Traditional Chinese Medicine (TCM) is a style of traditional medicine informed by modern medicine but built on a foundation of more than 2500 years of Chinese medical practice. According to statistics, TCM accounts for approximately 14% of total adverse drug reaction (ADR) spontaneous reporting data in China. Because of the complexity of the components in TCM formula, which makes it essentially different from Western medicine, it is critical to determine whether ADR reports of TCM should be analyzed independently.R , P (precision ratio), and D (discrepancy ratio) based on the reference database and then constructed a decision tree for data classification based on such indicators.Reports in the Chinese spontaneous reporting database between 2010 and 2011 were selected. The dataset was processed and divided into the total sample  and the subsample (including TCM data only). Four different ADR signal detection methods-PRR, ROR, MHRA and IC- currently widely used in China, were applied for signal detection on the two samples. By comparison of experimental results, three of them—PRR, MHRA and IC—were chosen to do the experiment. We designed several indicators for performance evaluation such as R1-R2 = 0.72%, P1-P2 = 0.16% and D = 0.92%; For MHRA: R1-R2 = 0.97%, P1-P2 = 0.20% and D = 1.18%; For IC: R1-R2 = 1.44%, P2-P1 = 4.06% and D = 4.72%. The threshold of R,Pand Dis set as 2%, 2% and 3% respectively. Based on the decision tree, the results are “separation” for PRR, MHRA and IC.For PRR: In order to improve the efficiency and accuracy of signal detection, we suggest that TCM data should be separated from the total sample when conducting analyses. The World Health Organization (WHO) defines adverse drug reactions (ADRs) as harmful and unintended reactions, which occur from the standard use of medicinal dosages for prophylaxis, diagnosis or treatment of diseases, or for the modification of physiological functions. ADRs include side effects, toxic effects, residual effects, idiosyncratic reactions, multiple infections arising from anti-infectives, drug dependence, and carcinogenic and mutagenic actions . It is vCurrently, the methods used in signal detection in China mainly include Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Medicines and Healthcare products Regulatory Agency (MHRA) and Information Component (IC) –9. TheseSafflower) injections, the safety issues of Lei Gong Teng (Tripterygium wilfordii) preparation, and the digestive reactions of the compound Qingdai pill. For example, Shengmai injection is a traditional Chinese medicine injection composed of Red Ginseng, Ophiopogon japonicus and Schisandra Chinensis. The adverse reactions of Shengmai injection in severe cases are as follows: body as a whole-general disorders accounted for about 53.2%, respiratory system disorders accounted for 20.7% and cardiovascular disorders accounted for about 11.4%. A total of 179 cases of Shengmai injection were reported, including anaphylactic shock (90) and severe anaphylactoid reaction (89), accounting for about 35.2% of all severe cases .Tab.Tab20].TThe four methods are applied to the subsample respectively. The signal detection of the subsample was done at the TCM product level. The results of positive signals are listed in Table As shown in Table We knew from Tables To judge the superiority and inferiority of the signal detection outcomes in the two samples before and after data separation, the following issues needed to be addressed: How to evaluate the difference of results of signal detection methods applied to the two samples before and after data separation? What indicators were needed to be set for discrimination? What was the decision- making process? Flowchart on decision-making process is presented as follows Fig. .Fig. 1DeDetecting signals in the total sample and the subsample respectively. Then, two results were obtained. Some indicators of evaluation method were constructed and used to evaluate the superiority and inferiority of the two results. The decision-making was conducted based on the reference database and the evaluation results.The reference database provided an objective evidence for classification decisions. Signal detection of the two samples was performed and compared with the reference database, which could be structured into a fourfold table as follows Table .Table 5Ta represents the number of ADR pairs with positive signals in both the total sample and the subsample, a1 represents the number of ADR pairs that are present in the total sample, the subsample, and the reference database, a0 represents the number of ADR pairs that are present in the total sample and the subsample but are absent in the reference database, and a = a1 + a0. The other symbols such as b, c and d are analogous to a. The ADR pairs in b1 and c1 are distinct signals between two samples, and thus can be used as a basis for classification decision.In Table R - Recall ratioIndicator R1 to describe the ability to detect known signals in the total sample, as represented by formula P - Precision ratioIndicator By comparing the recall ratios between the two samples, the differences in the ability to detect known signals can be distinguished, which also reflect the differences in signal detection sensitivities between these two samples based on the reference database. Therefore, the recall ratio is a key indicator that should be used as the primary basis for classification decisions.a1 is much greater than b1 and c1, there is no significant difference between b1 and c1 even though they have markedly distinct values. Therefore, if the difference between R1 and R2 is slight, it is necessary to define the precision ratio. The precision ratio, a measure of the accuracy of detecting known signals, is the proportion of the known signals detected based on a certain sample. We use precision ratio P1 to describe the ability to detect known signals in the total sample, which is represented by formula (The drawback of formula  and 2)  2) lies  formula .3\documea + b is the total number of signals detected in the total sample and a1 + b1 is the number of known signal detected in the total sample. Similarly, using precision ratio P2 to describe the ability to detect known signals in the subsample, as represented by formula (3)D - discrepancy ratioIndicator From Table  formula .4\documeDiscrepancy ratio represents the diversity measurement of the detection outcomes in the two samples, represented by formula .5\documeb + c represent the total number of different detection outcomes in the two samples.In formula , b + c rR, P and D in the total sample and the subsample to decide whether to classify TCM data, the process is as follows.R of the two samples is considered .The primary basis is to consider the coverage percentage of known signals based on the detected outcomes in the two samples. Specifically, the indicator P of the signal detection needs to be considered, which is the precision ratio of the two samples .When there is little difference in recall ratios of the two samples, the indicator D is compared. If the discrepancy ratio exceeds the prescribed threshold (D > Dt), the decision outcome is “non-separation”, otherwise is “separation” (Dt represents the threshold of D).When there is little difference in both recall ratio and precision ratio of the two samples, then indicator Using the indicators of Based on the above analysis, the decision tree for determining whether to perform classified detection of data is constructed as Fig. The statistical results of the two samples are shown in Table The PRR, MHRA, and IC methods are used to perform experiments with the total sample and the subsample and the results are shown in Table R, P and D across three signal detection methods. For R, the difference of PRR, MHRA and IC is 0.72%, 0.97% and 1.44%, so the mean of three differences about equal to 1.04%. Thus, the threshold of R is set to 2%, an integer slightly larger than the mean. Similarly, for P, the mean of the differences is (0.16% + 0.20% + 4.06%)/3 ≈ 1.47%, so the threshold of P is set to 2%. For D, the mean of the differences is (0.92% + 1.18% + 4.72%) /3 ≈ 2.73%, so the threshold of D is set to 3%. In brief, we set Rt to 2%, Pt to 2% and Dt to 3% in the following decision process. Based on the decision tree , this could result in uncertainty of the decision outcome. At the same time, according to the previous data analysis, there have been some differences in the two samples. Thus, according to the TCM data characteristics, the decision result is biased towards “separation” when the corresponding values of the three indicators are relatively close before and after data separation.Table Table For PRR and MHRA, decision-making paths are consistent and the results are all “Separation” because all the values of three indicators fall within the according thresholds. For IC, however, decision-making path is deferent from PRR and MHRA. The recall ratio of the two samples is very similar, but the subsample has a significantly high precision ratio than the total sample. So, the conclusion is “Separation” for IC.The dataset of this research is fixed, that is to say, the total sample and the subsample are fixed, so the values of three indicators calculated based on two samples are constant, and they will not change with the different threshold. Thus, it is impossible to determine the optimum threshold in traditional ways such as Precision Recall curve, Receiver Operating Characteristic Curve or Mean Average Precision. In this paper, the choice of the threshold is mainly based on the mean value of the difference between the same indicator across all methods before and after data separation. If the ADR data of more years can be obtained, we can group data by increasing year by year to observe change trends of the difference value of the three indicators. It will help to determine the thresholds more accurately. This will be the further work we need to do.Our research has a few limitations. First, the low quality of the ADR report data and the co-existence of multiple signal detection methods may bring uncertainty to the results. Second, this study is based on the data provided by the CFDA during 2010–2011. The limited amount of data (approximately 2.2 million) may not be representative of the total data of the CFDA (approximately 10 million). Third, another limitation of the study is the use of only the ADRs extracted from drug product labeling manually. In China, there is a requirement that all ADRs observed in clinical studies should be submitted as spontaneous reports and those verified by the experts should be added to the drug product labeling. But the reference database might not include the kinds of signals which are novel and unexpected associations that appear once a drug is used by a broader group of individuals.R, P and D based on the reference database, and then construct a decision tree for data classification. Finally, we conduct experiment on the total sample and the subsample (TCM). Experiment results show that the conclusions are all “Separation” for PRR, MHRA and IC. Thus, we suggest that TCM data should be separated from the total data when conducting analyses.Whether TCM should be independent signal detection or not has become an important issue of pharmacovigilance in China. In order to address this problem, we propose a method based on decision tree in this study. We first analyze the results of applying four mainstream ADR signal detection methods that have been used widely in China and select three of these four methods. We set three indicators used for performance evaluation such as


