO logistic regression model ; Table 2A cohort  and appep = 0.048), and a tendency could be observed that LNM signature was associated with OS (p = 0.075) (p = 0.098)   and PFS = 0.098)  in the MMLL2 mutation as independent predictors (MLL2 mutation survived in the full model with p < 0.05 in the logistic regression (p = 0.973).Logistic regression analysis identified the pre-operative features of LNM signature and edictors . We alsogression . Thus, wgression . Predictp = 0.032), and no departure from perfect fit was identified (p = 0.485)  . The dec= 0.485) .MLL2. For the construction of the LNM signature, 180 candidate genes were reduced to 48 potential predictors by examining the predictor–outcome association by shrinking the regression coefficients with the LASSO algorithm. Compared to predictor selection based on strength of univariable association between predictor and outcome, this method further enables the combination of all selected features and creates a single signature, i.e., marker panels. Marker panels have been embraced in recent studies for multi-marker analysis (p = 0.048) and associated with patients' outcomes to some extent (p < 0.1). Thus, the non-invasive LNM signature allows for more convenient prediction of LN metastasis.Bladder cancer ranks fourth in men and eighth in women among the most common malignancies in terms of frequency . Howeveranalysis , 31, sucanalysis , and a 6analysis . Moreoveanalysis , 34. Anoanalysis . SimilarMLL2, which was differentially mutated between LN+ and LN– tumors , was also a significant variable in the predictive model (p = 0.012). MLL2, also known as KMT2D (Lysine Methyltransferase 2D), of which mutation is a driver in numerous different cancer types resulting in transcription stress and genome instability  , 39. ResIn short, this study presents a LNM nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently used to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder cancer.The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.XL and YW proposed the conception and design of this research. XL and LJ developed methodology. XL, BZ, and FY collected data and performed preprocessing. XL, WH, YZ, JW, XR, ZX, and XM analyzed and interpreted the data including statistical analysis, biostatistics, bioinformatics, and computational analysis. XL, JG, and YW were major contributors in writing the manuscript. All authors read and approved the final manuscript.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.


Alzheimer’s disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disability. Genetic and genome-wide association studies (GWAS) have identified about 33 risk factor genes for common, late-onset AD (LOAD), but these risk loci fail to account for the majority of affected cases and can neither provide clinically meaningful prediction of development of AD nor offer actionable mechanisms. This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD. Specifically, we generated whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data from multiple regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform. The certain cause of AD is known for 1-3% of early onset disease, which is autosomal dominant and 100% penetrant. Apolipoprotein E ε4 (APOE ε4) is a risk gene in 30% of common, late-onset AD (LOAD), but the mechanism linking APOE ε4 to increased AD risk remains elusive2. Conventional genetic and genome-wide association studies (GWAS) have revealed additional loci associated with LOAD, the most recent of which is TREM2  and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform.Alzheimer’s disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disabilityEM2 refs . However9. Briefly, for each sample, neuropathological assessments was performed according to the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) protocol10 and included assessment by hematoxylin and eosin, modified Bielschowski, modified thioflavin S, and anti-β amyloid (4G8), anti-tau (AD2) and anti-ubiquitin (Dakoa Corp.). A Braak AD-staging score for progression of neurofibrillary neuropathology12 was assigned to each case. Quantitative data regarding the mean of the density of neuritic plaques in the middle frontal gyrus, orbital frontal cortex, superior temporal gyrus, inferior parietal cortex and calcarine cortex were also collected as described9. Clinical dementia rating scale (CDR)13 was conducted for assessment of dementia and cognitive status. Ages for the samples were defined as Age at the time Of Death (AOD). AOD of the present population ranged 61–108, with a mean and standard deviation (s.d.) of 84.7±9.7. 364 human brains were accessed from the Mount Sinai/JJ Peters VA Medical Center Brain Bank (MSBB–Mount Sinai NIH Neurobiobank) cohort, which holds over 2,040 well-characterized brains. This cohort was assembled after applying stringent inclusion/exclusion criteria and represents the full spectrum of cognitive and neuropathological disease severity in the absence of discernable non-AD neuropathology. All neuropsychological, diagnostic and autopsy protocols were approved by the Mount Sinai and JJ Peters VA Medical Center Institutional Review Boards. Neuropathological assessments, cognitive, and medical and neurological status determinations were performed according to previously published procedures as described in detail14, were selected for molecular profiling primarily based on our recent study of microarray profiling of 19 brain regions in AD15. In that study, we systematically investigated the regional vulnerability to AD at the molecular level by evaluating region-specific expression changes associated with cognitive/neuropathological traits and then rank ordered the 19 brain regions accordingly. The parahippocampal gyrus (Brodmann area 36-BM36) and the inferior frontal gyrus (BM44) were the top two most vulnerable regions and thus were selected for this study. We also chose the superior temporal gyrus  and the frontal pole  as contrast for studying the selective regional vulnerability to AD.Four specific brain regions, Brodmann areasoC until DNA, RNA or protein isolation.These four brain regions were dissected while frozen from fresh frozen, never-thawed ~8 mm thick coronal tissue blocks using a dry ice cooled reciprocating saw. The dissected regions were then pulverized to a fine powder consistency in liquid nitrogen cooled mortar and pestle and distributed into 50 mg aliquots. All aliquots were barcoded and stored at −80 Total RNA were isolated from brain tissues using RNeasy Lipid Tissue Mini Kit from Qiagen (cat#74804) according to the manufacturer's protocol  with these modifications: 1) all brain tissues (pulverized) were kept on dry ice before adding QIAzol Lysis Reagent, 2) tissues were suspended in the lysis reagent by vortexing with tubes placed on ice, and 3) the tissues were homogenized using a Tissue Ruptor  at full speed for 20–30 s.RNA-Seq library preparation was performed using the TruSeq RNA Sample Preparation Kit v2 . Briefly, rRNA was depleted from total RNA using the Ribo-Zero rRNA Removal Kit (Human/Mouse/Rat)  to enrich for coding RNA and long non-coding RNA. cDNA was synthesized using random hexamers, end-repaired and ligated with appropriate adaptors for sequencing. The library then underwent size selection and purification using AMPure XP beads . The appropriate Illumina recommended 6-bp bar-code bases are introduced at one end of the adaptors during PCR amplification. The size and concentration of the RNAseq libraries was measured by Bioanalyzer  and Qubit fluorometry  before loading onto the sequencer. The Ribo-Zero libraries were sequenced on the Illumina HiSeq 2500 System with 100 nucleotide single end reads, according to the standard manufacturer’s protocol .16 was used to quantify the gene expression at the gene level based on Ensembl gene model GRCh37.70. The gene level read counts data were normalized as counts per million (CPM) using the trimmed mean of M-values normalization (TMM) method17 to adjust for sequencing library size difference. The RNA-seq alignment and expression quantification matrices are available (Data Citation 3).The raw sequence reads were aligned to human genome hg19 with the star aligner (v2.3.0e). Following read alignment, featureCountshttps://gatkforums.broadinstitute.org/gatk/discussion/3891/calling-variants-in-rnaseq). Starting from star aligned bam files, Picard tools were used for adding read group information, sorting, marking duplicates and indexing. Next, GATK tool SplitNCigarReads was applied to split reads into exon segments and trim any sequences overhanging into the intronic regions. Then GATK base-quality score recalibration (BQSR) was called to detect systematic errors caused by the sequencer and subsequently adjust the base quality score. After recalibration, single nucleotide polymorphisms (SNPs) and insert/deletions (INDELs) were called jointly with the GATK HaplotypeCaller. Lastly, a series of hard filters were applied to remove low quality variant sites using recommended parameters, including filtering clusters of at least 3 SNPs within a window of 35, Fisher Strand values (FS)>30.0, or Qual By Depth values (QD)<2.0.We also called variants in RNA-seq data by following the GATK Best Practices for variant calling on RNA-seq v3.0  or superior temporal gyrus (BM22). Specimens were homogenized in 300 μl of elution buffer, and DNA was isolated using a Promega Maxwell 16 semi-automated system using the Promega Maxwell 16 Tissue DNA Purification Kit according to the manufacturer’s instructions. DNA quality and yield were assessed using an Agilent 4200 Tape Station.Genomic DNA samples were sheared into small DNA fragments and libraries were prepared with Illumina compatible adapters and indices. Biotinylated cRNA baits were incubated with the library for 16 h and then targeted regions were selected using magnetic streptavidin beads according to Agilent SureSelect human all exon V5 kit sample preparation protocol. Targeted regions were amplified, producing an exome enriched library. The sequence ready libraries were loaded onto Illumina HiSeq 2500 System with 125 bp paired-end sequencing on V4 flow cell. Three samples were pooled per sequencing lane, aiming for 80X mean coverage per sample.The raw sequence reads