LOC441178, C5orf66-AS1, HCG22, FLG-AS1, CCL14/CCL15-CCL14, LOC100506990, TRIP10, PCBP1-AS1, LINC01315, LINC00478, COX10-AS1/LOC100506974, MLLT4-AS1, MIR31HG, and DUXAP10/LINC01296. Three lncRNAs in the validated list which showed the highest fold change  were verified by quantitative RT-PCR in a subset of 20 OSCCs and 10 control samples. In silico prediction of their functional role has given us directions for further investigation.Oral cavity and oropharyngeal squamous cell carcinoma (OSCC) is a major cancer type in the head and neck region. To better understand the roles long non-coding RNA (lncRNA) play in OSCC carcinogenesis, we compared the expression levels of 3,054 probe sets for lncRNAs between 167 OSCCs and 45 healthy oral mucosa using an Affymetrix HG U133 plus 2.0 array dataset. We found 658 lncRNA transcripts (790 probe sets) to be significantly differentially expressed using a criteria of FDR < 0.01, with 36 of them (39 probe sets) showing more than a 2-fold change. We further validated the top differentially expressed lncRNAs in three independent datasets from Gene Expression Omnibus (GEO) repository: GSE42743, GSE9844, and GSE6791. Fourteen lncRNAs (15 probe sets) were validated in all three datasets using the criteria FDR < 0.01:  About 600,000 new cases arise annually worldwide [Oral cavity and oropharyngeal squamous cell carcinoma (OSCC) is the eighth most common cancer among men and fourteenth among women in the U.S. according to recent data . The estorldwide . Tobaccoorldwide , 4–6. Hoorldwide . There cIt is estimated that >70% of the human genome can be transcribed. However, only 2% are protein-coding and the majority of transcripts are not . These mHOTAIR or Xist influences chromatin remodeling by recruiting PRC2 [MALAT1 and NEAT1 serve as molecular scaffolds for proteins within nuclear speckles and paraspeckles [UCA1 and MEG3 regulate oncogenes by “sponging” miRNAs and decreasing their function [The functions of only a handful of lncRNAs have been studied with results showing that they play a role as transcriptional and post-transcriptional regulators. Their reported functions include: 1) Remodeling chromatin state (e.g. ing PRC2 –13); 2) speckles , 14); 3)function , 16).HOTAIR (HOX transcript antisense RNA) was reported to be up-regulated in OSCC [HOTAIR expression and function [MALAT1 (metastasis associated lung adenocarcinoma transcript 1) is another reported lncRNA. This large, infrequently spliced non-coding RNA was aberrantly expressed in lung cancer and cervical cancer, and its upregulation is associated with growth and metastasis of several types of cancer, such as colorectal, pancreatic and gastric cancers [MEG3, which was down-regulated in tongue cancer [UCA1, which was up-regulated in tongue cancer [AFAP1-AS1, AB209630, GAS5, and HOTAIR, could potentially serve as predictors of patient outcome or treatment response.Gibb et al. was the first to report the expression of lncRNA in oral mucosa, implying lncRNAs contribute to the oral transcriptome . Subsequ in OSCC , 19 and  in OSCC , 21. An  in OSCC . In 2015function . Whether cancers , 24 and  cancers , 25. Othe cancer , and UCAe cancer . Some ste cancer –30 suggeTo better understand the role of lncRNAs in OSCC carcinogenesis, and to gain insight for the identification of potentially clinically relevant targets, we used a whole genome approach to examine expression differences of lncRNAs between OSCC and normal oral tissue. Previous studies by Risueño et al. , Liao etLOC441178 with both showing a greater than 14-fold down-regulation in OSCC compared to control oral mucosa. To validate our findings in external datasets, we analyzed the expression of the top 39 probe sets that were identified in our study in three external datasets downloaded from GEO, adjusting for age and sex. Study participants’ characteristics for our study and for these three validation sets are summarized in Table LOC441178, C5orf66-AS1, HCG22, FLG-AS1, CCL14/CCL15-CCL14, LOC100506990, TRIP10, PCBP1-AS1, LINC01315, LINC00478, COX10-AS1/LOC100506974, MLLT4-AS1, MIR31HG, and DUXAP10/LINC01296. The results on their differential expressions are shown in Table To identify differentially expressed lncRNAs between OSCC and control, we used a dataset comprised of 167 OSCCs and 45 oral mucosa samples from healthy controls previously generated using Affymetrix Human Genome U133 Plus 2.0 array . Out of 3,054 candidate probe sets that represent 2,172 lncRNA transcripts (see Supplementary Table 1), we found 790 probe sets (representing 658 lncRNAs transcripts) to be significantly differentially expressed with the criteria FDR < 0.01(see Supplementary Table 2). Of them, 568 were down-regulated and 222 were up-regulated in cancer. Amongst all, 39 probe sets (36 lncRNA transcripts) have a fold change difference > 2, with the majority (31 out of 39) of them being down-regulated. The two most differentially expressed probe sets identified the same lncRNA LOC441178, HCG22 and C5orf66-AS1) in the validated list showing the highest fold changes were chosen to undergo qRT-PCR testing in a subset of 20 OSCCs and 10 normal controls samples (see Supplementary Table 3). The samples were randomly chosen from the original FHCRC study samples which consist 167 OSCCs and 45 oral mucosa from healthy people. LOC441178 has two identified transcripts, one shorter transcript that codes for a 93 aa protein, and the other longer transcript is identified as a lncRNA. To confirm that the lncRNA transcript is truly differentially expressed, we designed a PCR primer pair that targets only the lncRNA transcript. Figure P < 0.05). The correlation of Affymetrix array data and PCR results were calculated. Pearson's correlation coefficient value of LOC441178, HCG22 and C5orf66-AS1 were 0.88, 0.83 and 0.72, respectively .To confirm the differential expression signals found in the array data, three lncRNAs . C5orf66-AS1, also known as Epist, was previously found to be downregulated and may act as a tumor suppressor in esophageal squamous cell carcinoma [C5orf66-AS1 may be involved in multiple cancer types that occur in the upper aerodigestive tract. Within 5kb distance of C5orf66-AS1 is pituitary homeobox 1 (PITX1). PITX1 expression level was found recently to be a novel predictor for treatment response of head and neck cancer [C5orf66-AS1 could be a regulator of this neighboring PITX1 gene. MIR31 host gene (MIR31HG), also named lncRNA LOC554202, was found to be down-regulated in gastric cancer [MIR31HG induced a tumor suppressor p16INK4A dependent senescence phenotype, which might link it to HPV-related cancer carcinogenesis [MIR31HG expression does