Cancer molecular profiling provides better understanding of tumor mechanisms and helps to improve the existing cancer management. Here we present the gene expression signatures from ∼9000 human tumors with clinical information across 32 malignancies from The Cancer Genome Atlas project (TCGA). Major predictors from the RNA sequencing data that were significantly correlated with cancer survival were identified. The expression level of these prognostic genes revealed significant genomic pathways that were clinically relevant to survival outcomes across human cancers. Furthermore, it is shown that in most cancer types, combinations of these genomic signatures with clinical information might yield improved predictions. Thus, with respect to clinical utility, our study reveals the promising values of genomic data from the pan-cancer perspective. Cancer is a global health burden and the second leading cause of death . DespiteGenomic features emerge as promising biomarkers for cancer . Of the The cancer genome atlas (TCGA) project “motivated large-scale genomic efforts to obtain the complete catalogs of the genomic alterations in cancer” . BesidesIn the present study, we depicted the global pan-cancer prognostic landscape by analyzing the expression signatures from ~9000 human tumors across 32 malignancies from TCGA data sets. Furthermore, the clinical utility of survival predictions was evaluated by combining the genomic data with clinical information.Patient information with complete RNA sequencing data and clinical data of all TCGA cancer types were collected (32 tumor types) .Data analysis steps and clinical characteristics of the cancer patients were shown in Figure To explore the pan-cancer prognostic signatures, pan-cancer dataset was built by combining all the cancer patients. Samples were randomly assigned into two groups, where 80% of the samples were assigned as the training group and 20% as the testing group. By cox regression analysis for the training group, the top ten adverse genes  and top ten favorable prognostic genes  were identified Figure . Based oTo assess the clinical utility of the risk score, correlation of the risk score with the clinical variables in the training group was explored. In the analysis, higher scores were associated with male patients, patients with older age, and patients with advanced tumor stages Figure . FurtherThe RNA-seq data demonstrated great value for cancer prognosis. Risk scores specific for each type of cancer were shown in Table To evaluate the effect of different normalization method for the RNA-seq data, quantile data were transformed into z-score or being applied the voom normalization. As shown in For the above prognostic model, the high or low expression for prognostic model was determined by the median expression level of each gene. The gene was divided as binary categorization such as 1 for high expression  and 0 for low expression . Here we also applied the z-score (continuous variable) directly to propose a prognostic model that can reflect the values of gene expression (Table The prognostic genes (in each cancer type and in the whole cancer population) were filtered by a specific cutoff . As an investigation of the relationship of different prognostic gene across different cancer types, prognostic genes in each cancer type were compared with the whole cancer population. As shown in the Based on the prognostic risk score, patients were stratified into two different survival groups of a positive risk score and a negative risk score. This unsupervised cluster analysis showed obvious distinctions between the stratified survival groups, both in the training group and the testing group Figure . To linkIn order to assess possible effects of different pathways, the GSEA for every sample were evaluated using the single sample gene set enrichment analysis (ssGSEA). Based on the calculated scores for each pathway, cox analysis was performed to evaluate their prognostic effects. Results showed that most of the significant pathways from the GSEA output showed positive correlations with the survival outcome Figure . In addiSince the gene expression analysis and pathway analysis showed great prognostic values in the study, prognostic power of gene expression data were further explored. C-index was applied to assess the predictive power of the gene expression data alone or combined with clinical information. To improve accuracy, cancer types that don't have enough death events  were excluded. Cancer patients were randomly split into 80% training and 20% testing for 100 times to calculate the final C-index. As shown in Figure To explore any additional prognostic power, the gene expression data was combined with clinical information. Significant clinical features  were applied as baseline to build the cox model. A feature-selection step against the residuals was utilized to include the gene features that better fit the model. Results showed that the most gene expression data alone (18 out of 20 cases) had significant predictive power (C-index > 0.5). Incorporating clinical information to gene expression data statistically boosts the model performance in 12 cancer types  (p < 0.05) Figure .In this study, we assessed the clinical utility of genomic expression data from ~9000 cancer patients of 32 tumor types. The prognostic power across different cancer types was also evaluated , 8.et al. reported that for cancer patients, incorporating molecular features with clinical information yields significantly improved predictions. However, they only focused on 4 cancer types , and no conclusions could be drawn for the whole cancer population [et al. described the genomic prognostic landscape across human cancers, highlighting the promise of genomic expression data as biomarkers for clinical outcomes [Currently, only a few gene expression-based markers are routinely used in clinical practice –12. The pulation . Recentloutcomes . In our Based on the genomic expression data in the whole cancer population, the top prognostic genes were identified, such as FOXM1, CBX7, CREBL2 and SKP2, which were consistent with previous studies –16. NotaBecause of heterogeneity, many statistical methods have been developed to analyze cancer genomics, based on gene sets, pathways and network modules –19. For To explore prognostic power, our results showed that combining the clinical and molecular information could improve the predictive power of the gene expression data in most cancer types. Although the absolute magnitude gains were limited, the gene-expression signatures provide new biological insights into the process of cancer progression and metastasis that can help to improve the prediction power . ActuallIt is also important to realize that gene expression information is just one of the abundant molecular data  revealing the biological complexity of cancer. Other molecular information will also improve our understanding of the genotype–phenotype relationships involved in cancer. On the other hand, regarding the reliability and the reproducibility of the clinical use of molecular data, future technology, statistical and analytical methods are in great need to catch up with clinical needs .In conclusion, our gene analysis and pathway analysis showed significant values for the prediction of survival outcomes for cancer patients. Additionally, it was found that by combining clinical information with molecular data, the model performance could be boosted statistically in most cancer types. However, further efforts would be needed to generate prognostic models ready for clinical use in the future.https://tcga-data.nci.nih.gov/tcga/). RNA sequencing data was obtained from the GDAC Firehose System (http://gdac.broadinstitute.org/). To maintain data consistency, only the RNA sequencing data from the platform of Illumina HiSeq 2000 RNA Sequencing V2 was included. Patients who have a complete clinical and RNA sequencing data were screened for further analysis. For each cancer data set, patients were split into two groups randomly: 80% as the training set and 20% as the testing set. For the pan-cancer study, all RNA sequencing data were combined by intersecting the common genes across different cancer types.Clinical and survival data were acquired from the TCGA Data Portal  and multiplied by 1000, to get the quantile normalization for survival analysis. Furthermore, quantile data were also transformed to the z-score or normalized by “voom” to evaluate the effects of different normalization method. The Z-score was calculated as “(tumor expression - mean expression in reference) / standard deviation of expression in reference”. The voom normalization was applied using the R package “limma”. It estimates the mean-variance relationship of the log-counts and generates a precision weight for each observation.The association of each gene expression with survival outcomes was assessed via cox proportional hazards regression using the ‘coxph’ function of the R ‘survival’ package. Cox coefficients, hazard ratios with 95% confidence intervals, p values, and z-scores were obtained for each array probe. Top prognostic genes were identified by the values of z-scores. Based on these top prognostic genes, risk scores were built and it was defined as the weighted sums of the independent prognostic gene values . They were weighted with their regression coefficients from the cox models. Based on the prognostic risk score, further cox regression analysis and correlation analysis with clinical variables were performed.Differential expression analysis was done using R “limma” package. Based on the limma output for the most differentially expressed genes, unsupervised hierarchical clustering analysis was used to discover the gene expression patterns of these groups sharing common characteristics. Heatmap was constructed using the R “gplots” package.Prognostic gene sets are groups of genes that share common biological function. The evaluation of prognostic gene sets was performed using gene set enrichment analysis (GSEA) , where get al [Performance evaluation of gene expression data was conducted based on the method suggested by Yuan et al . Firstly