were aligned to targeted exome regions on human genome hg19 with the BWA mem aligner. Then the sequence variants were called using the DNAseq Variant Analysis workflow of GATK Best Practices version 3, including insertion/deletion (INDEL) realignment, de-duplication, and base-quality score recalibration (BQSR). SNPs and INDELs were called jointly with the GATK HaplotypeCaller. GATK Variant Quality Score Recalibration (VQSR) was used to estimate the probability that a variant is a true genetic variant rather than a sequencing or data processing artifact. The SNP VQSR model was trained using HapMap3.3, 1KG Omni 2.5, 1KG phase1 high confidence, and dbsnp138 SNP sites, while Mills et. al. 1KG gold standard and dbsnp 138 sites were used for training INDELs model. After VQSR recalibration, a 99% sensitivity threshold was applied to filter variants by the GATK ApplyRecalibration tool. The WES alignment files and the resulting genetic variants are included (Data Citation 4).Whole genome sequencing (WGS) libraries were prepared at the New York Genome Center (NYGC) using the KAPA Hyper Library Preparation Kit (PCR-free) in accordance with the manufacturer’s instructions. Briefly, 650 ng of DNA was sheared using a Covaris LE220 sonicator (adaptive focused acoustics). DNA fragments underwent bead-based size selection and were subsequently end-repaired, adenylated, and ligated to Illumina sequencing adapters. Final libraries were evaluated using fluorescent-based assays including qPCR with the Universal KAPA Library Quantification Kit and Fragment Analyzer  or BioAnalyzer (Agilent 2100). Libraries were sequenced on an Illumina HiSeq X sequencer (v2.5 chemistry) using 2×150bp cycles.18, bcftools and in-house software. Other functional annotations include variant frequencies in different populations from 1000 Genomes project19, Exome Aggregation Consortium–ExAC, dbSNP 13820; cross-species conservation scores from PhyloP21, Genomic Evolutionary Rate Profiling (GERP)22, PhastCons23; functional prediction scores from Polyphen224 and SIFT25; variant disease associations from OMIM, Clinvar; regulatory annotations from ENCODE26, Regulome27, KEGG pathway annotations28; and gene ontology annotations for biological process, cellular component, molecular function29. Variants and annotations were exported to tabular formats for the ease of downstream analysis. Additional filtration based on functional annotation was applied to extract variants with predicted effects on protein coding. The WGS alignment files together with the genetic variants calling and annotation are included (Data Citation 5).Paired-end 150 bp reads were aligned to the GRCh37 human reference using the Burrows-Wheeler Aligner (BWA-MEM v0.7.8) and processed using the GATK best-practices workflow that includes marking of duplicate reads using Picard tools v1.83, local realignment around indels, and base quality score recalibration (BQSR) via Genome Analysis Toolkit (GATK v3.4.0). Variant discovery was run using a two-step process. HaplotypeCaller was run on each sample individually in gVCF mode (GATKv3.4.0) producing an intermediate file format called gVCF . gVCFs were combined by batches into merged gVCFs and run through a joint genotyping step (GATK v3.2.2) to produce a multi-sample VCF. Variant filtration was then performed using Variant Quality Score Recalibration (VQSR). VQSR identifies annotation profiles of variants that are likely to be real and assigns a score (VQSOD) to each variant. Variant effects annotation was then performed using SnpEff30. Protein homogenates (150 μ g) were diluted with 50 mM NH4HCO3 to a final concentration of less than 2M urea and then treated with 1 mM dithiothreitol (DTT) at 25 °C for 30 min, followed by 5 mM iodoacetimide (IAA) at 25 °C for 30 min in the dark. Protein was digested with 1:100 (w/w) lysyl endopeptidase (Wako) at 25 °C for 2 h and further digested overnight with 1:50 (w/w) trypsin (Promega) at 25 °C. Resulting peptides were desalted with a Sep-Pak C18 column (Waters) and dried under vacuum.Tissue samples dissected from the frontal pole (Brodmann area 10–BM10) were processed as described in ref. Mass spectrometry analysis was performed and processed as described in ref. 31, was used to build and search a concatenated target-decoy Uniprot human reference protein database , plus 245 contaminant proteins from the common repository of adventitious proteins (cRAP) built into MaxQuant. Methionine oxidation (+15.9949 Da), asparagine and glutamine deamidation (+0.9840 Da), and protein N-terminal acetylation (+42.0106 Da) were variable modifications ; cysteine was assigned a fixed carbamidomethyl modification (+57.0215 Da). Only fully tryptic peptides were considered with up to 2 miscleavages in the database search. A precursor mass tolerance of ±20 ppm was applied prior to mass accuracy calibration and ±4.5 ppm after internal MaxQuant calibration. Other search settings included a maximum peptide mass of 6,000 Da, a minimum peptide length of 6 residues, 0.05 Da tolerance for high resolution MS/MS scans. Co-fragmented peptide search was enabled to deconvolute multiplex spectra. The false discovery rate (FDR) for peptide spectral matches, proteins, and site decoy fraction were all set to 1 percent. Label free quantification of proteins was performed by MaxLFQ, which considered razor plus unique peptides for protein level measurements. Surrogate amyloid beta (Aβ) levels by precursor intensity trace area of tryptic fragments from the region of APP containing the Aβ sequence were quantified as described previously30. The MS raw data, MaxQuant parameters, search results and quantification are included (Data Citation 6).RAW data for the samples were analyzed using MaxQuant v1.5.2.8 with Thermo Foundation 2.0 for RAW file reading capability. The search engine Andromeda, integrated into MaxQuantAll data described herein are available for use by the research community and have been deposited in the AMP-AD Knowledge Portal in study specific folders (Data Citation 1). These include sample metadata (Data Citation 2), RNA-seq alignment files in bam format and the gene level expression matrix from 4 brain regions (Data Citation 3), WES alignment files in bam format and the variants calling in plink and VCF format (Data Citation 4), WGS based variants calling in VCF format and variants annotations (Data Citation 5), and mass spectrometry raw files and protein quantification analysis results (Data Citation 6).33. Combining known and novel loci, the overall Ti/Tv ratio was 2.68. −11). Samples of Latino ancestry presented more variants than samples of European ancestry.We utilized the GATK VariantEval tool to calculate various quality control metrics of the identified variants, including overall and per sample number of variants, transition/transversion (Ti/Tv) ratio, alternate allele heterozygous/homozygous (Het/Hom) ratio and insertion/deletion (Ins/Del) ratio. Overall, there were 399,135 SNPs and 22,967 INDELs identified, including 70,083 (17.6%) and 10,641 (46.3%) novel SNPs and INDELs, respectively. On average, 39,744 (364 novel) SNPs and 3,597 (228 novel) INDELs were detected per individual. The Ti/Tv ratio is a critical metric for assessing the quality of variants calling, with high quality exome sequencing variation dataset expecting to have Ti/Tv ratios between 2.8 and 3.0 refs ,33. OverThe mean mapped depth of all 349 samples was 42X (32X-62X). The paired-end short reads mapped to around 91.91% of human reference genome (GRCh37). There was no significant difference in mapped depth and breadth of coverage among the AD, MCI and control groups . There wet al.34, we implement a mixed effects model to remove batch effects and impute missing values while taking into consideration the abundance-dependent missing mechanism in the data.6,107 different proteins were identified from the experiment with an average missing rate at 41.8%. By selecting proteins with missing rate smaller than 15%, we obtain protein abundance profiles for 2,962 proteins in total. kth sample in the ith experiment  we have:yki is the observed abundance, μ is the average abundance, Iki is the index of reference sample taking value 1 if the kth sample in ith experiment is a reference sample and 0 otherwise, α is the fixed effect of reference samples, bi is random variable with standard deviation d indicating the batch effect of ith experiment and eki is the error term representing the individual variation of the protein abundance with standard deviation σ0 of reference sample or σ of regular sample. For different k and i, bis and ekis are all independent. μ, α, d, σ0 and σ are parameters to be estimated. We also assume the abundance dependent missing data mechanism as follow:Mki is the missing status, taking value 1 if Yki if missing, and 0 otherwise.We assume that, for each protein, its abundance measurements follow a mixed effect model with a random effect term representing the batch effect. For the With the above model, we can estimate the random effect and impute the missing values by maximizing the likelihood function of the observed data. Specifically, we implement an expectation conditional maximization (ECM) algorithm which iterates between (1) estimating the expectation of missing data and random effect conditional on the observed data and parameter estimated from last step, and (2) maximizing the conditional likelihood given the updated conditional expectation of missing data and random effect to obtain the parameter estimation, until the pre-defined convergence criterion is met.We performed the above procedure of Batch effect Correction and Missing value Imputation (BCMI) on each protein feature separately. Notice that 18 samples have two replicates measured in both the 7th and 8th batches. We examine correlations between each replicate pair before and after the above procedure. As illustrated in In the current study, 4 different types of -omics data, including WGS, WES, RNA-seq (from 4 brain regions), and proteomics, have been generated from the same set of postmortem brain donors and brain regions, resulting in more than 1,800 samples of different data types. Quality control (QC) and assessment relating to individual data platform/technique is in place, e.g., phred-score based