not appear to be significantly different between HPV-positive OPC  and HPV-negative OPC . However, we found it was significantly differentially expressed between subsites of OSCC , P = 9.29E-07, lower in OPC), and between smokers and non-smokers . LINC01296 was reported to be dysregulated in colorectal cancer and was suspected to be a potential prognostic biomarker [LINC01296 in our study was also significantly different between OSCC and controls, suggesting that this lncRNA may be involved in carcinogenesis process.In this study, we identified 790 significantly differentially expressed probe sets, representing 658 lncRNAs, between OSCCs and normal oral mucosa, with 39 of them, representing 36 lncRNAs, showing more than a 2-fold change in their expression levels. We also used three independent datasets to validate our findings, and 14 lncRNAs were found to be significantly differentially expressed in all data sets. We suspect these differentially expressed lncRNAs in OSCC may be involved in oral cancer carcinogenesis, and they also could serve as new choices for the investigation of potential OSCC biomarkers and/or therapeutic targets. Some of our findings are novel in that of the 14 validated lncRNAs, more than a half (8 out of 14) have never been reported to be associated with cancer before: database  showed tC16/CA125, and nonCGA data . While oarcinoma . It has c cancer  and bladc cancer , but up-c cancer . Furtherogenesis . In our iomarker . The expHOTAIR, UCA1, and MALAT1, were found to be differentially expressed in our study, but none was in our top, validated list. Interestingly, some lncRNAs showed opposite expression direction in our study than in previous studies focusing on individual lncRNAs in head and neck cancers. Supplementary Table 5 shows their deregulation status in other studies and in our data. The discrepancies perhaps could be related to differences in sample size, platform used, origin of the control tissue used, or tumor site.Some previously reported cancer-associated lncRNAs, such as LOC645949, LINC00896, FAM99B, LINC00167, LINC01056, SND1-IT1, NCBP2-AS2). The reasons of so few overlaps are unknown and could be related to different version of the Affymetrix platform used and the difference in data processing. Zhang et al. excluded probe sets that map to multiple transcripts in their analysis; while we did not. Excluding probe sets that also identify coding transcripts may mean a loss of potential lncRNA of interest. In our study, we chose to keep those probe sets in the initial analysis, because in some cases, it is possible to determine lncRNA transcript expression status more definitively through other methodologies such as quantitative RT-PCR. This approach allowed us to identify our most differentially expressed lncRNA LOC441178.There are a large number of lncRNAs with unknown function that have yet to be studied. While genome wide study of lncRNA is a good approach to unveil more lncRNAs that may be involved in carcinogenesis or may represent potential therapeutic targets, making direct comparisons of results from various studies has been difficult. A major challenge is the use of different assay platforms by different studies. The lack of standardization in gene nomenclature is also an issue. Several studies of head and neck squamous cell carcinomas (HNSCC) have been carried out based on deep sequencing data from TCGA , 50, 51.In silico prediction of the potential targets and the pathway analysis of these targets gave us some suggestion to the possible biological functions of our top differentially expressed lncRNA. However, further research is needed to explore more on these functions and the potential usage as biomarkers and/or therapeutic targets for OSCC.The recent development of using CRISPRi method for high throughput functional screening of lncRNAs has increased the number of lncRNAs with known functions . Howeverhttp://www.genenames.org/ at 1/27/2016) with Affymetrix probe ID using Entrez gene ID or Ensemble gene ID. 2,183 probe sets were matched to transcripts identified as “RNA, long non-coding” by HGNC; second, we identified 2,488 probe sets mapped to lncRNA from the Affymetrix annotation file  for the U133 2.0 Plus array. After removal of 1,617 duplicates, we obtained a final list of 3,054 probe sets that represent 2,172 lncRNA transcripts.The gene expression microarray data used in this study was generated previously by our group as part of an OralChip study at the Fred Hutchinson Cancer Research Center (FHCRC). The study was designed to identify gene expression profiles that are related to clinical outcomes of OSCC , 55, 56.The statistical analysis of differentially expressed lncRNA (between cases and controls) was performed using linear regression, adjusting for age and sex. The false discovery rate (FDR) of 0.01 and a 2-fold difference in expression between the two groups were used as criteria to select the differentially expressed lncRNA. All analyses were performed using R version 3.3.0.http://www.ncbi.nlm.nih.gov/geo/) for datasets with OSCC and normal control samples that also used the Affymetrix U133 Plus 2.0 Gene Chip array. Three datasets were identified: GSE42743 with 74 oral cavity squamous cell carcinoma and 29 adjacent normal tissue [In order to validate our findings, we searched Gene Expression Omnibus (GEO)  with the highest fold change to do further validation by performing qRT-PCR using a subset of 20 oral cancer samples and 10 control samples randomly chosen from samples in the OralChip study. Each sample was assayed in triplicate in 10 uL reaction volumes using the QuantiTect SYBR Green RT-PCR kit  on a 7900HT Sequence Detection System . The cycling conditions were as follows: 30 min incubation at 50°C, 15 min incubation at 95°C, and 40 cycles of 15 s at 94°C, 30 s at 55°C, and 30 s at 72°C. Ten-point standard curves were generated using Total RNA - Human Normal Tissue Tongue  for all three genes. The linear correlation coefficient (R2) was ≥ 0.99 for all runs. The mean threshold cycle (Ct) values were calculated from the triplicate Ct values. Samples that had Ct values with SD > 0.35 in their triplicate run were repeated. Mean Ct values were standardized to the mean Ct value of beta actin housekeeping gene . Correlations between qRT-PCR and microarray data were performed using R Stats Package. Primer pairs used for each gene were as follows: LOC441178: Forward primer GGGTATTTTGTGC TCCCCCA; Reverse primer CAGGCACTGAAGGTTCG GAT; HCG22: Forward primer ACAGCAGTGAAACC CACCA; Reverse primer GAAGCCCAATCCAACA AAGAGC; C5orf66-AS1: Forward primer GCTTCGCGTC AAGAGGGTAT; Reverse primer AAGCCGCGGGAA TGTCTTTA. P value was calculated using student's t test between groups.Among the validated list of lncRNAs, we chose three (LOC441178 was done using a comprehensive interaction database of lncRNA-mRNA which was reported by Terai G et al. [Prediction of the putative targets of lncRNA G et al.  in 2016.G et al. . The enr