This work was conducted to characterize the 16S rRNA gene profile of a cohort of patients with traditional risk factors for developing atherosclerosis. The patients in the cohort were divided into two extremes; those predicted to develop extreme atherosclerosis who did not (Protected), and those predicted not to develop atherosclerosis who did (Unexplained). Bacterial DNA was isolated from stool and saliva and this was used to determine the V4 variable region of the 16S rRNA gene sequence composition of the samples in triplicate. Specifications TableValue of the data•The data was collected using the Earth Microbiome consortium V4 variable regions and should be comparable to other human microbiome studies that use the same primer sets.•The data was collected in triplicate and can be used as a reference dataset to characterize technical variation in human microbiome studies.•The data are from a cohort of patients with extreme phenotypes of atherosclerosis and can be used in meta-analyses of this condition.•The data are from two different body sites, and can be used to compare the microbiota in different human niches.1https://www.ebi.ac.uk/ena) with the accession number PRJEB24929, and can be accessed with the link: https://www.ebi.ac.uk/ena/data/search?query=PRJEB24929.The data are PCR-amplified V4 variable regions that were paired-end sequenced on an Illumina MiSeq. The raw fastq file was downloaded and demultiplexed based upon identity to combinatorial barcodes 2Western University Health Sciences Research Ethics board .The study and participant cohorts are described in ref. Fresh fecal and saliva samples from 20 volunteers in each group that were the most extreme in phenotype were frozen on dry ice within 1 h of sampling. Samples were thawed and purified using the standardized methods described https://github.com/ggloor/miseq_bin/releases/tag/v1.0. In brief, the primers used were: GTGCCAGCMGCCGCGGTAA, and GGACTACHVGGGTWTCTAAT, with 8-mer inline barcodes according to a published protocol One to five ng of purified DNA was used as input for PCR amplification using the Earth Microbiome primers for the V4 variable region of the 16S rRNA gene. The exact protocol, primers, barcodes and standard operating procedures (SOP) are permanently archived at: Samples were processed for analysis using the software released in the archived pipeline, using the barcode and sample information in the metadata table attached to the ENA archived data.https://github.com/ggloor/miseq_bin. This script demultiplexes inline combinatorial barcoded paired-end fastq sequences into forward and reverse files, one per sample per direction, with the primers and barcodes removed. Samples were named according to the convention given in the MiXS file attached to the archived DNA reads.The actual reads deposited in the ENA were demultiplexed with the Perl script ‘demultiplex_dada2.pl’ archived at The actual table of counts is appended as ‘td_OTU_tag_mapped.txt’, and the minimal metadata file appended as ‘category. txt’. The read table was processed using a compositional data analysis paradigm In brief, the operational taxonomic units (OTUs) were filtered to remove OTUs that contribute less than the median amount of variance of all OTUs to the dataset. In the entire dataset, this reduced the number of OTUs from 984 to 492; low variance OTUs are not informative by definition. A secondary filter was applied to remove all OTUs with a relative abundance less than 0.5%, reducing the OTU table from 492 OTUs to 363. Filtering in this way can be done without a loss of information on the composition when using a compositional data analysis paradigm [@bian:2017]. Data were subset to only gut, or only saliva and the operations repeated. The gut only samples had 289 OTUs and the saliva only samples had 136 OTUs after filtering. Zero count reads were imputed with the “CZM” function of the zCompositions R package


Instead, alleles at heterozygous loci can be compared within a sample, thereby controlling for those confounding factors. Here we introduce a method for chromatin structure-based allele-specific pairing of regulatory variants and target transcripts. With phased genotypes, much of allele-specific expression could be explained by paired allelic cis-regulation across a long range. This approach showed approximately two times greater sensitivity than QTL mapping. There are cases in which allele imbalance cannot be tested because heterozygotes are not available among reference samples. Therefore, we employed a machine learning method to predict missing positive cases based on various features shared by observed allele-specific pairs. We showed that only 10 reference samples are sufficient to achieve high prediction accuracy with a low sampling variation. In conclusion, our method enables highly sensitive fine mapping and target identification for trait-associated variants based on a small number of reference samples.Genome-wide association studies (GWASs) have identified a large number of noncoding associations, calling for systematic mapping to causal regulatory variants and their distal target genes. A widely used method, quantitative trait loci (QTL) mapping for chromatin or expression traits, suffers from sample-to-sample experimental variation and  Most disease associations discovered by genome-wide association studies (GWASs) are distant from coding genes. It was shown that these noncoding variants are concentrated in regulatory DNA marked by DNase I hypersensitivity or histoAdditional methods and data are required to map these functional regulatory variants to their target genes. Expression QTL (eQTL) analysis has been commonly used for the variant-gene mapping–16. Howetrans-acting effects. Because of sample-to-sample experimental bias, for example due to different read depths or other unspecified batch effects, true biological variation is often buried in confounding technical noise. The effect of trans-acting mechanisms can be illustrated by negative-feedback control that cancels out variability across samples with different cis-regulatory genotypes[trans-acting influences. Therefore, this approach should confer greater sensitivity in uncovering the direct influence of cis-regulatory variation[QTL mapping for either chromatin or expression traits may suffer limited sensitivity because of technical variation and genotypes. In contvariation.cis-regulatory elements and target transcripts, and then apply machine learning for the identified pairs.To overcome the limited sensitivity, QTL mapping requires a large cohort of samples to increase statistical power. In contrast to QTL mapping, allelic analyses are not hampered largely by small sample size, insofar as there is at least one heterozygous sample with a sufficient read depth for the given locus. However, there is limitation in coverage  especially for less frequent variants. The same problem applies for QTL mapping. A powerful workaround may be the employment of machine learning. Diverse features of the identified variants can be learned and used to predict functional variants that were not testable using given samples. Here we first perform allele-specific, long-range mapping of We collected data of chromatin immunoprecipitation-sequencing (ChIP-seq) for histone modifications (including H3K27ac and H3K4me1), RNA-sequencing (RNA-seq), and phased genotyping on 100 genetically different lymphoblastoid cell lines–9,22 S1. This daWe examined the degree to which regulatory allele imbalance was reflected in paired allele-specific expression. To this end, the transcript allele ratios were regressed on the paired ChIP-seq allele ratios for each histone mark, and the explanatory power of the linear regression model was obtained. Here we focused on H3K27ac and H3K4me1. The explanatory power of linear regression for the imbalance scores was consistently high across varying distances between the paired SNPs , and then searched them for eQTLs by associating them with the expression level of genes connected via chromatin interaction. For fair comparison, we started with the same sets of 814 and 875 GWAS SNPs for which at least one LD SNP was heterozygous in at least one sample and that resided in H3K27ac and H3K4m1 regions having chromatin interactions . Becauseapproach . This unAs for H3K27ac, there were 176 GWAS SNPs that were identified by our allelic analyses. Among them, 35 SNPs (20%) were also identified by the histone QTL-eQTL methods. Regarding H3K4me1, 21 out of 112 SNPs (19%) were supported by QTL mapping. However, there were more cases in which only allelic mapping was able to detect. For example, ITGA4, a therapeutic target for multiple sclerosis and CrohAnother example was RASSF5. This gene was recently shown to negatively control lymphocyte proliferation and prevents autoimmunity. Indeed,Previous QTL studies commonly reported that many of regulatory variants associated with histone modification or TF binding were not associated with gene expression variability–12. In ocis-regulatory or exonic heterozygotes especially when sample size is small. Therefore, we employed a machine learning method that predicts missing positive pairs whose allelic imbalance cannot be directly tested. To apply machine learning, we first identified testable cases by checking whether we could assess allelic imbalance thanks to the presence of heterozygous loci with sufficient read depth in each sample. Some of the testable pairs passed our imbalance tests and were used as true cases for machine learning. The testable pairs that failed to pass the imbalance tests, in other words, the cases in which cis-regulatory regions or transcripts show no allelic imbalance, were used as false cases. We trained Random Forest to learn 259 features , our method enables the identification of causal regulatory variants in LD and their functional target genes. This task requires a reference panel consisting of a small number of samples for which matched epigenome, transcriptome, and phased genotypes are available. In addition, a reference chromatin interactome dataset is needed to link regulatory variants and target genes. Instead, large-scale reference eQTL data can serve the same purpose. For example, the GTEx project providesSimilar attempts can be made through QTL mapping. However, this requires a large number of samples. The accuracy of QTL mapping increases in proportion to the number of samples used. While reference eQTL data have been made available in many tissues, hQTL or other chromatin QTL mapping has been performed only in lymphoblastoid cells. With eQTL data alone, causal variants and their direct, physical target genes cannot be mapped. Moreover, there is inherent limitation that undermines the sensitivity of association detection. Our results show that only ten samples enable highly sensitive detection of allele specificity. This approach can be extended to cover different traits. For example, obesity associations can be dissected by examining a small number of reference data based on genetically different adipose tissues. In conclusion, our method is expected to assist in the annotation of a large number of trait-associated variants residing noncoding regions of the genome.http://www.ebi.ac.uk/arrayexpress/) and under accession numbers GSE47991, GSE19480, and GSE50893 at Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/). From another study[We collected data of ChIP-seq for histone modifications (including H3K27ac and H3K4me1), RNA-seq, and phased genotyping in 100 genetically different lymphoblastoid cell lines–9,22. Thher study were avaher study were nothttps://www.broadinstitute.org/gatk/guide/best-practices) by using the Picard tool[The bam files of the collected raw data had been aligned to different releases of the reference genome. Thus, we re-mapped the data to GRCh37/hg19,27. The card tool and GATKcard tool. In the http://www.1000genomes.org/) phase 3 was referenced for LD calculation. For each human subpopulation , GAB blocks and GAM blocks, by the algorithm of Gabriel et al.