filtering of low quality sequencing reads and missing data imputation for proteomics. However, during the production and management of large-scale data, sample errors, including incorrect labeling, sample swapping and contamination, are inevitable but harder to detect by the platform specific QC procedure on each type of data. Because sample errors can cause both false-positive and false-negative results, systems approach for QC across multiple -omics datasets are essential for reliable and accurate identification of true biological signals from the integrative data analysis. Given the scale and complexity of such datasets, automatic QC and adjustment methods need to be applied.35. Variants with missing rate >50%, minor allele frequency<0.01 or Hardy-Weinberg equilibrium test P value<0.001 were removed from further analysis. Samples with excessively high or low heterozygosity rate (more than 3 standard deviations away from the mean) in each data type were marked as they suggested either mixture (possibly due to contamination) or inbreeding in these samples. Plink can impute sex from X chromosome inbreeding coefficients and flag individuals for whom the reported sex in the meta table does not match the inferred sex. It is noted that some of the statistics, including heterozygosity rate, sex imputation and PCA, require a relatively large number of independent SNPs and hence the inference is more accurate from WGS than from WES or RNA-seq. On the other hand, gender inference in RNA-seq can be facilitated by using gender-specific marker genes, such as XIST , DDX3Y  and RPS4Y1 . We found 6 sex-mislabeled samples in the MSBB WGS data  while the sequencing samples from different brains are expected to present low genetic similarity, any kinship failure or mismatch suggested sample errors due to either incorrect ID labeling, sample swapping or contamination. We flagged the suspicious sample pairs which should match genetically but did not, and spurious sample pairs which should not match but present very high genetic similarity. Using an iterative procedure coupled with a majority voting scheme, we sequentially tested whether every suspicious or spurious sample was mislabeled and could be unambiguously identified with the correct brain source. Any sample which was inconclusive for its brain source was deemed problematic and discarded from further analysis. In each iteration, one sample with the most suspicious or spurious flags was selected for correction, then the suspicious and spurious flags for all samples was updated because one mislabeling may lead to multiple kinship errors and hence one correction may help resolve multiple errors.Next we estimated pairwise sample kinship using KING36, an estimated kinship coefficient >0.354 suggests a duplicate or mono-zygote twin while a kinship coefficient less than 0.1 indicates a 2nd-degree or more distant relationship. Thus, it was likely that sample hB_RNA_10452 was mis-labeled for its brain id and its true brain source should be re-mapped to 53661. Similarly, hB_RNA_10392 from the brain 10730 should be re-mapped to brain id 44475. We further identified that one RNA-seq sample, hB_RNA_10442, from the brain 53661 was also mislabeled. Resolving the true brain source for the samples in these 3 brains resulted in multiple corrections involving 3 more brains  as shown in 37 to match proteomics data with the QCed sequencing data. In this analysis, we considered the sample labels in QCed sequencing data as ground truth. We first computed cis- protein QTLs (pQTLs) based on protein expression (after missing data imputation) and WGS genotype data using MatrixEQTL and then inferred genotypes at the cis-SNPs for each proteomics sample separately. With the inferred genotypes, genetic identity similarity scores between proteomics and WGS data were computed. Second, we ran the same pQTLs based genotype imputation analysis using WES genotype data as some proteomics samples had WES genotype but not WGS genotype. In the present analysis, we considered a proteomics sample Pi as properly aligned if (a) the similarity score (Sii) with the supposed self-align WGS/WES sample Gi which had the same brain id was ranked in top 3 among all WGS/WES genotype profiles, or (b) Sii was ranked in top 3 among all proteomics samples with respect to the genotype profile Gi. This is slightly different from the original MODMatcher criterion which requires a reciprocal self-alignment. On the contrary, a proteomics profile would be considered mis-aligned with other unmatched genotype sample by reciprocal matching, in which we explored whether a mis-aligned genotype profile Gj has the highest similarity with an unmatched proteomics profile Pi among all proteomics samples, and the unmatched proteomics profile Pi has the highest similarity with Gj among all genotype profiles. If mislabels in proteomics data were identified, the quality of sample alignment was re-assessed according to the updated sample annotation and this process was iterated until convergence. In the present analysis, we obtained a 97.3% self-alignment rate in proteomics samples by using WGS genotype profiles and similarly a 97.1% self-alignment rate using WES genotype profiles. Collectively, we achieved a rate of 98.6% self-alignment combining the results from two genotype profiles , which is not surprising as the correlation between mRNA and protein expression is poor .We used MODMatcherprofiles . We did The relatively mild correlation between the RNA-seq and proteomics data may be caused in part by the sample processing where the tissues for the transcriptomic and proteomic profilings were dissected from different locations of the same region at different times. Other factors, such as different degradation rates of RNAs and proteins in postmortem brains, may also have some impact. Nevertheless, the mild correlations between RNAs and their corresponding proteins are not unique to this cohort as we observed a similar level of mild correlations between RNAs and proteins in an independent cohort of multi-Omic data from postmortem human brains, the Mayo cohort (doi:10.7303/syn5550404), which utilized the same LC-MS/MS technique for proteomic profiling as the Mount Sinai cohort. After filtering those with more than 15% missing values in the proteomic data followed by normalization and covariates adjustment, there were 2,914 proteins remained, out of which 2,880 had matched mRNA expression data from the same brain regions in the Mayo cohort. The mean and median of the Spearman’s correlations between matched mRNA-protein were 0.116 and 0.104, respectively. Therefore, extreme caution must be taken if one wants to predict protein abundance from RNA expression, or vice versa, in postmortem brain tissues.15.The present MSBB AD cohort consists of samples with varying degree of disease severity with respect to 4 different clinical and neuropathological traits in relation to AD. Stratifying samples into different severity groups allows for the differential expression of mRNAs and proteins at early or advanced stage of the disease. Investigators can prioritize genes which are altered in both mRNA and protein levels. Moreover, RNA-seq based mRNA expression was profiled in 4 different brain regions which can help identify region-specific gene signatures implicated in selective vulnerability to AD38. Massive genome sequencing from the present WGS and WES data offers an unprecedented opportunity to characterize both low frequency and rare variants that may have a profound impact on disease risk but otherwise missed by traditional GWAS. However, we must notice that the sample size is small and hence the power is very limited.Genome-wide association studies (GWAS) have successfully identified more than 30 common genetic variants associate with the disease risk of late onset AD. However, the majority (>75%) of the phenotypic variance remain unexplained by the known markersIn addition, a joint analysis of WGS/WES genotype, RNA-seq and proteomics profiles can identify variants playing transcriptional or translational regulatory roles by the so-called expression and protein quantitative trait loci (eQTLs and pQTLs) analysis. Together with the genotype-disease association from this cohort and other public datasets, this could help identify variants conferring risk to AD via transcriptional or translational regulatory mechanisms.39. The network analysis approaches  combined with the deep and comprehensive multiscale -omics data can not only help identify gene/protein modules that were enhanced or disrupted in AD but also prioritize network hub regulators driving the key biological process implicated in AD.AD is a complex human disease with a multifactorial nature involving a system of genetic and environmental interactions. Thus, there is a growing interest in applying an integrative network biology approach to model the interconnected relationships among a large network of gene/protein expression traits, genetic variants and clinical/neuropathological outcomesHow to cite this article: Wang, M. et al. The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease. Sci. Data 5:180185 doi: 10.1038/sdata.2018.185 (2018).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