Data analysis methods are usually subdivided in two distinct classes: There are methods for prediction and there are methods for exploration. In practice, however, there often is a need to learn from the data in both ways. For example, when predicting the antibody titers a few weeks after vaccination on the basis of genomewide mRNA transcription rates, also mechanistic insights about the effect of vaccinations on the immune system are sought. Principal covariates regression (PCovR) is a method that combines both purposes. Yet, it misses insightful representations of the data as these include all the variables.Here, we propose a sparse extension of principal covariates regression such that the resulting solutions are based on an automatically selected subset of the variables. Our method is shown to outperform competing methods like sparse principal components regression and sparse partial least squares in a simulation study. Furthermore good performance of the method is illustrated on publicly available data including antibody titers and genomewide transcription rates for subjects vaccinated against the flu: the selected genes by sparse PCovR are higly enriched for immune related terms and the method predicts the titers for an independent test sample well. In comparison, no significantly enriched terms were found for the genes selected by sparse partial least squares and out-of-sample prediction was worse.Sparse principal covariates regression is a promising and competitive tool for obtaining insights from high-dimensional data.https://github.com/katrijnvandeun/SPCovR.The source code implementing our proposed method is available from GitHub, together with all scripts used to extract, pre-process, analyze, and post-process the data:  Traditionally, data analysis methods are divided in two classes with different goals: Methods for prediction  and methods for exploration . An example of the former is assessing whether someone is at risk for breast cancer; in this case the aim is to use currently available information to predict an unseen (often future) outcome. On the other hand, the goal of exploratory methods is to gain an understanding about the mechanisms that cause structural variation and covariation in the available information. For example, exploration of gene expression data collected over time after addition of serum gave not only insight in the transcriptional program but also in processes related to wound repair . There aTo reveal the underlying mechanisms, component or matrix decomposition based methods can be used. Well known examples are principal component analysis (PCA) and the singular value decomposition . Yet, anp>n case , and α is a tuning parameter giving either more weight to the prediction of the outcome (α close to 0) or to the reconstruction of the predictor variables (α close to one). In fact, α=1 corresponds to principal components regression while α=0 corresponds to ordinary regression. Let X accounted for by T and Y. It can be seen then that the criterion is equivalent to maximizing such that X was proposed by T were calculated on the basis of the scaled component weights and the loadings resulting from the SPCovR analysis. These are data with no noise and in a final step noise was added by sampling from the normal distribution with mean zero and variance set in correspondence to the level of the proportion of variation accounted for by the components in the covariates and the outcome yielding data X and y. For each of the 27 conditions, 20 replicate data sets were generated resulting in 540 data sets in total. The code used to generate the data, including the seed used to initialize the pseudo random number generator, can be found on GitHub: https://github.com/katrijnvandeun/SPCovR.Note that the initial matrix α, namely 0.01, 0.50, and 0.99. For the SPCR analysis, we used the elasticnet R package that implements the sparse PCA approach in [W. For the SPLS analysis, the R package RGCCA was used that allows to (approximately) set the total number of zero coefficients over the components; the analyses options were set to result in (approximately) 320 zero coefficients. For the SPCovR analyses, we used stability selection with the upperbound on the number of non-zero coefficients q set equal to 80. Hence all analyses were tuned such that they had exactly or approximately the same number of zeroes as present in the true underlying component weight matrix. This makes the interpretation of the resuls easier in the sense that performance of the methods is not dependent upon proper tuning of the sparseness penalty. In the comparison of the methods, we will consider their performance in recovering the underlying components and how well they predict a new set of test data .Each dataset was subjected to five analyses: sparse principal components regression (SPCR), sparse partial least squares (SPLS), and three SPCovR analyses with different values of roach in . The elaroach in  procedurT=XTRUEW and those obtained from the analyses ϕ, is defined as [The results with respect to the recovery of the components is shown in Fig. fined as , 13\docT) and α=0.99, SPCR, and SPLS). For each combination of the variation accounted for in the covariates and in the outcome , three boxplots are shown for the three methods. These are the three levels of the relative strength factor with the boxplots at the left referring to the conditions where the first component is weaker than the second one, the boxplots in the middle referring to the conditions where they are equally strong, and boxplots at the right to the conditions where the second component is stronger than the first one. SPCovR outperforms the two other methods in all conditions, followed by SPCR which outperforms SPLS in most conditions. Only when the variance accounted for by the components in the block of covariates (the conditions VAFX = 0.01) is very low while it is high in the outcome variable (VAFY = 0.50/0.80), SPLS outperforms SPCR by taking advantage of the information included in the outcome. SPCovR, in all conditions, takes advantage of putting some weight on modeling the outcome in the construction of the components.this is the cosine of the angle between the vectors vec was calculated on test data as follows, where entclass1pt{minimaα: namely α=0.01, α=0.50, and α=0.99. For