[A total of 2,351 GWAS SNPs spanning 51 immune-related diseases and traits  were ret2 P = 0.05 was used as a threshold for allelic imbalance across multiple samples.Among variants that passed all filtering processes, heterozygous SNPs were selected. Sufficient sequencing depth is required for accurate allele imbalance testing. We thuscis-regulatory region. H3K27ac, H3K36me3, H3K4me1, and H3K4me3 were regarded as activating marks while H3K27me3 was regarded as a repressive mark. We paired ChIP-seq imbalance and RNA-seq imbalance only when the major regulatory allele and major transcript allele were on the same chromosome for the activating histone marks, and the opposite for the repression mark. For functional analysis of the paired genes, we ran WEB-based GEne SeT AnaLysis Toolkit [RNA-seq variants were assigned to their respective gene using the transcript location data provided by the hg19 version of RefSeq. We employed seven chromatin interactome datasets  and obtabGestalt).cis-regulation. The transcript allele ratios were regressed on the paired cis-regulatory allele ratios for each histone mark, and the explanatory power of the linear regression model was obtained as R2. There were cases in which multiple cis-regulatory regions were mapped to a single transcript. In these cases, we considered the multiple pairs independently. The explanatory power was plotted according to the genetic distance between the two variants, 1−|r|, where r is the Pearson coefficient of correlation measuring linkage disequilibrium between the two loci. The r value was obtained by using the 1000 Genomes Project[R2 was computed for all pairs within a given genetic distance. For comparison with the chromatin interactome data, we used reference eQTL data for mapping cis-regulatory variants to their target genes. A total of 358,199 and 478,204 significant eQTL-gene pairs in whole blood and lymphoblastoid cells[We wanted to test the extent to which allele-specific expression is explained by allele-specific s Project phase 3 oid cells,14, resphttp://www.blueprint-epigenome.eu/). We chose cell lines related with inflammation processes, including CD4+ T cells, CD8+ T cells, macrophages, monocytes, neutrophils, NK/T cells, and B cells. In addition, GM12878 and K562 data were obtained from the UCSC Genome Browser. All available histone ChIP-seq data were used. All the histone ChIP-seq data were in the narrow-peak bed file format. We assigned 1 or 0 for each ChIP-seq feature. For distal cis-regulatory regions, we assigned 1 if ChIP-seq peaks covered the variant of interest. For the promoter region of target genes, we assigned 1 when the ChIP-seq peak covered at least half of the promoter (1.5 kb upstream ~ 0.5 kb downstream of the transcription start site (TSS)). For the features of target genes, we determined whether each gene belonged to a specific GO term. We chose GO terms related to immune process and inflammatory response. The distance from the cis-regulatory variant to the target gene was defined based on the TSS of the target gene. The affinity of transcription factor binding at the distal cis-regulatory variant was estimated by FIMO[cis-regulatory variant and its linked gene shares similar features as those with observed allelic imbalance. We used an R package named randomForest[We chose Random Forest for our d by FIMO. ROC plodomForest. The defdomForest. From thWe wanted to estimate the number of samples needed to build a reliable Random Forest classifier. A subset of 2, 5, 10, or 20 random samples was used for allelic prediction and 10 or 20 samples were used for QTL prediction. The sampling was repeated 10 times to generate 10 such subpanels, for each of which an ROC curve was drawn based on 10 Random Forest classifiers resulted from a 5-fold cross validation with two repetitions each time. The variability among the 10 ROC curves from the 10 subpanels was observed. Next, we sought to apply our model for predicting missing positive pairs from small-size panels. To this end, we selected untestable cases in which we could not assess allelic imbalance due to absence of heterozygotes among the given samples. In some cases, observation could not be made because we filtered out heterozygous SNPs according to the read depth and imbalance ratio as described above. The previously built Random Forest classifiers for 10 samples were used to rescue missing positive cases from the untestable set. As described above, the sampling was repeated 10 times each with 10 Random Forest classifiers. Therefore, an untestable set was generated for each of the 10 subsets, and the matched 10 classifiers were run for each target set. Positive calls were identified as being supported by more than 5 of the 10 classifiers. To assess the capability of our prediction method, we examined how many of the positive calls actually displayed allelic imbalance when the whole set of samples in the panel was tested for allele specificity.S1 FigThe chromatin interactome data were merged and permuted to connect allele imbalance pairs randomly. Because only allele-specific pairs in the same regulatory direction were mapped, a certain level of explanatory power was achieved even with randomization. However, there was an overall reduction compared to the real data (compare the grey curves with the coloured lines). Four different permutations were performed for H3K27ac.(PDF)Click here for additional data file.S2 FigThe ChIP-seq SNPs (blue) showing allele imbalance (blue bar graphs) with the risk allele underrepresented were connected to RASSF5 as indicated by different chromatin interactome datasets (black lines). The RNA-seq variants (red) showed allele-specific expression (red bar graphs) in the same direction as the ChIP-seq variants. eQTL mapping failed to detect association (boxplots).(PDF)Click here for additional data file.S3 FigRandom Forest prediction was performed after permuting the assignment of features to each pair. Permutation was repeated 10 times (grey ROC curves).(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting.hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically.This prompted us to create R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.The website and  In addition to current clinically used risk factor scoring systems, several independent gene expression profile (GEP) based risk stratifications have been proposed, with biological and clinical significance in hematological cancers. Although drug targetable genes, which are only expressed in subtypes of e.g. DLBCL tumours have been identified, they are not readily applicable in clinical research and routine settings due to a lack of available routine diagnostic tests , 2. developeThe traditional lymphoma staging and risk classification systems are based on the Ann Arbor classification for staging of lymphoma  and simple prognostic tools such as the international prognostic index  for larhemaClass.org are compared to the existing state-of-the-art and approved ABC/GCB classifications of DLBCL. We believe that hemaClass.org will provide a novel and user-friendly concept for bringing complex molecular classification of diseases more swiftly into daily clinical practice.In clinical settings, the methods need to be applicable for a single patient sample and straightforward to use. This prompted us to develop a user-friendly and flexible web-based tool for ABC/GCB, BAGS, and REGS classification using our recently developed classifiers for microarray data based on the Affymetrix’s Human Genome U-133 Plus 2 array. The classifications made by the web-based tool hemaClass.org is illustrated in The workflow architecture of hemaclass.org was created using the statistical programming language R hemaClassribed by . To mimihemaClass.org allows users to upload their own RMA reference dataset prepared under similar conditions. In the this paper a laboratory specific RMA reference is referred to as an InLab reference. InLab references were simulated by selecting a random subset of 30 samples from each cohort. Samples were also one-by-one RMA normalized using the LLMPP CHOP dataset as an external reference; this is referred to as ExLab reference normalization.The one-by-one normalization has the implicit assumption that the samples and reference follow the same distribution. This assumption might be violated by batch effects arising from differences in laboratory specific sample preparations and can cause severe bias in the normalization; to accomodate this hemaClass.org. However, in GEP experiments, the number of probe-sets present on the microarray always outnumbers the sample size. Collinearity present among the features further aggravates the problem of identifying genes responsible for the underlying biological mechanism. Regression under these ill-posed circumstances is typically handled by so-called regularization. Here the elastic net penalty [Logistic and multinomial regression were used in all classification methods available at  penalty , 33, whi penalty  and ridg penalty , was useα and λ. The parameter α interpolates the elastic net penalty between the ridge and the Lasso penalty which corresponds to values of 0 and 1, respectively. The parameter λ determines the amount of shrinkage of the coefficients with larger values inducing more shrinkage until no variables are contained in the model. Regularized logistic and multinomial regression were performed with the R-package glmnet [The elastic net penalty contains two parameters e glmnet .The ABC/GCB classifier was established using logistic regression with an elastic net penalty on the LLMPP CHOP cohort. Of the 181 patients 74 were ABC, 76 were GCB, and 31 were non-classified. Using the 150 patients classified as either ABC or GCB, a dichotomous classifier capable of assigning each sample an estimate of the probability of being ABC was established.α and λ were chosen through 10 fold cross-validation. The parameter α was varied between 0.1 and 1 with step size 0.025 and log(λ) was varied between −10 and 2 with step size 0.06. The optimal combination of the parameters, and thereby the number of probe-sets, was found at the values minimizing the deviance. The results of the cross validations are shown in α = 0.15 and log(λ) = −7.29. This resulted in a gene expression classifier consisting of 381 probe-sets corresponding to 273 Ensembl Gene IDs.To avoid over-fitting and limit the number of noise contributing genes, the elastic net parameters When a tumour sample was classified according to the ABC/GCB classifier using cohort or InLab one-by-one normalized data the associated gene expressions are rescaled probe-set wise by the standard deviation of the LLMPP CHOP data divided by the standard deviation of the cohort or InLab reference data. For ExLab one-by-one normalization the data was used directly, since the training data for the ABC/GCB classifier was the same as the ExLab reference in the current study.hemaClass.org, but is used in the current study for comparison of results from our elastic net classifier.Standard GEP ABC/GCB classification is done using Wright’s naive Bayes classifier. This method is not included on Bayesian compound covariate classification  with proIn the paper by  REGS clapCHO, was estimated based on the probabilities of drug resistance toward each of the three drugs: PC, PH, and PO, respectively. This probability is calculated as the posterior probability of being resistant, given resistance towards each of the individual drugs under the assumption of conditional independence and uniform priors. The formula is also known as Graham’s formula:The probability of resistance to the combination therapy, When