However, due to the intrinsic properties of RNA molecules and the intricate procedures of this technique, m6A-seq data often suffer from various flaws. A convenient and comprehensive tool is needed to assess the quality of m6A-seq data to ensure that they are suitable for subsequent analysis.Methylated RNA immunoprecipitation sequencing  contains supplementary material, which is available to authorized users. This pa6A-seq data is assessed by the trumpet package from mainly 3 perspectives, including (1) statistics of sequencing reads distribution with respect to different genomic regions; (2) the strength of the immunoprecipitation signal evaluated by the exome signal extraction scaling (ESES) and other statistical approaches; (3) comparison between different biological replicates to identify possible outliers. These assessment components are detailed in the following with a sample dataset that profiles midbrain gene under wild type and FTO knockdown conditions [https://github.com/skyhorsetomoon/Trumpet.The quality of mnditions , 11. TheThis module is aimed to gain overall insights into samples via statistics of read counts, which is probably the most fundamental way to check the quality of samples. Relatively low number of reads or distinct proportion of reads mapped to a specific genomic region may be naturally associated to poor data quality due to unbalanced sequencing in sample multiplexing, DNA contamination or other bias during the experimental procedures. In this section, we mainly evaluate read alignment and their distribution, with which we inspect the sequencing depth of the input files, the heterogeneity of read coverage, the read alignment mapped to different genomic regions, such as exon, intron, 5’UTR, CDS and 3’UTR.6A-seq samples, which profile the m6A epitranscriptome [The Table criptome  in mousecriptome . It is ocriptome .Table 1N4 reads compared with the other samples, suggesting a higher degree of heterogeneity in read coverage, which may indicate potential PCR artifacts during sample preparation or sequencing. This has been confirmed by the FASTQC [6A-seq experiment.In order to show the heterogeneity of read coverage in the entire transcriptome due mainly to different levels of gene expression, PCR artifacts and randomness, we used bin-based approach to check the percentage of regions covered different number of reads. To make the result comparable and not affected by different sequencing depth, the same number of reads are randomly selected from each sample using the built-in option. In Table e FASTQC  software6A methylation is enriched near the stop codon. In this module, the distribution of reads in different genomic regions  is visualized. Since there exist highly abundant genes, whose m6A enrichment signal may dominate the analysis if raw reads are directly used in the analysis, the same weight is assigned to all the detected genes regardless of their read coverage. Specifically, genes that are not expressed or have less than 10 reads are first excluded; then, for the remaining genes, the read coverages at different regions of the same transcript are counted and then standardized (divided by the read coverage’s mean counts). The quantiles  of the standardized read coverage at different genomic regions is then plotted as shown in Fig. It is known that, RNA m6A-seq data quality is the efficiency of immunoprecipitation, which can be reflected by the enrichment of immunoprecipitation signal. To evaluate the enrichment of m6A signal in the IP sample, trumpet package uses a metric called exome signal extraction scaling (ESES), which is a modified form of the signal extraction scaling (SES) approach previously developed for assessing the signal of ChIP-seq data [n bins and count the number of reads mapped to each bin. Let yt, g, i be the read count of the i-th bin on the g-th gene in the IP sample, andyc, g, i represent the read count of the i-th bin on the g-th gene in the Input sample. The standardized the read count that eliminates the difference in expression of genes can be calculated asg-th gene in the IP and Input sample, respectively, which are proportional to the abundance of that gene and the sequencing depth  of sample, and i-th element of i-th least number of the normalized reads mapped in 6A signals are likely to appear in the end of the list. If we assume that there are a total of N bins on the transcriptome surveyed in this analysis, then we should have (i) ∈ {1, 2, ⋯, N}. Meanwhile, let A major aspect of the mseq data . The ESEN bins on the transcriptome surveyed in this analysis, it is then possible to calculate a fraction of cumulative immunoprecipitation signal in the IP sample as pj = yt(j)/yt(N) and also the cumulative background information in the input sample asqj = yc(j)/yc(N). Because the bins are arranged in an increasing order of normalized read count, the bins that are enriched with m6A signal are likely to appear in the very end of the list. For this reason, as j increases from 1 to N, pj should first increases slower than qj before reaching bins absent of m6A and then increases faster than qj afterwards. Moreover, ∣qj − pj∣, which computes the difference in the cumulative percentage between IP and Input samples, will also first increase from 0 with as j increases but decrease rapidly once the bins with sufficiently large read count or enriched signals are incorporated  and also the scale factor (maxj ∣ qj − pj∣) to show the degree of difference between the IP and Input samples.If we consider a total of see Fig. . ConsistThe reported ESES metrics on the sample dataset is shown in Table 6A signals at different levels. The C-test compares two Poisson means and is used in the exomePeak package to predict RNA methylation sites [6A signal at different fold enrichment thresholds are counted and plotted. It is then possible to compare the difference between different samples. As shown in Fig. Besides the ESES metrics, the trumpet package also include a C-test to detect the regions enriched with mon sites . This isxi, j represent the number of reads of the i-th bin located on the exome in the j-th sample. The standardized read count after eliminating difference in sequencing depth can be calculated asHierarchical Clustering (HC) is then applied to all the samples for the identification of possible outliers and for assessing the relative similarity between samples and groups (if applicable). To eliminate the impact of different sequencing depth and transcriptional regulation, the hierarchical clustering is performed as follows. Let yj is the total number of reads in the j-th sample, sj is the size factor of the j-th sample, and i-th bin in the j-th sample. We can then perform HC and PCA analysis to study the relationship between different samples, as shown in Fig. yj, g, i be the read count of the i-th bin on the g-th gene in the j -th IP sample, and SDj, grepresent the average number of reads mapped to the bins on theg-th gene in the j-th IP sample and its standard deviation. We then use a local regression to fit a curve between SDj, g for ∀g. As shown in Fig. In practice, the aligned reads are not evenly distributed on the same gene. In the IP sample, heterogeneity of read coverage may be generated from the enrichment signal around the true methylation sites, which may be complicated due to isoform ambiguity, or some possible bias and artifacts due to PCR process. Compared with the IP sample, the coverage is more flat in the input sample. The gene-specific heterogeneity of read coverage is assessed in the trumpet package with the mean and standard deviation (SD) of read count in each gene of each sample. Specifically, let i-th bin in the j-th sample. Also assume that we have multiple biological replicates  obtained from the same experimental condition. Then, the mean and standard deviation of the read counts of the same bin across different samples can be calculated as:This metric is used to assess the degree of difference between multiple biological replicates. In order to eliminate the difference in sequencing depth between theses biological replicates, we first normalize the read count of the each bin in each sample. The normalization method is the same as the hierarchical clustering analysis detailed in Section “It is possible to fit the variables with a local regression curves to show the consistency between different samples, or compare the reproducibility of the samples obtained from different conditions.a) There exists increased variability in the RNA methylation level due to gene knock down operation; (b) Different immunoprecipitation efficiency is observed on datasets using antibodies from different companies, (c) The RNA m6A methylation level of U2OS cell line is relatively high compared with other cell line, (d) m6A-seq is enriched near stop codon while m1A is enriched on 5’UTR.We included in the following 4 case studies to show that:  [6A-seq samples to 10 million mapped reads was first performed before further analysis. As shown in Fig. 6A signal. The m6A signal can still be readily observed on relatively lowly expressed genes (corresponding to 25 and 50% quantile green and red curves) in the two SYSY samples, but not in the NEB sample, suggesting NEB sample suffers from potential artifacts. One possible explanation is that there was insufficient amount of RNA in the NEB sample. Because the input material did not contain a large variety of RNAs, the sequencing data thus failed to capture the m6A signal in lowly expressed genes. Please note that all the samples here were from the same study, were generated by the same protocol, had the same number of reads after down-sampling, and profiled the same cell type (HEK293T).In this example, we considered reads distributions of the mand NEB)  , HEK293T, HeLa, neural progenitor cells (NPC), fibroblasts and U2OS. Under the default setting of the trumpet R package, the U2OS cell line is reported to have the largest percentage of regions enriched with m6A signal, suggesting the m6A methylation level is relatively high in this cell line, as shown in Fig. In this example, we compared the m studies , 15–18 p6A-seq data, the trumpet package can also be applied to other affinity-based fragmented RNA immunoprecipitation sequencing data, such as m1A-seq [1A-seq dataset profiling the m1A epitranscriptome in HEK293T cell line [6A-seq data obtained from the same cell line [Besides m m1A-seq  and PSU- m1A-seq . As an eell line , and comell line .1A-seq and m6A-seq, the less abundant m1A modification is enriched in 7–8% of regions, while the more abundant m6A modification is enriched in 14.5% of region. The scale factor of the m1A-seq samples are a lot larger compared with that of m6A-seq.As shown in Table 1A-seq data is enriched in the 5’UTR, which is consistent with the known distribution of m1A modification. However, similar to case study 2, m1A signal was not observed for very lowly expressed genes . It is important to use similar gene annotation database for comparison purpose. By default, the UCSC gene annotation can be downloaded automatically from the internet from the trumpet package. Since the majority m6A-seq samples are constructed from polyA selected RNA libraries, only the exonic signals mapped to mRNA will be used, and the intronic signals are discarded from the analysis. In case it is desirable to analyze intronic signals or the RNA methylation on pre-mRNA, the library should be constructed from ribo-minus RNA library, and a gene annotation database including pre-mRNA should be constructed and provided to trumpet. From a computational perspective, pre-mRNA is no difference from an isoform transcript; however, the analysis performed will be affected by added transcriptome complexity and additional intronic regions with relatively lower signal-to-noise ratio. Another concern is from the rRNA. In theory, rRNA should not exist in the m6A-seq library when the two popular protocols, i.e., polyA selected or ribo-minus library, are used, and as a result, the trumpet package did not specifically test the impact of rRNA annotations on the m6A-seq data quality. As shown in Additional file Table S1-S3, the amount of rRNA in m6A-seq data is likely to be very small; however, in practice, it may be still possible to see m6A-seq data targeting rRNAs, because the existence of RNA modifications on rRNA has been well established and their functions are often of interests [Additionally, the gene annotation required by the trumpet package may still affect the metrics. Larger gene annotation databases may report more reads aligned to the exonic regions compared with smaller gene annotation database, and the ESES enrichment metrics can also be slightly affected Additional file 2:Table S1-S4) for trumept. (DOCX 21 kb)Supplementary Material (including