both performance measures and in all conditions, SPCovR with α=.99 yields the best results. Hence, it seems that little weight should be given to fitting the outcome in order to obtain good results in terms both of recovering the components and prediction of the outcome. Note that giving no weight at all to the outcome in modeling the components, this is a SPCR analysis, leads to worse recovery in general. For prediction, on the other hand, the gain of using SPCovR is limited to a few conditions and, when the noise in the covariates is considerable, SPCovR is prone to overfitting while SPCR is not.SPCovR was run with three levels of the weighting parameter We will illustrate SPCovR and compare with SPLS using data that result from a systems biology study on vaccination against influenza . The genhttps://www.ncbi.nlm.nih.gov/geo/) with accession numbers GSE29614 and GSE29617. For both seasons, a micro-array analysis was performed on the genomewide expression in peripheral blood mononuclear cells collected just before and 3 days after vaccination for all participants (26 in 2008 and 9 in 2007). Two different array platforms for measuring gene expression were used but the first 54,675 of 54,715 probe sets of the 2008 season are shared with the 2007 season. Hence, we can use the 2007 data as an independent test sample. Note that the choice for taking the 2007 data as the test set is motivated by the extremely small sample size. The RMA algorithm  was used to pre-process the CEL-files. The data collected just before vaccination were considered as the baseline and subtracted from the data at Day 3. For each variable (probeset), the difference scores were centered and scaled to sum-of-squares equal to one. These scaled difference scores form the set of predictor scores X in the SPCovR and sparse PLS analyses.The authors made data for two seasons, 2007 and 2008, publicly available on the NCBI Gene Expression Omnibus  antibody titers were assessed just before and 28 days after vaccination. As described by [α=.01,.02,…,.99) are compared to those resulting from the PLS analysis using the RGCCA R package [α. Because these solutions with high values for α give little weight to explaining the variance in the outcome, low α→0. When it comes to the use of the components obtained for the 2008 season to predict the outcome in the 2007 season, better results are obtained with the PCovR components obtained with α close to one, this is giving more importance to explaining the variance in the predictor data than in the outcome variable.We start with a principal component analysis of the gene expression data. The variance accounted for by each component is displayed in Fig.  package : Fig. 4 α was set to a value close to one (α=.99), and the ridge penalty was set equal to.05λ1. In the stability selection procedure, we used N=500 resamples, the threshold π was set equal to 0.90 and E(V)=1. This results in qR=416. We compare with the sparse PLS results from two R packages, RGCCA [R=2 components and tuned such that approximately 416 non-zero component weights were obtained in order to have similar sparseness of the sparse PCovR and sparse PLS solutions. spls [λ2→∞. The SGCCA function in RGCCA [Next we turn to the analyses with imposed sparseness on the component weights. The metaparameters of the SPCovR model were set using the proposed stepwise model selection procedure. Hence, based on Fig. s, RGCCA  and splss, RGCCA  also usins. spls  uses a uin RGCCA  was usedThe fit of the solutions to the observed data is summarized in Table The percentage of variance accounted for by each of the individual components can be found in Table Another criterion that is important when comparing the different solutions is related to the interpretation of the solution: Do thecomponents reflect a common biological theme that gives insight into the mechanisms that underly vaccines? To answer this question, a functional annotation based on the strength of association of the genes with the components can be performed. The SPCovR and SGCCA results contain such information in two ways, namely in the component weights and in the loadings. The component weights reflect those genes (probesets) that have the strongest contribution to the component scores. Because of the sparseness restriction, only few of them are non-zero. The loadings, on the other hand, reflect the strength of association between the expression values of a particular gene and the component scores. Whereas the component weights measure the unique contribution of a gene on top of the other genes, the loadings measure the strength of association without taking the other genes into account ; see also .https://github.com/katrijnvandeun/SPCovR We performed the functional analysis of these gene lists using the statistical test of over-representation in PANTHER. This means that, for each functional class, the number of genes belonging to that class and present in our list of selected genes was compared to the number of genes for that class in the whole genome. A test of overrepresentation was conducted for each class. An overview of significantly overrepresented functional classes is given in Table py2=0.02 compared to py1=0.004. Notably, the gene encoding for Calcium/calmodulin-dependent kinase IV (Camk4) was included as an active predictor in the set. This gene was singled out in the original study of [BACH2 (Transcription regulator protein BACH2) gene, which is a known transcription factor necessary for immunity against influenza A virus [https://github.com/katrijnvandeun/SPCovR) it can be seen that only 32 probesets have non-zero weights both for SPCovR and SGCCA, corresponding to 19 unique gene symbols. Relatively high weights in both analyses were obtained for SMUG1  which has a role in antibody gene diversification and PPP1R11 (Protein phosphatase 1 regulatory inhibitor subunit 11) known to effect NF- κB activity [First, we performed a functional annotation of genes associated to the probesets with non-zero component weights using the publicly available annotation tool of PANTHER . A list  A virus , was incactivity . Also foactivity , no termhttps://github.com/katrijnvandeun/SPCovR), here we summarize the main results. A first result of interest is that the same kind of processes are recovered from the enrichment analyses of the loadings as obtained previously when looking for over-represented classes in the gene lists with non-zero component weights. Also here, the annotation of the loadings obtained with SPCovR shows evidence of immune related processes while such evidence is weak for the SGCCA loadings. Notably, some immune related gene