a tumour sample is classified according to the REGS classifiers the associated gene expressions are rescaled probe-set wise by the standard deviation of DLBCL14 divided by the standard deviation of the cohort, InLab, or ExLab RMA reference dataset.hemaClass.org, though established elsewhere [Resistance classifiers for other chemotherapeutic drugs and diseases are also available on lsewhere –39. The lsewhere  uses an lsewhere  uses spalsewhere .The BAGS classifier established by  was baseWhen a tumour sample is classified according to the BAGS classifier the associated gene expressions are rescaled probe-set wise by the standard deviation of the Tonsil data divided by the standard deviation of the cohort, InLab, or ExLab one-by-one reference dataset. The rescaling is performed to make the data comparable to the Tonsil dataset.total least square regression lines were compared to assess bias in the estimated probabilities [To evaluate the reproducibility of the class probabilities obtained through cohort or reference based RMA normalization, Pearson’s correlation coefficient for the logit-transformed probabilities and 95% confidence interval (CI) were calculated for each classifier and dataset. The identity and bilities . Total lκ, and corresponding 95% CIs were computed to assess the agreement between the determined classes.For each classifier the associated categories were obtained by thresholding the estimated probabilities. The ABC/GCB classifier was thresholded by 0.1 and 0.9, i.e. a tumour sample was classified as ABC when the estimated probability exceeded 0.9, GCB when it was below 0.1, and unclassified otherwise. For the BAGS classifier a tumour was classified as the class N, CB, CC, M, or PB with the highest probablity, if the associated probability exceeded 0.5 and unclassified when this threshold was not met for any subtype. For the REGS classifiers, C, H, O, and CHO combined, the thresholds were the 33% and 66% percentile of the estimated probabilities. The classifiers were applied to datasets using cohort, InLab, and ExLab one-by-one RMA normalization. Confusion matrices tabulating classifications from cohort normalized data versus InLab or ExLab normalized data were created, and from these the Accuracy (percent with similar classification to cohort), Cohen’s weighted hemaClass package with the desired RMA normalization and the classification methods selected by the user. The usage of the website is largely self-explanatory with context-dependent boxes aiding users with further information, warnings, or errors. A comprehensive tutorial and guide to both the website and package is provided on the website or by running vignette(“howto”) in R. Uploaded patient samples are normalized and classified depending on settings chosen by the user.The website is an easy-to-use, interactive interface for the κ, where misclassifications involving the unclassified group are weighted by 1/2. High agreement between the two classifiers are observed for CHEPRETRO and LLMPP R-CHOP, while the accuracy and Cohen’s weighted κ are lower for IDRC and MDFCI. The accompanying confusion matrices are shown in the first rows of In order to classify patients as ABC/GCB based on the implemented one-by-one normalization method a classifier based on the regularised logistic regression was established. The classifications were evaluated in the four clinical cohorts CHEPRETRO, MDFCI, IDRC, and LLMPP R-CHOP, which have all been classified according to Wright’s naive Bayes classifier , 9, 36. The logit probability of being ABC estimated using the established cohort-based classifier was compared to the corresponding estimate obtained through the ExLab one-by-one normalized classification scheme in κ for cohort, using either Wright’s Bayes classifier or the elastic net classifier, against InLab or ExLab RMA reference normalization. For ExLab normalization CHEPRETRO and LLMPP R-CHOP both show a high Cohen’s weighted κ and accuracy considering that misclassifications involving unclassified samples count as errors, while values are moderate for MDFCI and IDRC when comparing to cohort based classifications for both the elastic net and Wright’s classifier. The reduced rate of agreement and Cohen’s weighted κ using ExLab one-by-one normalization in IDRC may be due to the samples being FFPE although this seems to be remedied by InLab one-by-one normalization. Accuracy and Cohen’s weighted κ are very high when comparing InLab based classifications to cohort based classifications for the elastic net classifier, but values are still moderate for MDFCI when comparing against Wright classifications. The accompanying confusion matrices for the elastic net classifier are shown in the lower part of For both methods, patients are classified as ABC when the estimated probability exceeds 0.9 and GCB when it is below 0.1. In hemaClass.org for both InLab and ExLab one-by-one normalization. The logit probabilities of sensitivity are plotted against those obtained by cohort based normalization in The probability of sensitivity towards each of the three drugs C, H, and O was estimated using hemaClass.org are compared to those obtained by the cohort based approach in κ. Low to moderate rates of agreement are observed for the ExLab normalized data, and again the InLab one-by-one approach yielded higher agreement with classifications obtained from cohort based normalization. The associated confusion matrices for InLab and ExLab one-by-one normalization are shown in Based on the estimated probabilities, the patients were categorised as sensitive, intermediate, or resistant based on the thresholds specified in Section. The classes obtained by The BAGS classifier was evaluated in a manner similar to the ABC/GCB classifier. The logit probability of a patient’s tumour originating from one of the five subpopulations was estimated by means of cohort, InLab, and ExLab one-by-one normalization. The logit probabilities estimated by the ExLab normalization are plotted against logit probabilities from cohort based normalization in κ. For the BAGS classifier low rates of agreement were also found when comparing results from ExLab normalization with cohort normalization, while InLab normalization again led to improved agreement. The associated confusion matrices are shown in Based on the probabilities estimated by means of each of the three normalization methods the patients of the four clinical cohorts are grouped into the BAGS. The rates of agreement between the cohort based, and InLab or ExLab based classifications are shown in Despite the enormous amount of resources spent on developing molecular based cancer classification systems, most of these are still not available in daily clinical practice. To allow for fast validation of our recent findings , 10, we hemaClass.org were then compared to those obtained using cohort based normalization in four clinical cohorts. The results showed that a one-by-one array analysis approach is feasible and performs comparably with the whole cohort based method when an InLab reference is used, while differences between ExLab and cohort normalized classifications were too large to be satisfactory. Users are thus encouraged to supply their own reference for RMA pre-processing. It seems that this approach allows for a realistic application of microarray based lymphoma classification for research projects, and after suitable standardisation and calibration, even for clinical use.One-by-one normalization was done using both an external RMA reference (ExLab) and a mimicked laboratory specific reference (InLab). Classifications obtained through one-by-one pre-processing performed by hemaClass.org calculates the inter-quartile range (IQR) of the relative log expression (RLE) [The poorer results for ExLab one-by-one normalization and classification is likely caused by not accounting for lab-specific batch effects in the normalization process. To check for incorrect normalization on (RLE)  across pκB pathway by proteasome inhibitors (i.e. bortezomib). Interestingly, the constitutive activation of the NF-κB pathway is characteristic for the ABC subtype of DLBCL which consequently enhances the effect of bortezomib in this subtype [The present treatment algorithms for DLBCL are based on disease stage and clinical risk stratification without accounting for the underlying tumour-biology  and does subtype .hemaClass.org provides an example of fast processing of complex molecular information in a way that is simple and readily at hand for clinicians.With the increasing number of new drugs likely to become available over the next years and the fact that their efficacy may vary between subsets of patients defined by gene expression profiles, the current treatment of patients based on disease stage and clinical information alone will not be sufficient. From a practical perspective a couple of challenges have to be addressed. First, the prognostic potential of the classifiers in these types of analyses has been established on specific tissues, which means one has to trust that the right tissue has been extracted and handled correctly through all the steps in the laboratory ending up with a reference array data set of sufficient quality. Our reference data have for instance been controlled by looking at the frequency of ABC/GCBs and BAGS classes and their survival curves as well as tissue control by experienced pathologists. Second, one has to address the need for a reference dataset for one-by-one RMA pre-processing established under similar conditions as the samples one wishes to classify. However, calibration of laboratory equipment is a well-known issue for many experimental techniques used in molecular biology like qPCR, mass spectrometry, immunohistochemistry, and flow cytometry. An important part of the calibration is that samples should be calibrated towards a dataset consisting of a representative set of tissue samples. We suggest that these challenges could be addressed by establishing a central tissue bank with officially approved data, e.g. by an international medical consortium, for the specific disease, similar to The Gene Expression Barcode  where cohemaClass.org, but had the training data for the classifiers been normalized with fRMA this might eliminate the need for centering and scaling sample data.Alternatively the frozen RMA (fRMA) approach suggested by  could behemaClass.org only works with Affymetrix HG-U133 Plus 2.0 microarrays. This can, however, be circumvented by either re-annotation to HUGO Gene Nomenclature Committee (HGNC) approved symbols as suggested by [Another limitation of the current web application is that ested by  or by reested by . At the Traditionally ABC/GCB classification has been achieved using the naive Bayes classifier of  which isκ the conservative approach is retained and the issue is addressed using a Cohen’s weighted κ approach. Given that an idealised approach is problematic to formulate, readers are encouraged to consider the confusion matrices in the supplementary material to make an overall evaluation of the performance.Under the validation of the one-by-one method one should notice that the unclassified is treated as a class in its own right. This implies a lower accuracy compared to an approach where the unclassified are left out of the validations. The latter approach seems reasonable as changing classifications to unclassified is less serious than changing real classes. Despite the disputed properties of Cohen’s ABC/GCB, BAGS, and REGS are only a part of the GEP-based armamentarium of methods for stratifying lymphoma patients into risk groups –52 and ihemaClass.org is still separated from the clinic we believe that a web based tool and suggestion for a clinical reference sample will bring cancer classification closer to the clinic. Hopefully, this work can also spawn interesting discussions on the clinical requirements of GEP based diagnostic and prognostic tools.Although high throughput technologies in molecular biology have been around for almost two decades, only a few of the numerous biomarkers identified have undergone extensive validation and made it into the clinic . It is ohttps://github.com/oncoclass/hemaclass-paper. Comments, suggestions, bug reports, and other issues are warmly welcome at https://github.com/oncoclass/hemaclass/issues or by mail to the corresponding author.All material for reproducing this paper and its results is found at S1 Text(PDF)Click here for additional data file.S1 TableThe columns represent cohort based normalisztion using the ABC/GCB classifier based on elastic net. The first part of the table compares Wright’s method for ABC/GCB classification with the elastic net based. In the second and third part ExLab and InLab reference based normalization is compared to cohort based normalization using the ABC/GCB classifier based on elastic net.