The ever-increasing high-volume and high-dimensional genomics data on the one hand challenge traditional data analysis approaches, and on the other hand provide ample opportunities for developing novel analytic strategies. In recent years, deep learning has been driving the next wave of artificial intelligence and machine learning. Now, Yi Xing’s lab reported DARTS cis-elements around the splicing site in both exonic and intronic regions, and trans-acting factors that bind to specific cis-elements. It has been shown that AS plays critical roles in a variety of physio-pathological processes.In higher eukaryotes, the vast majority of protein-coding genes are transcribed into precursor mRNA (pre-mRNA) containing exons and introns that need to be removed by the splicing machinery to generate mature mRNA. Often the transcript can be spliced in different ways, leading to a different combination of exons. AS contributes to the variety of the cellular proteome as well as to the fine tuning of gene expression levels at the post-transcriptional level. The regulation of AS is mediated by the interaction between vs. disease, as well as control vs. treatment. For the latter, many computational methods have been developed. The common strategy underlying these methods is to use the number of RNA-seq reads exclusively supporting either isoform to estimate an abundance ratio between the two spliced isoforms, i.e., inclusion and exclusion isoform, and then perform a statistical test to determine whether the splicing pattern between the two samples is significantly different or not. Although implemented with different statistical frameworks, all these methods would encounter high uncertainty for the splicing events sampled with low sequencing coverage. Therefore, the sensitivity is rather limited in detecting differential AS for the lowly-expressed genes. Moreover, many currently available RNA-seq datasets originally designed only for differential analysis of gene expression are often of low sequencing depth, which is insufficient for AS analysis even for moderately-expressed genes. Harnessing these valuable resources for AS studies warrants novel analytic strategies.In the last decade, the amount of RNA-seq data has soared, which provides valuable resources for extensive studies of transcriptional and post-transcriptional regulation. In addition to providing the information on RNA abundance, RNA-seq data could also be used to infer the AS pattern, and more often to identify the differential AS between different samples, such as those from different developmental stages, normal e.g., image recognition and language processing) with great success. Unlike the traditional machine learning algorithms, deep learning trains both the feature extractor and the classifier simultaneously. The high model complexity caused by the extraordinarily large number of parameters makes deep learning models data-hungry. Such high model flexibility, on the other hand, together with the powerful optimization algorithms, enables deep learning to achieve the state-of-the-art performance on a wide spectrum of applications where large datasets are available, such as computer vision, natural language processing, and genomics.Deep learning has recently reemerged in various fields  to estimate the prior probability and a likelihood estimator (DARTS BHT) based on the prior probability as well as RNA-seq read counts. Before training DARTS DNN, large-scale RNA-seq data are analyzed first by DARTS BHT with uninformative prior to generate a high-confidence labeled training dataset that contains both differential splicing and unchanged splicing between conditions. Then the labeled training dataset is used for training DARTS DNN. In contrast to the aforementioned deep learning-based AS methods, this deep learning module incorporates not only the cis-elements from the primary genomic sequences but also the trans-elements represented by the expression level of 1498 splicing-relevant RBPs. Zhang et al. first evaluated the performance of DARTS on the test data corresponding to leave-out RBPs and showed that DARTS outperformed the baseline methods. They then applied DARTS on two cell lines to infer cell-type-specific splicing events, in which they found that the performance of DARTS BHT with an informative prior probability is better than that without the prior, demonstrating that incorporating DNN prediction as an informative prior improves the performance of DARTS BHT in detecting differential splicing. To further demonstrate the power of DARTS DNN on other RNA-seq datasets, they trained three DARTS DNN models using ENCODE data only, Roadmap data only, and their combination, respectively. They found that the model trained on ENCODE data has high predictive power for the ENCODE leave-out datasets, but modest predictive power for Roadmap leave-out datasets, and vice versa, while the model trained on the combination of both datasets has the best performance. Furthermore, they extended DARTS DNN to other types of AS events, i.e., alternative 5′ or 3′ splice sites and retained introns, and they also achieved a high prediction accuracy. Finally, they applied DARTS to investigate the change of AS pattern during the epithelial-mesenchymal transition (EMT) using the previously published RNA-seq dataset The new tool, DARTS, mainly consists of two components, a prior based on learned knowledge about each AS event in a specific sample, while the latter further integrates the information from RNA-seq data. (2) The deep learning model within DARTS framework for the first time takes both cis-elements and trans-factors into consideration, which improves differential AS detection between conditions.The major innovation of DARTS lies in two aspects. (1) DARTS combines a deep learning model with Bayes hierarchical framework: the former provides the latter cis–trans interactions, such association requires a prohibitively large number of input combinations. Second, DARTS is trained on invariant genomic sequences from different samples, and thus could not capture the splicing landscape of sequence variants. Third, the performance of DARTS may be further improved by incorporating increased lengths of flanking regions or more cis-features. However, it requires more data and sophisticated feature engineering to obtain a better model.There are yet some directions for further development of DARTS. First, although DARTS can theoretically capture the cis–trans interaction. Therefore, APA regulation could be treated as a similar problem and accordingly investigated with a similar strategy. Xia et al. recently developed a robust, poly(A) signal (PAS) motif-agnostic, and transferable deep learning model to differentiate true PASs from false ones Other than AS, alternative polyadenylation (APA) is also a key, but less-well studied step in RNA processing. And conceptually, similar to AS, the regulation of APA is also mediated by The authors have declared no competing interests.


We develop RADAR, a comprehensive analytical tool for detecting differentially methylated loci in MeRIP-seq data. RADAR enables accurate identification of altered methylation sites by accommodating variability of pre-immunoprecipitation expression level and post-immunoprecipitation count using different strategies. In addition, it is compatible with complex study design when covariates need to be incorporated in the analysis. Through simulation and real dataset analyses, we show that RADAR leads to more accurate and reproducible differential methylation analysis results than alternatives, which is available at  N6-adenosine methylation (m6A), the most abundant modification on mRNA, has drawn extensive attention due to its important functions in various biological systems [The RNA epigenetics gold rush in the past few years has brought reversible RNA modifications into the spotlight as an important mechanism of gene regulation. Particularly,  systems –3. Methy systems –6 is a kEarly studies performing qualitative analysis compared peaks called in one experimental group versus peaks in another group and identified peaks unique to each experimental group as differentially methylated peaks. However, many differential peaks identified by this method are caused by boundary cases at the peak-detection threshold rather than true presence/absence of peaks as noted in a recent study . To enabWhile existing methods have yielded promising results, they also have important drawbacks: (1) Current methods –11 desigRNA methylAtion Differential Analysis in R (RADAR) for MeRIP-seq data. RADAR accounts for variation in pre-IP RNA and in post-IP read counts using different strategies. Specifically, RADAR uses gene-level read counts instead of peak-level read counts in the