ontology terms are found in the enrichment analyses of the SGCCA loadings. In fact, overall more terms are recovered from the enrichment analyses of the loadings. This could be expected given the small number of genes involved in the lists obtained from the non-zero component weights.Second, we performed an enrichment analysis based on the loadings. The loadings reflect the strenght of association of a gene with the component with higher loadings indicating that the gene is more important for the process at play. Both PANTHER and GSEA  accept aTaken together, the results suggest that SPCovR, by putting more emphasis on accounting for the structural variation in the gene expression data when building the prediction model, catches the processes that are important in establishing the immune response to the vaccine. This pays off in the sense that a more stable prediction model is obtained that has better generalizability (and thus better prediction for the held out sample).Often a large amount of variables is measured with a double goal: Predicting an outcome of interest and obtaining insight in the mechanisms that relate the predicting variables to the outcome. In the high-dimensional setting this comes along with a variable selection problem. Principal covariates regression is a promising tool to reach this double goal; we extended this tool to the high-dimensional setting by introducing a sparse version of the PCovR model and offerering a flexible and efficient estimation procedure.In this paper we showed through simulation that sparse PCovR can outperform sparse PLS as it allows to put less emphasis on modeling the outcome: By putting more weight on accounting for the variation in the covariates, more insight in the processes that underly the data may be obtained and this, in turn, results in better out-of-sample prediction. The benefit of this was illustrated for publicly available data: clearly a meaningful annotation of the selected genes was obtained with SPCovR while no enriched terms were found for the genes selected by sparse PLS. At the same time, the SPCovR analysis resulted in a much better out-of-sample prediction.Here we will discuss the estimation of the loadings and component weights in the alternating procedure presented in Algorithm 1.Given the component weights, the problem that needs to be solved is to minimize PTP)=1 (oblique case) or (PTP)= I  and with such that diag.The solution to the orthogonal case is given by WGiven the loadings, we need to solve the following problem: Minimize with respect to The latter expression can be rewritten as follows: zij modeled on the basis of RJ variables. To solve the optimization problem, we rely on a coordinate descent procedure [wjr is updated in turn while keeping the remaining coefficients fixed. Hence, rewriting the loss function with isolation of one specific coefficient wj∗r∗, we obtain This is an elastic net regression problem with outcome scores rocedure : each ofThis is a univariate elastic net regression problem with the following solution RJ coefficients and the involvement of large matrices in the computations. We accounted for these computational issues in our implementation by rewriting the expressions in (P. A further speed up may be obtained by warm restarts and active set learning, this is cycling over the non-zero coefficients only. Further improvement over the GLMnet procedure [The procedure may seem slow given the loop over sions in  and makirocedure  was obtarocedure .


Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological  Having moved into an era of molecular medicine, high throughput ‘omics’ screening methods are being used to decipher informative, disease specific markers promising effective treatment strategies for individualized treatments. With improved gene-based technologies now enabling rapid and cost-effective genome sequencing, researchers are now looking to the proteome as accurate and responsive predictors of the pathophysiological state of an individual. Proteome workflows to identify biomarkers capable of diagnosis ,2, prognin vitro or in vivo experimentation have been profiled in an effort to bring biomarkers to the clinical setting. Problematically, despite numerous claims of success, no test derived using MS-based proteomic techniques is currently FDA approved. Acknowledging the dynamic complexity of any proteome, this lack of validated biomarkers is ultimately attributed to flaws in experimental design [Since the mid-1990s, research in the area of MS-based proteome analysis is growing exponentially in conjunction with the search for novel disease biomarkers. l design ,18, the l design , or inadl design ,22,23. Il design ,25,26,27This review will discuss the current state of biomarker research, as well as the inherent challenges associated with proteomic technologies for identification of disease biomarkers. It should be noted that a biomarker discovery experiment extends beyond the analytical lab. For example, proper consideration must be given to the number  and type  of samples to be taken for analysis, the method of sample collection  and preservation . Following discovery of a putative biomarker, a validation phase must be included to determine the efficacy  of the biomarker at the clinical level. Methods for validation of biomarkers have been reviewed ,29 and iAcknowledging that most reliable biomarker would arise from analysis of the ‘normal’ state of a single individual compared with the ‘diseased’ state of the same individual, this may not be possible. A lack of baseline comparisons, such as in paediatric populations, or knowledge of what sample to analyze, and what to search for make this form of biomarker discovery not feasible for the discovery phase. This review focuses on the fundamentals of experimental design and provides an in-depth analysis of common errors in biomarker discovery experiments that must be addressed prior to execution of the experiment. “…characteristic that is objectively measured and evaluated as an indicator of a normal biological process, pathogenic process, or pharmacologic responses to therapeutic intervention” [The National Institute of Health defines a biomarker as a vention” . With reThe stringent requirements for ideal biomarkers presented in in vitro or in vivo models of disease, or to profile clinical (human) samples to uncover biomarkers. Therefore, careful consideration of the source of biomarker must be given prior to experimentation. The choice of sample generally depends on the method of analysis, but also the disease in question. The underlying hypothesis of biomarker experimentation is that pathophysiological