(PDF)Click here for additional data file.S2 TableExLab and InLab reference based normalization are shown in the columns and cohort normalization in the rows.(PDF)Click here for additional data file.S3 TableExLab normalization is shown in the rows and cohort normalization in the columns.(PDF)Click here for additional data file.S4 TableInLab normalization is shown in the rows and cohort normalization in the columns. Note, 30 samples were used as reference data and hence not present in this table.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S1 Figα and regularization parameter λ, respectively. In Panel C the regularization curves are shown. Black and grey curves represent selected and non-selected probe-sets, respectively. Positive and negative coefficients indicate that high expression values for the associated gene are related to ABC and GCB, respectively. The red line indicates the model chosen through 10 fold cross validation. The gene symbols for the 20 probe-sets associated with the largest absolute coefficients in the chosen gene expression predictors are displayed in Panel C.In panels A and B the deviance is plotted against the model parameter (TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 Fig(TIFF)Click here for additional data file.S6 Fig(TIFF)Click here for additional data file.S7 Fig(TIFF)Click here for additional data file.S8 Fig(TIFF)Click here for additional data file.S9 Fig(TIFF)Click here for additional data file.S10 Fig(TIFF)Click here for additional data file.S11 Fig(TIFF)Click here for additional data file.S12 Fig(TIFF)Click here for additional data file.


Diagnosis of Alzheimer’s disease (AD) is very important, and MRI is an effective imaging mode of Alzheimer’s disease. There are many existing studies on the diagnosis of Alzheimer’s disease based on MRI data. However, there are no studies on the transfer learning between different datasets (including different subjects), thereby improving the sample size of target dataset indirectly.Therefore, a new framework method is proposed in this paper to solve this problem. First, gravity transfer is used to transfer the source domain data closer to the target data set. Secondly, the best deviation between the transferred source domain samples and the target domain samples is searched by instance transfer learning algorithm (ITL) based on wrapper mode, thereby obtaining optimal transferred domain samples. Finally, the optimal transferred domain samples and the target domain training samples are combined for classification. If the source data and the target data have different features, a feature growing algorithm is proposed to solve this problem.The experimental results show that the proposed method is effective regardless of different kernel functions, different number of samples and different parameters. Besides, the transferred source domain samples by ITL algorithm can enlarge the target domain training samples and assist to improve the classification accuracy significantly.Therefore, the study can enlarge the samples of AD by instance transfer learning, thereby being helpful for the small sample problems of AD. Since the proposed algorithm is a framework algorithm, the study is heuristics to the relevant researchers. Alzheimer’s disease is seriously hazardous and the diagnosis is important. MRI is an important neuroimaging, so based on MRI data to achieve AD diagnosis is an effective way –3. At prThese studies have shown that machine-learning method is effective for AD classification, but most of them are based on the public data set, not for the specific people. In fact, for different regions –22, ethnHowever, because AD is concealed, slow, non-lethal, sample collection is very difficult, and the number of samples is often less , 25–28. In fact, there are some public datasets available . Although the subjects within the datasets are different from those in the target dataset, all of them are about AD, so they are correlative. The information within the dataset are helpful for pre-training of the classifier to replace the random initialization of the classifier.Therefore, how to use these public data sets to effectively improve the accuracy of classification of target subjects is a key problem. Recent studies about machine learning show that transfer learning can help to solve this problem. Transfer learning has the advantage of transferring the well-learnt knowledge from the related work to facilitate an improved learning result of one task . It has The relevant studies above show the effectiveness of the transfer learning for classification of AD. However, these papers do not study how to use other related data sets to improve the classification accuracy of the target data set based on transfer learning , 34–39. Based on the analysis above, a method based on transfer learning is proposed to meet the requirements. First, gravity transfer is used to make a rough migration of the source domain samples to the target domain samples. Secondly, the best deviation between the source domain samples and the target domain samples is searched based on wrapper mode and the optimal deviation is obtained between the source and the target domain. Finally, the transferred source domain samples plus optimal deviation are used for classification by combining with the target training samples.http://adni.loni.usc.edu/), and the other data set is the one from us . The local data is the target of classification of AD, so it is called target data ; the ADNI database include more samples of AD, but the subjects are different from those of the local data.In order to verify the validity of the algorithm, two related but different data sets are used to verify the algorithm. One of the data sets is a publicly accessible ADNI database  and 30 texture features. The dataset consists of structural T1 MR images of 90 subjects. Because the number of the effective AD samples is small, the number of samples for each class is 30 for balance. These images were acquired by GE 1.5T Signa scanner at the Southwest Hospital, China. The spoiled gradient-recalled (SPGR) volumetric T1-weighted pulse sequence was used with the following parameters, optimized for maximal contrast among gray matter, white matter, and CSF: TE = 5 ms, TR = 25 ms, flip angle = 40, NEX = 1, slice thickness = 1.5 mm/0 mm interslice. The individuals for the study were selected by neuroradiologists. No participant has a neurological disease and all have similar educational level.https://pan.baidu.com/s/1dmsUfk). It is convenient for readers and editors to view them.The experimental data have been uploaded to the public cloud disk of our laboratory  features, thereby transferring the source data to those close to the target data. The source data after transformation have the same features with the target data. After that, ensemble learning is conducted for improving classification accuracy.In order to combine two datasets, the proposed method was named as Instance Transfer Learning (ITL) which can effectively transfer the source data to the target data. First, gravity transfer is used to make a rough migration of the source domain samples to the target domain samples. Then the best deviation between the source domain samples and the target domain samples is searched based on wrapper mode and the best deviation between the source and the target domain is obtained. The wrapper mode here means the evaluation criterion for searching the optimal deviation, which is the classification accuracy of validation set. If the features of the source data and the target data are same, ITL algorithm can be used to conduct instance transfer learning. However, if the features of the source data and the target data are not same, ITL algorithm is not enough. The common features between the source data and the target data are chosen. Based on the features, ITL algorithm is used to conduct instance transfer learning. Suppose the number of features of target data is In the paper, the ADNI data is the source domain (SD) data, the local data is the target domain (TD) data. The target data set is divided into training sets and test sets, denoted as TD_train and TD_test. One part of training set is for training the classifier; another part of training set is for validating and searching deviation.The number and features information of the data set sample is shown in Table ylabel is the label of samples.Due to the small number of deviation candidates, an exhaustive approach is used to find the optimum deviation for the candidates. The fitness function of the deviation is the maximization of classification accuracy. The fitness function can be described as follows.Assuming the number of the candidate deviations is N. The main procedures are shown in the Fig. i). The other part is used to validate the model and obtain the fitness value of the deviation candidate. The cross-validation algorithm used here is to leave one-out cross-validation method. In the figure, SD_GraTrans_Opt is obtained by SD_GraTrans plus the output optimum deviation. SD_GraTrans_Opt&TD_train is obtained by combining SD_GraTrans_Opt and TD_train.In Fig. The pseudo code of ITL algorithm is shown as follows.Xs and Xt, n is the number of features.In the instance transfer learning process based on ITL, only some features (left and right hippocampus) are same between the source data and target data , and the texture features are not used. The texture features are needed to be added to the source dataset. According to similarity principle, the Euclidean distance are used as a similarity criterion between the source samples and the target samples. The fitness function of the Euclidean distance is as follows.Fi means the fitness value of the ith sample of source data (ADNI data), Distance means the distance between Xsi and Xtj, N is the number of the target data , Xsi means the ith sample of source domain, Xtj