INPUT library as a robust measurement of the initial pre-IP RNA expression level. In addition, RADAR uses a flexible Poisson random effect model to accommodate over-dispersion in the post-IP read counts due to variability of biological replicates and noise introduced in the immunoprecipitation process. This generalized linear model framework enables incorporation of covariates in complex study designs.To combat these challenges and allow for accurate identification of differentially methylated loci, we present a novel approach to perform 6A-meRIP-seq (aka m6A-seq) datasets generated by us and others, including an ovarian cancer dataset (GSE 119168) consisting of 7 normal fallopian tube tissue from healthy individuals and 6 metastatic omental tumors, a type 2 diabetes  dataset consisting of human islets from 8 type II diabetes patients and 7 healthy control patients with samples being processed in three batches due to different sample acquisition times, a mouse liver (GSE 119490) dataset consisting of mouse liver from 4 wild type mice and 4 METTL14 knockout mice, and a mouse brain (GSE 113781) dataset consisting of 7 mouse cortex samples of stress-exposed mice and 7 from control mice. We showed that our approach can accommodate distinct study designs and led to more sensitive and reproducible DM locus identification than possible alternatives.We benchmarked the performance of RADAR with alternative methods on simulated data by different data generating models. We showed RADAR achieved higher sensitivity and specificity compared to existing alternative methods. We also demonstrated the performance of RADAR on real MeRIP-seq data by applying it to four high-quality mUsing BAM files as the input, RADAR first divides transcripts (concatenated exons) into 50-bp consecutive bins and quantifies pre-IP and post-IP read counts for each bin Fig. a. UnlikeAfter proper normalization across all samples, RADAR then calculates the methylation level for each bin conditioned on its pre-IP RNA expression level for each sample. In contrast to previous methods –11 that 6A-seq datasets that the mean-variance relationship of post-IP counts across genes significantly differs from that of regular RNA-seq counts . The former does not always follow a similar quadratic curvature and can exhibit very different patterns of variability  model adopted from QNB , 10 withFor DM loci with an effect size of 0.5, RADAR achieved 29.1% sensitivity and 12.0% FDR at an FDR cutoff of 10%. At the same cutoff, exomePeak and Fisher’s test achieved 72.8% sensitivity/52.5% FDR and 72.2% sensitivity/50.5% FDR, respectively. MeTDiff achieved 10.5% sensitivity and 16.2% FDR. QNB, on the contrary, did not own any power for the small effect size. When the effect size increased, RADAR achieved much higher sensitivity, 77.8% for an effect size of 0.75 and 95.7% for an effect size of 1, while FDR were well calibrated at 10.4% and 10.1%, respectively. exomePeak and Fisher’s test both achieved 89% and 96% sensitivity for effect sizes of 0.75 and 1, respectively, but at the cost of unsatisfactory FDRs, which were greater than 46%. MeTPeak exhibited well-calibrated FDR (12.3% and 11.4%) and moderate sensitivity of 50.4% and 81.5% for effect sizes of 0.75 and 1, respectively. QNB only had low power for an effect size of 1 . Overall, for the simple case without covariates, RADAR achieved high sensitivity while maintained low FDR at varying true effect sizes  , T2D (GSE120024), mouse liver (GSE119490), and mouse brain (GSE113781). To evaluate the sensitivity of different methods, we first checked the distributions of p values obtained from corresponding DM tests . We compared the DM sites detected in the reduced dataset with the results obtained from the full dataset  and MAPK pathways , including hypo-methylated PTEN and hyper-methylated BCL2  detected by RADAR Fig. a. We fou6A might play in T2D. We further analyzed these DMGs in related pathways and found the methylome of insulin/IGF1-AKT-PDX1 signaling pathway been mostly hypo-methylated in T2D islets . To sum6A [6A, making it a suitable method for validating DM sites discovered by RADAR analysis. We selected six DM sites  for single nucleotide-specific detection of m6A . This meSample size is an important parameter of the study design that directly affects the power and reproducibility of an inferential test . DM analThe choice of bin size in the RADAR analysis concerns the tradeoff between resolution and accuracy. At a given sequencing depth, the smaller the bin, the higher resolution achieved. However, a smaller bin size also implies fewer reads in a bin, resulting in increased sampling noise. At common library size of 20 million mappable reads or above, the default bin size (50 bp) should enable sufficient coverage for most enriched regions in the IP library. We recommend to use larger bin size  for data with smaller library sizes (< 15 million mappable reads) to include enough reads in each bin for DM tests.A potential caveat of using gene-level instead of local read count to represent pre-IP expression level is that exon-specific expression variation in alternative splicing scenarios would be underrepresented. Filtering out DM sites that co-localize with alternative spliced exons in post-processing could help avoid false signals due to this caveat.6A-seq datasets, we demonstrated that RADAR can achieve higher sensitivity with lower FDR than existing methods in the DM analysis. Taking advantage of newly developed SELECT method for experimental validation, we verified that RADAR analysis can uncover true differentially methylated sites. RADAR is a general framework that can be applicable to comparative profiling by MeRIP-seq of various types of RNA modifications including but not limited to N6-methyladenosine, N1-methyladenosine, and 5-methylcytosine. It also offers great flexibility to adopt to a wide range of mean-variance relationships in the data and accommodate different study designs. We believe RADAR will greatly advance our knowledge of the functions of post-transcriptional modifications.Using simulation and real mAll human tissue samples were collected with informed consent under approved University of Chicago Institutional Review Board protocols and in accordance with the Declaration of Helsinki. All experiments were conducted in accordance with approved protocol guidelines and regulations. Six omental tumor tissues were collected from newly diagnosed patients with advanced, metastatic high-grade serous ovarian cancer during primary debulking surgery at the University of Chicago. Seven normal fallopian tube tissues were collected from patients with benign gynecological conditions at the time of surgery.A minimum of 20,000 human islets equivalents (IEQs)/patient were obtained from the Integrated Islet Distribution Program (IIIDP) and Prodo Laboratories. Upon receipt, islets were cultured overnight in Miami Media #1A  and then handpicked, washed twice by self-sedimentation with ice-cold DPBS, and pelleted for RNA isolation. All studies and protocols used were approved by the Joslin Diabetes Center’s Committee on Human Studies (CHS#5-05). Samples from eight T2D patients and seven non-diabetic controls were collected for analyses in this study.Mettl14+/+ and Albumin-Cre;Mettl14flox/flox;Cre liver-specific conditional knockout mice [Mettl14 cKO mice on 42% high-fat diet for 3 months were sequenced and analyzed.Mouse liver tissues were collected from wild type Albumin-Cre;out mice . Four wi6A-immunoprecipitation (m6A-IP) was performed using EpiMark N6-Methyladenosine enrichment kit (NEB cat. E1610S). RNA eluted from m6A-IP was cleaned using RNA Clean and Concentrator . Input and IP samples were then used to prepare library with KAPA mRNA Hyper Kit . For fallopian tube and omental tumor tissues, total RNA was fragmented and directly subjected to m6A-IP. Takara Pico-Input Strand-Specific Total RNA-seq for Illumina  was used to construct libraries from total RNA where ribosome-derived cDNA was removed before final library amplification. T2D and mouse liver libraries were sequenced by the HiSeq4000 platform at SE50 mode. The ovarian cancer libraries were sequenced by the NextSeq 500 platform at PE37 mode.Total RNA was extracted from tissues using TRIzol (Invitrogen) according to the manufacturer’s instruction. For T2D and mouse liver samples, mRNA was further purified with Dynabeads mRNA DIRECT purification kit . mRNA was adjusted to 15 ng/μl in 100 μl and fragmented using Bioruptor ultrasonicator (Diagenode) with 30 s on/off for 30 cycles. mHisat2 [For each dataset, the raw sequencing data were mapped to the corresponding reference genome  by Hisat2  with parGenomicFeatures [Rsamtools [RADAR takes a GTF file as an input for gene annotation and obtains a gene model using the R package Features . Exons osamtools  is used ti, j depends on the abundance of RNA in the pre-IP RNA pool, the overall IP efficiency of that sample, the total sequencing depth of that IP library, and the methylation level of that locus. To normalize the variation due to sample-wise IP efficiency difference and sequencing depth variations of IP libraries, we estimate a sample-wise size factor from the fold enrichment ti, j is IP read counts and geneSum (gene-level) read count at corresponding gene. The reason that only top bins are used to estimate the overall IP efficiency is to exclude the regions where IP read counts are mainly attributed to non-specific binding.Unlike previous methods –11 that To account for the pre-IP gene expression level variation in IP read counts, we compute a gene-wise size factor by centering normalized gene-level counts to 1. For each bin, we divide the normalized IP read counts by the gene-wise size factor of the corresponding gene. The resulting IP read counts now reflect the methylation level as other factors have all been accounted for. The adjusted IP read counts representing methylation levels are further used for DM tests.We apply two filters to remove unwanted bins in the data: (1) we remove bins in which reads are depleted in IP libraries because read counts in these bins are likely attributed to non-specific binding during the immunoprecipitation and (2) we remove bins in which raw IP read counts are smaller than 15 (this cutoff can be defined by the user) because signals in regions without sufficient coverage will be too noisy and unreliable. Low IP read count also implies that the bin is likely a non-methylated region.Yi in the ith sample as follows:λi is the mean of a Poisson distribution, μ is a bin-specific intercept, X is the design matrix including the indicator of the groups of interest X0 and covariates Xj , β represent associated coefficients, and ei is a random effect following a log gamma distribution with a scale parameter ψ and mean equal to 1, i.e., ei ∈ log Gamma. Introducing a new variable wi ∈ Gamma, we have λi = eXβμ + wi. The differential analysis is equivalent to test against the null hypothesis β0 = 0.For each bin, we model the processed IP read counts wi, the likelihood of observing the data given all other parameters Θ is:After integrating out Y can be written as:The marginal log likelihood of observing We use the gradient ascent algorithm to calculate maximum likelihood estimators of all the parameters, which involves the calculation of the first derivatives.st + 1) and random effect parameter ψ from real data (T2D) to better reflect the property of real data. For each dataset, we simulated 26,324 sites where 20% of them were pre-defined as true DM sites with effect sizes of 0.5, 0.75, or 1. At pre-defined true DM sites, we simulated read counts with β = 0.5 (or 0.75 or 1) while β = 0 at null sites. Other alternative methods take INPUT and IP read counts as input data for DM tests. To convert our simulated enrichment data into paired INPUT and IP read counts, we used INPUT read counts from the real T2D dataset and generated corresponding IP read counts by rescaling simulated IP counts to match the IP/INPUT ratio in the real data.To assess the sensitivity and specificity of each method on detecting true DM sites, we simulated dataset of eight replicates with and without covariate. Since RADAR make inferential test on fold enrichment (pre-processed IP read counts adjusted for input RNA level variation), we first simulated this enrichment data (pre-processed IP read counts) using model (1). We draw the distribution of gene-specific intercept parameter We then applied RADAR, MeTPeak (version 1.1) , QNB , and 2 μl 10X CutSmart buffer (NEB) supplemented with H2O to 17 μl total volume. The mix was incubated at a temperature gradient: 90 °C for 1 min, 80 °C for 1 min, 70 °C for 1 min, 60 °C for 1 min, 50 °C for 1 min, and then 40 °C for 6 min. Subsequently, a 3 μl of enzyme mixture containing 0.5 μl Bst 2.0 DNA polymerase (0.02 U/μl) (NEB M0275S), 0.5 μl SplintR ligase (1 U/μl) (NEB M0375S), and 2 μl ATP (5 mM) was added in the former mixture to the final volume of 20 μl. The final reaction mixture was incubated at 40 °C for 20 min then denatured at 80 °C for 20 min. Then, qPCR reaction to quantify the “read through” oligos was assembled by 10 μl 2X qPCR master mix, 0.8 μl universal primer as designed in the oligo probes (10 μM), 2 μl reaction from previous step, and 7.2 μl H2O. To quantify the RNA expression level of each gene harboring the m6A site, we first prepared the cDNA from 50 ng of total RNA using SuperScript VILO Master Mix (Thermo Fisher 11755050). Then, 2 μl of cDNA was used for qPCR quantification of each gene. Finally, the gene expression level quantification was used to normalize the “read through” oligo probe quantification to obtain “relative read through” level for each site. Note the “relative read through” level reflects the non-methylated level, which is inversely correlated with m6A site.We applied the SELECT method to four control and T2D samples that have enough RNA material leftover from sequencing experiment. For each sample, 50 ng of total RNA was mixed with 0.8 μl up-probe and down-probe oligo (1 μM) of each target mN = 2 to N = 8 using Model (1) without covariates. We assessed the performance by plotting the sensitivity of each method against its FDR using DM loci obtained at an FDR cutoff of 10%. Additionally, we also made the ROC curve for each sample size by varying the FDR cutoffs when selecting predicted sites , GSE 120024 (T2D), and GSE 119490 (mouse liver). The mouse brain dataset was downloaded from GSE 113781 [Additional file 1: Figure S1. Scatter plot of read count. Figure S2. Read count distribution of INPUT data. Figure S3. Sequencing depth distribution of m6A-seq in published literatures. Figure S4. Variability distribution comparing RNA-seq and m6A-seq (MeRIP-seq). Figure S5. Evaluating performances of benchmarked methods on simulated data using sliding thresholds. Figure S6. P-value and effect size estimates on simulated data. Figure S7. Coverage plot of individual samples for example DM sites and bogus sites in the T2D dataset. Figure S8. Compare the methods to adjust for gene expression level. Figure S9. Compare results obtained from shallower sequence depth with that from original depth. Figure S10. Motif analysis and topological distribution of putative DM sites. Figure S11. Coverage plot to visualize differential m6A peaks in ovarian cancer. Figure S12. Representation of Insulin/IGF1-AKT-PDX1 pathway. Figure S13. Analysis of statistical power and the number of replicates. Table S1. Enriched DM genes in KEGG pathways in the T2D dataset. Table S2. Selected DM sites for experimental validation. Table S3. Oligo probes sequences and qPCR primer sequences.Additional file 2. Review history.


We performed a series of bioinformatics analysis on a set of important gene expression data with 76 samples in early stage of non-small cell lung cancer, including 40 adenocarcinoma samples, 16 squamous cell carcinoma samples and 20 normal samples. In order to identify the specific markers for diagnosis, we compared the two subtypes with the normal samples respectively to determine the gene expression characteristics. Through the multi-dimensional scaling classification, we found that the samples were clustered well according to the disease cases. Based on the classification results and using empirical Bayes moderation and treat method, 486 important genes associated with the disease were identified. We constructed gene functions and gene pathways to verify our result and explain the pathogenicity factor and process. We generated a protein-protein interaction network based on the mutual interaction between the selected genes and found that the top thirteen hub genes were highly associated with lung cancer or some other cancers including five newly found genes through our method. The results of this study indicated that contrast on the gene expression between different subtypes and normal samples provides important information for the detection of non-small cell lung cancer and helps exploration of the disease pathogenesis. Lung cancer is the most common malignant tumors, which poses a major threat to public health. In 2018, it was predicted that 1,735,350 new cancer cases and 609,640 cancer deaths will occur in the United States, including 13.49% lung cancer cases and 25.27% lung cancer deaths [In the recent years, researchers have paid more attention to the mechanism of the occurrence and growth of NSCLC, which has brought new breakthroughs to the diagnosis and treatment of this disease. However, because of the high cost of treatment and the presence of drug resistance, effective treatment is only applicable to a narrow population.With the development of information technology, using gene expression data resources to solve medical problems has become a general trend. Data mining technology is helpful to extract potential and valuable information related to diseases, so as to effectively prevent and control the diseases. Therefore, gene expression profile analysis has been widely used to identify new potential biomarkers of cancer , 5, amonIn this study, we focus on a particular set of gene expression data associated with early stage of NSCLC. We are interested in this data set of 76 samples because the data set contains detailed information about AC, SCC and normal samples. This information, as our study will show, is critical for the extraction of candidate diagnostic markers for NSCLC. We will use the affy package to read raw data, the edgeR package  to asseshttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33532) database. This dataset was based on the Affymetrix microarray GPL570 platform, which was submitted by Meister, et al. There are 100 samples in dataset GSE33532 including 80 NSCLC tissue samples and 20 normal tissue samples. Considering that 24 tumor samples do not have clear histopathological information, we selected 20 normal samples and 56 tumor samples for our analysis, including 40 AC samples and 16 SCC samples. These 56 samples were also classified as 32 samples in the first stage and 24 samples in the second stage according to the cancer infection  values firstly. In the process of sample preparation and sequencing, there is no biological significance such as sample batches which will affect the expression of a single sample. Therefore, we need to standardize the data of each sample to ensure the similarity of data distribution. Here, normalization by the method of trimmed mean of M-value (TMM) was applied .In the previous papers using GSE33532 datasets –18, the We followed the workflow in Bioconductor to find DEGs . Firstlyhttps://david.ncifcrf.gov/), a comprehensive set of functional annotation tool, to analyze GO function and KEGG pathway analysis of DEGs. It uses statistical methods to select the most prominent annotations in a large number of biological annotations, and the related information of their involvement in biological processes (BP), molecular function (MF), cell component (CC) and signal pathway can be found where p < 0.05 was considered to indicate a statistically significant difference.In this study, we used DAVID (https://string-db.org/), a database that collects and integrates known protein-protein interactions, to explore protein-protein interactions and construct PPI network. Through the plug-in network analysis in Cytoscape, the degree between nodes was calculated and the genes with the largest degree were selected to represent the hub genes which play important roles in the whole PPI network.We used STRING Click here for additional data file.


Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches.In this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases. Specifically, given a set of reference samples which were regarded as background, a novel index called single-sample Kullback–Leibler divergence (sKLD), was proposed to explore and quantify the disturbance on the background caused by a case sample. The pre-disease state is then signaled by the significant change of sKLD.The novel algorithm was developed and applied to both numerical simulation and real datasets, including lung squamous cell carcinoma, lung adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, colon adenocarcinoma, and acute lung injury. The successful identification of pre-disease states and the corresponding dynamical network biomarkers for all six datasets validated the effectiveness and accuracy of our method.The proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis. Critical transitions are sudden and large-scale state transitions that occur in many complex systems, such as ecological systems , 2, climThe rapid development of high-throughput technology provides new insights for computational analysis, even when there is only one single sample available. Actually, based on a sample of high-throughput data, it is possible to measure the expressions of thousands of genes simultaneously. Such high-dimensional observation at the genome-wide scale not only provides the global view of a biological system, but also presents the accumulated effects of its long-term dynamics. Motivated by this point, in this study we develop a data-driven computational method and achieve the single-sample detection of the pre-disease state, by exploring the rich dynamical information from the high-throughput omics data. Specifically, it is found that the qualitative state change often causes the significant changes in the distributions of some genes’ expression. Therefore, a novel index, the single-sample Kullback–Leibler divergence (sKLD), is proposed to quantify the disturbance brought by the single case sample on the background distribution, where the background or reference samples refer to samples collected from a few healthy/relatively healthy individuals. Correspondingly, an applicable algorithm is developed based on sKLD Fig. b, includhttp://cancergenome.nih.gov) and acute lung injury (GSE2565) from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). The successful identification of the pre-disease states in these diseases validated the effectiveness of sKLD method in quantifying the tipping point just before the critical transitions into severe disease states.We present the definition and algorithm of sKLD score in Methods section. Here, we used a single-sample with high-throughput omics data, to identify the pre-disease state or early warning signals of the disease deterioration based on the sKLD score. Achieving reliable identification with only one sample is of great importance in clinic application since it is usually difficult to obtain multiple samples from an individual who does not yet exhibit any disease symptoms during a short period. To illustrate how sKLD works, we applied our method first to a simulated dataset, and then to six real datasets, including LUSC, LUAD, STAD, THCA and COAD from TCGA database  presents two peaks, that is, when the network system is in a pre-disease state, the expressions of some nodes wildly fluctuate in a strongly collective manner, resulting a distinct distribution. This critical phenomenon is accurately detected by sKLD, which quantitatively provides a score for identifying the upcoming bifurcation point. Therefore, the numerical simulation validated the effectiveness of sKLD in detecting the early warning signal of a qualitative state transition. The detailed dynamical system is proposed in Additional file https://github.com/zhongjiayuna/KL_Project.It is seen in Fig. The sKLD has been applied to the microarray data of dataset GSE2565, which is obtained from a mouse experiment of phosgene-induced acute lung injury . In the To demonstrate the effectiveness, the proposed sKLD method is applied to five tumor datasets, lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), colon adenocarcinoma (COAD) from the cancer genome atlas (TCGA), all of which were composed by tumor and tumor-adjacent samples. The tumor samples were grouped into different cancer stages according to corresponding clinical information of TCGA, that is, the tumor samples were classified into seven stages for LUSC, LUAD and STAD, and four stages for THCA and COAD. The detailed sampling conditions are provided in Additional file Clearly, the significant change of sKLD successfully indicated the critical stages prior to the metastasis for all the five cancers Fig. a-e. To vp = 0.0034) between the survival curves of two sets of samples, i.e., samples derived before and after stage IIA of LUSC. For the samples solely from the two stages around the critical transition point, i.e., stages IIA and IIB, the survival time of stage-IIA samples is longer than that of stage-IIB samples , the top 5% most significant genes in terms of the cumulative area of the case sample are selected as “sKLD-signaling genes” for further functional analysis. Some genes in the common “sKLD-signaling genes” have been reported to be associated with the process of LUSC Table . For insBesides, functional enrichment through GO analysis shows that the common sKLD-signaling genes are involved in the biological processes including cytoskeleton organization, chromosome condensation, regulation of cell division and others Table . These bp = 3E-07) between the survival curves of samples before and after stage IIB of LUAD. Clearly, the survival time of before-transition samples (samples from stages IA-IIB) is considerably longer than that of after-transition samples (samples from stages IIIA-IV). For the samples solely from two stages IIB and IIIA around the critical transition point, the survival time of stage-IIB samples is much longer than stage-IIIA samples  between the survival time of two group of samples, i.e., samples respectively from the before-transition period (stages IA-IIIB) and from the after-transition period (stages IV) of STAD. It is also noted that the survival time of samples from stage IIIB is significantly longer than that from stage IV   Fig. i. It is p = 0)  Fig. j. As shoDetecting the early-warning signal for the sudden deterioration is crucial to most complex diseases. However, it is a challenging task to identify the pre-disease state prior to the occurrence of obvious symptoms due to the lack of samples, that is, there is usually only one single sample for an individual at a time point before an accurate diagnosis is made. Clearly, such single-sample problem, rising from clinical and experimental practice, leads to the failure of traditional statistic method, and thus requires new approaches that help to overcome the sample limitation. In this study, we proposed a single-sample-based computational framework, the sKLD method, to quantify the disturbance on the background caused by a sample. The sKLD has been applied to real-world datasets and successfully identifies the tipping points or critical states of complex diseases. Specifically, the significant change of sKLD score indicates the pre-disease state of phosgene-induced acute lung injury before the deterioration into pulmonary edema, the critical stage of (stage IIA) of LUSC prior to the lymph nodes metastasis, the critical stage (stage IIB) of LUAD before lymph nodes were metastasized, the critical stage (stage IIIB) of STAD before distant metastasis, the critical stage (stage II) of THCA before lymph node metastasis, and the critical stage (stage II) of THCA before lymph node metastasis. All these identified critical stages were validated by the survival analysis, that is, the patient would have a significantly better prognosis if they were diagnosed before the critical stage. Besides, at any other stages, there was no significant difference in the prognosis, suggesting that the identified critical stage is accurate and closely associated with prognosis. The functional analysis of sKLD-signaling genes is consistent with the upcoming deteriorations of diseases.There are three advantages of the proposed method. First, in contrast to the traditional biomarkers that are used to “diagnose disease” based on the information of differential expressions, sKLD is capable to “predict disease” based on the information of differential distributions among biomolecules. Second, given some reference samples, sKLD works with only a single sample. Third, it should be noted that sKLD is a model-free method, which implies that in the sKLD strategy there is neither feature selection nor model/parameter training procedure. It is thus different from the traditional machine learning or classification methods which, to produce a robust model in the learning process, requires a substantial number of case and control samples to avoid the overfitting problem.We proposed a novel computational method sKLD solely based on a single case sample. This method can effectively detect the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration. As the algorithm shown in Methods section, the sKLD is easy to implement and very flexible. It is therefore of great potential in personalized pre-disease diagnosis and prevention medicine. The identification of sKLD-signaling genes is also helpful in elucidating molecular mechanism of disease progression, and discovering prognosis indicators.in) between any pair of members in the DNB group rapidly increases;The correlation (PCCout) between one member of the DNB group and any other non-DNB member rapidly decreases;The correlation (PCCin) or coefficient of variation for any member in the DNB group drastically increases.The standard deviation , their distribution would significantly change when the system approaches the critical transition point. Therefore, by exploring the differential distributions  of some variables, it is possible to predict the upcoming qualitative state transition. On the other hand, a sample of high-throughput data enables us to analyze the expressions of thousands of genes simultaneously. Such a high-dimensional sample is actually enriched with dynamic information of accumulated effects, such as the gene interaction after a long-term development of the concerned biological system.P and Q, the K-L divergence is defined asThe Kullback–Leibler divergence (K–L divergence) was widely employed to measure the difference between two data distributions . It provP and Q. Particularly, DKL is zero only when the distribution P is identical with the distribution Q. DKL is positive when the distribution P is different from Q. Clearly, for the original K-L divergence, there is DKL ≠ DKL. In this study, we use a symmetric measure defined asIt should be noted that the K-L divergence in Eq.  is actuam genes/variables, then at each time point, the state of such system is expressed by a high-dimensional vector, i.e., the expressions of m genes/variables. A computational way is then developed to exploring the dynamic difference between the normal state and pre-disease state.Regarding a biological system as a time-dependent nonlinear dynamical system with Given a set of reference samples , the following algorithm is proposed to identify the pre-disease state by using only one case sample.[Step 1] Prepare a set of reference samples. The samples derived from the normal cohort are regarded as reference samples, which represent the background of relatively healthy individuals in the normal state. For numerical simulation, samples from a few initial time points are viewed as reference/normal samples. For real datasets, samples from a normal cohort or normal tissue are chosen as reference/normal samples, e.g., for the stage-course data from TCGA, the tumor-adjacent samples are taken as the reference.i, a Gaussian distribution k expressions of gi in the reference samples {S1, S2, …, Sk}. Then, a k-dimensional vector (j-th element is the cumulative area (its definition was shown in the Eq. (S4) of Additional file i in the j-th sample Sj  and five stage-course datasets LUSC, LUAD, STAD, THCA and COAD from TCGA database (http://cancergenome.nih.gov). The omics dataset GSE2565 comprises expression profiles from a mouse experiment, in which pulmonary edema was triggered by inhalation of carbonyl chloride. In this dataset, we discarded the probes without corresponding NCBI Entrez gene symbol. For each gene mapped by multiple probes, the average value was employed as the gene expression. The five stage-course datasets from TCGA contained RNA-Seq data and included both tumor and tumor-adjacent samples. The tumor samples were divided into different stages based on clinical (stage) information from TCGA, and the samples without stage information were ignored.The proposed method has been applied to six real datasets, i.e., the time-course dataset GSE2565 from NCBI GEO database (http://www.ncbi.nlm.nih.gov/gene). The enrichment analyses were separately obtained using web service tools from the Gene Ontology Consortium  and client software from Ingenuity Pathway Analysis .For all the diseases, functional annotations were performed by searching the NCBI gene database (Additional file 1. Identifying critical state by single-sample Kullback–Leibler divergence.Additional file 2. The signaling genes of LUSC, LUAD, STAD, THCA and COAD.Additional file 3. The common signaling genes for acute lung injury.Additional file 4. The common sKLD-signaling genes for LUSC, LUAD and STAD.