changes in cells or tissue are reflected through gene or protein expression, preferably in a disease-specific fashion. Biomarker discovery experiments aim to exploit these changes for clinical testing. A range of omics technologies ,37,38,39In vitro disease models provide a simplified sample source for researchers to elucidate a cell-specific physiological response to disease or treatment. A model culture system has the benefit of limiting several confounding variables which plague clinical samples by controlling test conditions which optimize the model. Cell culture models are common to study the response of a particular cell type to various stimuli [in vitro models likely do not reflect the complexity of the disease by excluding the heterogeneity of cells affected by the disease, as well as the heterogeneity of the afflicted population. In vitro models employing high throughput ‘-omics’ methods are therefore more commonly reserved for holistic, semi-quantitative assessments of changes in protein or gene expression profiles [ stimuli , as seenprofiles ,42. Tissue biopsies and core samples are a common source for clinical diagnosis through microscopic evaluation following a number of staining techniques, but can also provide a source for biomarker research. Biopsies can provide researchers with direct access to diseased tissues, and therefore potential biomarkers, making them the most relevant source for biological information during biomarker experimentation. Moreover, because biopsies constitute the traditional route for pathological characterization of many diseases, methods for sample collection, storage, and analysis are now standardized and routine. This offers a potential route to integrate tissue biopsies into biomarker identification platforms. Previously, the fixation and staining processes required for standard pathological assessment prevented coupling to standard proteomic methodologies. More recently, methods have been established which allow direct proteomic analysis of tissue samples following processing  [taining) . As a retaining) ,44. ExtrDespite the benefits of biopsy analysis, access to the sample by the researcher is restricted - perhaps rightfully so. Obtaining the sample from a patient is not trivial, and has the potential to introduce complications such as infection that could lead to decreased quality of life for the patient. This is especially true if the diseased tissue is present in a sensitive or difficult to access area, which makes collection of appropriate samples a key source of error. Combined with other sources of error which may be introduced by the researcher conducting the analysis, the use of less invasive sources of biomarkers has become a more common strategy for discovery purposes.Proximal fluids are obtained from the extracellular milieu of tissues and contain a wide range of soluble and secreted factors from cells within a tissue microenvironment [et al. [ironment . Compare [et al. ). Blood and urine are by far the most commonly obtained biological samples in a clinical setting and therefore provide an excellent medium for biomarker research. While these samples can be considered proximal fluids themselves, they are generally used as systemic measures, as opposed to tissue-specific indicators . Despite the simplicity of sample collection, biomarker identification from these samples has been especially lacking. Issues stemming from sample collection [llection ,47,48, sllection ,50, compllection ,51 have llection . Effortsllection ,20.Proteomic analysis of serum  and urinRegardless of source, biological samples contain a complex array of proteins, nucleic acids, and cell signalling molecules, all of which have potential use as disease biomarkers. Effective experimental design requires formulation of a hypothesis, proper selection of a test group, and allocation of appropriate experimentation to draw unbiased conclusions that support or refute the proposed hypothesis . The proi.e., LC-MS analysis). Construction of an unbiased experiment therefore begins with proper understanding of the experimental procedures before running those precious clinical samples. A number of biomarker studies have, through a wide range of experimental pitfalls, generated false results. Issues stemming from improper sample population selection , sample High throughput biomarker identification studies require adherence to the experimental setup provided by Fisher to prevent bias introduced during analysis. Sequential processing of samples, although potentially easier to execute, is one of the most common, though easily avoided sources of bias. Randomization in experimental design refers to both the collection of samples as well as sample processing and data analysis. Randomization guards against the introduction of uncontrollable variables unknown to the researcher that may affect the accuracy of the data. Changes in response may include factors such as sample collection and storage time, protein extraction and processing, changes in chromatographic separation and instrumental drift. All aspects of the workflow, from sample collection to data interpretation need to be controlled. The effect of randomization is shown in vs. text groups for a ‘false’ biomarker. Without randomization, the 5% signal drift introduced an apparent difference for the false biomarker, invalidating the results of the analysis. Numerous examples of this have been presented in the literature, including the false identification of a biomarker for ovarian cancer, which were later proven to be invalid as the original profiling of diseased vs. healthy samples occurred on separate days, yielding the false results [ results ,66.Replication allows assessment of biological and technical variability of the biomarker identification workflow while quantifying the range of ‘normal’ vs. ‘diseased’ states for potential biomarkers. Technical replication tests the variation within each of the experimental units, such as sample isolation, collection, storage, preparation, or detection. Quantifying the level of technical variation within each experimental unit is essential for determining a threshold above which test groups are statistically different. Knowledge of the variability in the methodology will also assist in prediction the number of biological replicates required to obtain quantitative information [ormation ,67,68. Bormation . The majority of variability is assumed to be biological, and so increasing the number of biological replicates will achieve a higher level of confidence in the result . HoweverBlocking is meant to prevent bias contributed by experimental parameters