means the jth sample of target domain.In this paper, the Euclidean distance criterion is used to match the texture features from the target domain to the source domain samples. The fitness function F is defined as:Here, the sample selection algorithm is used, where the confidence criterion is used to select the source samples. TD_train means the training set in the target data. According to the Euclidean distance criterion, the confidence criterion calculates the Euclidean distance between each sample in the source domain and all the TD_train samples. The N samples closest to the source domain are found from the TD_train, and the closest sample’s label is used as the label of the corresponding sample in the source domain data. Then, the label of the sample is compared with the labels of other N − 1 samples chosen from TD_train, and the number of the samples with the same label is counted. The higher the number is, the higher the confidence is.In the part, assuming that the number of SD_GraTrans_Opt is N_Opt, the number of target data train sets is N_TD_train. The main procedure is shown in Fig. Based on the principle of the ITL, ensemble learning is used to enhance the stability of the classification model. The flow chart is shown in Fig. When the FGA algorithm is not used, SD_GraTrans_Opt is used as input for Ensemble learning. When the FGA algorithm is used, SD_GraTrans_Opt_SamSel is used as input for Ensemble learning. As can be seen from the figure, an optimal deviation can generate three classifiers, and the classifiers obtained from the deviations are combined to get the ensemble classifier. Finally, the ensemble classifier is tested with the TD_test and the final prediction results are obtained.In the paper, the experimental operating system platform was the Windows, version 7, 64-bit operating system, and the memory size was 128G. The data processing was completed in MATLAB, version 2014a. In this paper, leave-one-out are used as cross-validation method. In the classification process, the classifier is support vector machine (SVM). The kernel functions of SVM is linear kernel and RBF kernel.Several groups of experiments are organized to verify the performance of the proposed method in this paper. In the 1st group of experiments, under the condition where the features are same between source data and target data, the performance of the ITL + ELA algorithm is shown and compared. In the 2nd group of experiments, under the condition where the features are not same between source data and target data, the performance of the ITL + FGA + ELA algorithm is shown and compared.In this section, support vector machine (SVM) is used as classifier. Different parameters of SVM are involved including different kernel functions and different kernel function parameters. The experimental results are recorded in Table It can be seen from Table In this section, the effect of different samples of the TD is studied. The method of leave-one-out (LOO) is used for cross validation, and the final classification accuracy is calculated. Each experiment is repeated ten times, and the results obtained as follows.From the table, it can be seen that regardless of the sample size the proposed algorithm (SD_GraTrans_Opt+TD_train) achieves the highest classification accuracy under different kernel functions. For example, in the case of linear kernel functions when the number of samples is 60, the classification accuracy of the target domain is 83.33%. Its classification accuracy is higher than TD_train (76.67%) and SD_GraTrans+TD_train (76.67%). Besides, with the number decreases, the classification accuracies of TD gradually decreases. The result means that more training samples will be helpful for classification. However, it is very hard to collect large number of samples, especially for AD. As for the proposed method (SD_GraTrans_Opt+TD_train), the classification accuracy is not affected by the number of the samples of TD. Therefore, it is feasible to make use of relevant large-scale dataset for improving the accuracy of local dataset.Figure It can be seen that from the figure that the proposed algorithm has the best classification accuracy compared with other algorithms no matter the number of the samples in the TD.Table As can be seen from the table, under the same conditions, the proposed method has a lower false positive than the other methods. In most cases, the false detection rate is also lower than other methods. Therefore, the effectiveness of the proposed algorithm is validated. In addition, compared with the result of simple migration (SD_GraTrans+TD_train), SD_GraTrans cannot be directly used to supplement TD data.As described above, when the features between source data (SD) and target data (TD) are not same, the ITL is not enough. The solution is as follows: first, the SD is transformed to TD by ITL based on the common features. Secondly, transformed SD is transformed to TD by FGA, to enlarge the features that are the same as those of TD. In this section, different conditions are considered, including different number of samples of TD, different kernel types, and different sub-classifiers.The number of sample selections for source data is 411 . As described above, the optimal deviations are not unique. So, different numbers of optimal deviations are considered here, and the best number is 25. Each experiment is repeated ten times, the results can be found in the Tables Seen from Table Figure From Fig. Table As seen from the table, under the same conditions, the proposed method has a lower false positive than the other methods. In most cases, the false detection rate is also lower than other methods. Therefore, the data set after feature growth still has a good effect, which indirectly verifies the effectiveness of the proposed algorithm.As described above, the deviation is possibly not unique. Therefore, the number of the sub-classifiers are explored here. The number of samples are 60. Every deviation generates a sub-classifier, and all the sub-classifiers form an ensemble classifier. Each experiment is repeated ten times, the results obtained as follows.As can be seen from Table As described above, the proposed FGA algorithm can expand the features based on transferred samples and target samples. The figures and tables above show its effectiveness.Figure AD is a serious social problem. For different areas, ethnicity, etc., the characteristics of AD are different. Therefore, the diagnosis of Alzheimer’s disease aiming at local people is very necessary. On the other hand, due to concealed, slow, non-lethal and other characteristics of AD, the sample collection is very difficult, so the number of samples is often small and dispersive. According to the principle of machine learning, small number of samples is likely to lead to inadequate training and over-fitting. Therefore, there is a big problem about how to obtain high efficiency based on small samples. In fact, there exists some public databases containing large relevant samples. Therefore, it is very necessary to study how to make use of the public data to improve the classification accuracy of target data.In this paper, instance transfer learning (ITL) algorithm was proposed to solve this problem. The samples in SD data can be transformed to target data based on the deviations and generated transferred SD data by ITL algorithm. The transferred SD data can improve the classification accuracy by combining the training set of the target data. Here, ensemble-learning algorithm (ELA) is involved. When the features between the SD data and target data are same, the ITL + ELA can deal with the process. When the features between the SD data and target data are different, the common features between the SD data and target data are dealt with the ITL algorithm; the other features are generated by the FGA algorithm; classification is conducted by ELA algorithm.The experimental results are positive. Regardless of the number of TD samples and the number of sub-classifier and kernel types, the ITL algorithm in this paper have achieved better results than those by using local data alone. In some cases [such as Table Although there are literatures related to the use of transfer learning for AD diagnosis, there is no research about how to use other datasets  to improve the classification accuracy of the target dataset. Therefore, they are completely irrelevant to this paper. In fact, the sample size is a key bottleneck problem, regardless of single mode or multimodal, traditional machine learning or deep learning, shape features or texture features or brain network characteristics. The current relevant literatures always are based on some specific target dataset (public or collected by self), but usually the sample size is small, especially for the datasets collected by those authors. However, according to theory of statistical learning, small sample size always leads to insufficient training of classifier and overfitting. Therefore, it is necessary to study the effective transfer learning to make full use of these related data sets to improve the classification accuracy of the target data set. This is also the main motivation and value of this paper. Besides, current relevant transfer learning algorithms focus on transfer the parameters of the pre-trained model rather than the source domain samples themselves, so they cannot obtain transferred samples and expand the target samples. As we known, for the small sample problems, people yearn for obtaining more samples for subsequent statistical analysis, and so on. The method in this manuscript can solve this problem to some extent.This paper proposed an instance transfer learning algorithm for classification of Alzheimer’s disease.The instance transfer learning algorithm can deal with the situation when the features between source data and target data are different.The instance transfer learning algorithm can transfer the source samples to target samples, and obtain the transferred source samples, thereby enlarging the target samples.Representative public dataset—ADNI is used for verifying the instance transfer learning.Although this paper involves one source dataset, this method can easily be generalized to multiple source datasets.The main contributions and innovations of this paper can be stated as follows:Currently, most of the diagnoses of Alzheimer’s disease just made use of the target dataset, and did not consider to make use of the other relevant dataset  to improve the classification accuracy. The main contributions of this paper are: (1) A method based on Instance Transfer learning (ITL) is proposed in this paper. The method can transfer the source dataset to target dataset, thereby improving the sample size of the target dataset, different from other relevant transfer learning algorithms. (2) For the relevant datasets with different features, a feature growing algorithm is proposed and can effectively expand the samples of the target domain. (3) The experimental results show that the classification accuracy can be improved apparently. In some cases, this improvement even exceeds 10%. The idea and method of this paper can provide a solution for other studies about small sample problems. Besides, the proposed method is not restricted to specific classifier, or feature learning method, so they are heuristic to relevant researchers.


Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size.Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate.These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.The online version of this article (doi:10.1186/s40168-017-0237-y) contains supplementary material, which is available to authorized users. Although data produced by high-throughput sequencing has been proven extremely useful for understanding microbial communities, the interpretation of these data is complicated by several statistical challenges. Following initial quality control steps to account for errors in the sequencing process, microbial community sequencing data is typically organized into large matrices where the columns represent samples, and the rows contain observed counts of clustered sequences commonly known as operational taxonomic units, or OTUs, that represent bacteria types. These tables are often referred to as OTU tables. Several features of OTU tables can cause erroneous results in downstream analyses if unaddressed. First, the microbial community in each biological sample may be represented by very different numbers of sequences , reflecting differential efficiency of the sequencing process rather than true biological variation. This problem is exacerbated by the observation that the full range of species is rarely saturated, so that more bacterial species are observed with more sequencing . ThuIn an attempt to mitigate some of these three challenges and aid in data interpretation, data are often normalized by various computational processes prior to downstream analysis. Normalization is the process of transforming the data in order to enable accurate comparison of statistics from different measurements by eliminating artifactual biases in the original measurements. For example, in microbiome data, biases that reflect no true difference in underlying biology can exist due to variations in sample collection, library preparation, and/or sequencing and can manifest as, e.g., uneven sampling depth and sparsity. After effective normalization, data from different samples can then be compared to each other. Ordination analysis, such as principal coordinate analysis (PCoA) , is ofteThis paper therefore examines how various normalization and differential abundance testing procedures available in the literature are affected by the challenges inherent in microbiome data. Recent work in this area  addresseBecause normalization is intended to enable meaningful comparison of data from different measurements, it is critical to the validity of all downstream analyses. Microbial ecologists in the era of high-throughput sequencing have commonly normalized their OTU matrices by rarefying or drawing without replacement from each sample such that all samples have the same number of total counts. This process, in effect, standardizes the library size across samples, mitigating the first challenge discussed above. Samples with total counts below the defined threshold are excluded, sometimes leading researchers to face difficult trade-offs between sampling depth and the number of samples evaluated. To ensure an informative total sum is chosen, rarefaction curves can be constructed . These cAnother common normalization method besides rarefying is scaling. Scaling refers to multiplying the matrix counts by fixed values or proportions, i.e., scale factors, and specific effects of scaling methods depend on the scaling factors chosen and how they are applied. Often, a particular quantile of the data is used for normalization, but choosing the most effective quantile is difficult , 27–30. A further alternative is Aitchison’s log-ratio transformation , which iFor OTU differential abundance testing between groups , a common approach is to first rarify the count matrix to a fixed depth and then apply a nonparametric test . Nonparametric tests are often preferred because OTU counts are not exactly normally distributed . HoweverThese parametric models are composed of a generalized linear model (GLM) that assumes a distribution , and theProper normalization may potentially remove biases and variations introduced by the sampling and sequencing process, so that the normalized data reflects the underlying biology. One way to evaluate whether normalization has effectively removed such biases is to examine whether the groupings of normalized data after ordination analysis reflect biological or artifactual features. For example, Fig. To assess the seven proposed normalization methods shown in Table M values (TMM) make the assumptions that most microbes are not differentially abundant, and of those that are, there is an approximately balanced amount of increased/decreased abundance [For unweighted metrics that are based on species presence and absence, like binary Jaccard and unweighted UniFrac, the variance-stabilizing transformation performed by DESeq clusters many fewer samples according to biological origin than other techniques. This is because, as done in McMurdie and Holmes ), the nebundance ; these aThe simulations of Fig. We next applied the normalization techniques to several datasets from the literature to assess performance in light of the additional complexity inherent to real-world data. To perform an initial, detailed comparison of normalization methods, we selected the data set from Gevers et al. . The datR2 or sum of squares’ group/total sum of squares)  Fig. . In the ata Fig. . DESeq aPCoA plots using ecologically common metrics for all of the normalization techniques on a few key real datasets representing a gradient , distincThus, both simulations and real data suggest that rarefying remains a useful technique for sample normalization prior to ordination and clustering for presence/absence distance metrics that have historically been very useful  taxon abundance between two groups . Some statistical methods developed specifically for RNA-Seq data, such as DESeq , DESeq2 Previous work in this area concluded that the newer differential abundance testing methods are worthwhile, and that the traditional practice of rarefying causes a high rate of false discoveries . HoweverWe recognize that the multinomial and the Dirichlet-multinomial (DM) distributions are not necessarily appropriate for the microbial taxon counts because under such probability distributions, every pair of taxa are negatively correlated, whereas as discussed in Mosimann  and MandOne of the objectives of a microbiome study is to compare the abundance of taxa in the ecosystem of two or more groups using the observed taxa abundance in specimens drawn from the ecosystem. As noted in Mandal et al. 2015)  and in t  and in t test on proportion-normalized data outperforms the nonparametric Wilcoxon rank-sum test in Fig. We also investigated the performance of the techniques on real null data, in which there should be no “true positives,” since samples from the same biological group were randomly divided into two groups “” sectionWhile the no normalization or proportion approaches control the FDR in cases where the average library size is approximately the same between the two groups Figs.  and 5, tUsing real data, we further tested the techniques shown to be most promising in the simulations: DESeq2 , metagenR2 for the studied biological effect, especially for small (<1000 sequences per sample) and very uneven (>~10× on average) library sizes between groups. The approaches of no normalization and sample proportion are prone to generation of artifactual clusters based on sequencing depth in beta diversity analysis. Therefore, researchers should proceed with caution and check for these effects in ordination results if the count data was not rarefied. In PERMANOVA tests, we recommend that a term for library size is included if the data set was not rarefied or otherwise normalized.We confirm that recently developed more complex techniques for normalization and differential abundance testing hold potential. Of methods for normalizing microbial data for ordination analysis, we found that DESeq normalization , 42, whiFor differential abundance testing, we used both simulations and real data. Overall, we found that simulation results are very dependent upon simulation design and distribution, highlighting the need for gold standard datasets. We confirm that techniques based on GLMs with the negative binomial or log-ratios are promising. DESeq2  was desiThanks to McMurdie and Holmes’ previous work in this area , we recoThe basic test of how well broad differences in microbial sample composition are detected, as assessed by clustering analysis, was conducted in “simulation A” from McMurdie and Holmes . BrieflyMcMurdie and Holmes  evaluatehttps://qiita.ucsd.edu/).On the real datasets, nonparametric multivariate ANOVA (PERMANOVA)  was calcThe simulation test for how well truly differentially abundant OTUs are recognized by various parametric and nonparametric tests was conducted as in “simulation B” in McMurdie and Holmes , with a A simple overview of the two methods used for simulating differential abundance is presented in Additional file However, in general, as noted in Additional file t test to the nonparametric Mann-Whitney test, which is widely used and more appropriate because the OTU distributions in microbiome data usually deviate from normality. The techniques used  of Caporaso et al. . We thenW that represents the number of nulls among the m-1 tests that are rejected. ANCOM determines the final significance of a taxon by using the empirical distribution of W. To deal with zero counts, we use an arbitrary pseudo count value of 0.001. For a more detailed description of ANCOM, we refer the reader to Mandal et al. [The ANCOM procedure compares the relative abundance of a taxon between two ecosystems by computing Aitchison’s  log-ratil et al. .