known to the researcher. Examples of blocking during experimentation include organizing samples based on age, gender or ethnicity, but also on the disease grade or sample origin. Blocking should be applied to insure equal allocation of experimental groups in a randomized trial, allotting equal analysis time of healthy and diseased groups. As an example, a test for obstructive coronary artery disease (CAD) predicts with high confidence the presence of CAD in non-diabetic men, however is ineffective in women, and men with diabetes [diabetes ,73,74. Odiabetes ,65.The previous section discussed in detail how employing an unbiased experimental workflow can improve confidence in results by eliminating error during analysis. However, without careful attention to experimental protocols, alternative sources of bias in experimentation can lead to erroneous results. Numerous forms of bias, ranging from sample collection and storage to purification and sample preparation, have contributed to false or low quality biomarker identification in prostate and ovarian cancers ,63,75,76The innate complexity of biological samples can also play a role in erroneous or insensitive results. For example, bottom-up (peptide level) proteomic strategies employing LC-MS generally include a preliminary, protein level form of fractionation ahead of MS analysis, which promotes visualization of a wider dynamic range of proteins. Unfortunately, many strategies employed for fractionation include buffer additives incompatible with LC-MS which must be removed prior to analysis. At best, removal of these compounds can lead to sample loss and decreased sensitivity ,82,83. Sp-value of a t-test of 95% confidence, implies that 5 times out of 100 the reading is a falsely identified as being significant. Following this logic, conducting a t-test on expression profiles of 10,000 genes or proteins at 95% confidence potentially leads to identification of 500 false positives. Shifting the confidence interval to decrease the number of false positive identifications (referred to as type I error) can theoretically improve confidence in the data; however will lead to a significant increase in false negative identifications (type II error). This confounding statistical problem is known as ‘the curse of dimensionality’ [Microarray and MS based techniques generate thousands of data points for a single sample ,53. As tonality’ .et al. (2012) [i.e., diseased or healthy). However, with these statistical approaches, there is a danger of over-fitting the data. Over-fitting occurs as a result of applying a large number of variable data points to a small number of outcomes . Insuring the quality of data therefore requires a large ‘training’ sample group accompanied by an independent ‘validation’ group. Ransohoff reviewed the terminology [Obviously, these methods are not ideal for biomarker identification in this fashion, introducing a need for statistical methodology better suited to high dimensional datasets. A number of researchers have published methods in an attempt to circumvent or reduce the effects of the curse of dimensionality ,90,91. S. (2012) . These sminology , and givminology . Other mminology ,94 or himinology ,96. Thesminology ,98.The field of biomarker discovery using high throughput methodology in the form of microarray chip technology and mass spectrometry is rapidly expanding. Biomarker studies aim to compile gene or protein profiles for effective and decisive disease diagnosis, prognosis, and prediction of effective treatment plans. Early on, the field of biomarker discovery experienced some growing pains in the form of flawed methodologies and inadequate statistical analyses, leading to irreproducible biomarker discoveries. These errors in judgement, as researchers rushed to publish and identify biomarkers unfortunately led to erroneous results. These false claims shook the confidence of researchers and caused a general mistrust of the ability of high throughput technologies to yield informative disease markers. Over the last decade, a large quantity of research has been committed to the critical assessment of sources of bias, statistical analysis, and experimental procedures employed in biomarker discovery studies. This research has provided a better understanding the fundamentals in experimental planning and execution, as well as statistical analysis of such large data sets. As technology develops to allow even greater amounts of data to be generated, methods to handle such large datasets must be understood and applied correctly to allow effective conclusions. In response to the need to obtain quantitative information of large data sets, statistical algorithms capable of dealing with them are being developed, and ever-expanding computational power is also allowing a greater number of samples to be analyzed. The future of biomarker research depends on the experiments currently being conducted, and retaining stringent requirements for true identification of biomarkers, with sufficient validation to back claims will boost confidence in the field and allow a greater understanding of the molecular pathophysiology behind a great number of diseases.


Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients.LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy.We propose a method  for stage I-II LUAD patients without adjuvant therapy , which was confirmed in two independent datasets of GSE50081  and GSE31210  after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion.In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.The online version of this article (doi:10.1186/s12943-017-0666-z) contains supplementary material, which is available to authorized users. LncRIndiv) to detect DE lncRNAs in individual patients, which has been improved based on our original methodology that were developed to detect DE miRNAs in individuals [Long non-coding RNAs (lncRNAs) are non-coding RNAs ranging in length from 200 nucleotides to ~100 kilobases  and DESeividuals .LncRIndiv method could be applied to detect the prognostic signature for cancer.Considerable efforts have been devoted to identify lncRNA prognostic signature for cancers using absolute expression profiles and risk score based methods , 12, 13.LncRIndiv could reach good performance for individual-level analysis of deregulated lncRNAs in independent paired normal-cancer samples. And, a significant proportion of up- or down-regulated DE lncRNAs showed concordance of amplified or deleted copy number alterations, providing evidence of the high reliability of the LncRIndiv method. Based on the lncRNAs individual-level differentially expression analysis, we successfully developed a new prognostic signature (C1orf132 and TMPO-AS1) for stage I and II LUAD patients without adjuvant therapy. This new signature does not rely on pre-setting thresholds for prognostic prediction and performed well in independent datasets.Lung adenocarcinoma (LUAD) is one of the important sub-types of lung cancer with high morbidity and mortality . In thisThe microarray platform used in this work was Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133 Plus 2.0), including the information of probes, Ensembl IDs and (or) RefSeq IDs. The information for each lncRNA, such as Ensembl ID, Ensembl transcript ID and symbol, was downloaded from the GENCODE (release 19). Meanwhile, the corresponding relationship between Ensembl transcript ID and the RefSeq ID for lncRNAs were downloaded from the HGNC database (version corresponding to GENCODE release 19). By matching those datasets, we got the symbols and RNA types for each probe. Finally, we retained the long non-coding genes and filtered them by removing discordant probes information, pseudogenes, rRNAs, tRNAs, snRNAs, snoRNAs and other short non-coding RNAs [LncRindiv. Besides, 136 stage I or II LUAD patients without adjuvant therapy with complete overall survival information were used as training dataset to derive the lncRNA prognostic signature  generated based on the HG-U133 Plus 2.0 were downloaded from Gene Expression Omnibus. The raw data for each dataset was processed using the RMA algorithm for background adjustment without normalization . Then, ehttps://cancergenome.nih.gov/), and was processed using the GISTIC 2.0 algorithm [FDR [et al. did [https://cancergenome.nih.gov/).The Affymetrix Genome-Wide Human SNP array 6.0 data of 429 LUAD samples was downloaded from The Cancer Genome Atlas (TCGA) database  using Fisher’s exact test at FDR < 0.1.Each lncRNA’s expression value was converted to its rank within each sample . Pairwise comparisons were performed for all lncRNAs to identify lncRNA pairs with stable order in normal samples. Stable lncRNA pairs were defined as patterns of rank, such as Step 1:The absolute expression profile of lncRNAs is transformed into rank profile.Step 2:lncRNA-A as an example. According to the rules in Fig. lncRNA-A in cancer samples, including partner lncRNAs lncRNA-B, lncRNA-C, lncRNA-D, lncRNA-E, and lncRNA-F. Only the partner lncRNAs that have the same dysregulation directions as lncRNA-A in the lncRNA-A reversal pairs are retained. Here, the dysregulation directions indicated the expression of lncRNA-A is up-regulated in the cancer group comparing with the normal group. In Fig. lncRNA-C is removed because of the down-regulation trend.Take Step 3:lncRNA-A in the lncRNA-A reversal pairs  of rank across cancer and normal samples for each partner lncRNA of irs Fig. . We hypoStep 4:lncRNA-A > lncRNA-D, lncRNA-A > lncRNA-E and lncRNA-A > lncRNA-F) are used to determine whether lncRNA-A is differentially expressed in an individual patient. If more than half of the reversal lncRNA pairs are detected in a patient, we conclude that lncRNA-A is differentially expressed in the patient . The simulation experiment enables us to know both the DE lncRNAs and non-DE lncRNAs and facilitates the calculation of sensitivity, specificity and F-score. Here, the sensitivity is defined as the ratio of correctly identified DE lncRNAs to all DE lncRNAs and the specificity is defined as the ratio of correctly identified non-DE lncRNAs to all non-DE lncRNAs. The F-score, a harmonic mean of sensitivity and specificity, was calculated as follows:First, a simulation was performed to evaluate the performance of LncRIndiv method and the actual dysregulation directions observed in the paired samples. For a pair-wise cancer and normal tissues, if the rank of a lncRNA in cancer sample was larger than that of matched normal sample, the dysregulation direction of the lncRNA was up-regulated (and vice versa), which was taken as the benchmark. The consistency score was calculated as the ratio of the observed consistent DE lncRNAs to all DE lncRNAs identified in each sample.Moreover, the real pair-wise cancer-normal samples were used to evaluate the consistence of dysregulation directions of DE lncRNAs between those identified by P < 0.05) [First, for each DE lncRNA, stage I and II LUAD patients without adjuvant therapy were separated into with and without DE lncRNA groups. Then, we selected prognosis-related lncRNAs that were significantly associated with patient overall survival using the log-rank test  and univ < 0.05) . Harrell < 0.05) . We perf < 0.05) .FDR < 0.05, which were defined as the lncRNA-DEGs. Then, we used GO-function method to extract the biological process from Gene Ontology (GO) database that were significantly enriched with lncRNA-DEGs (FDR < 0.05) [P < 0.05, Pearson Correlation Test). Then, we performed the KEGG  pathway enrichment analysis for the lncRNA correlated DEGs to study the regulation function for the lncRNA.T-test was used to detect the DEGs between the high- and low-risk patients at the  < 0.05) . To inveP = 6.26 × 10−23, binomial test, Fig. P < 1.0 × 10−15, hypergeometric test), indicating that stable lncRNA pairs are highly reproducible between different platforms. Compared with normal samples, 75648 and 38611 lncRNA pairs were significantly reversed in LUAD cancer samples from microarray and sequencing data, respectively. The consistent ratio of the reversal lncRNA pairs between the microarray and sequencing was 98.51% . Thus, to evaluate the performance of LncRIndiv between different platforms, we performed all analysis on the common 1310 lncRNAs between microarray and sequencing datasets.For each LUAD sample, lncRNA expression values were converted to rank values with increasing order. A stable lncRNA pair was defined as that the rank relationships between the expression levels of two lncRNAs were presented in more than 95% of normal samples (FDR < 0.1). For a lncRNA, considering all the partner lncRNAs that have reversal relationship with the specific lncRNA, we selected the top 3 partner lncRNAs with the smallest coefficient of variation to perform the following individual analysis. Totally, 1257 and 1123 lncRNAs could be detected with differential expression status in the microarray  comparing to the expression in the normal sample. Then, an average of 10 samples generated by random in which DE lncRNA was set to be differentially expressed, which was the same as the real dataset. Finally, RankComp was another method to detect DE genes in individual samples [LncRIndiv with RankComp in simulation data under the same condition. The detailed results of simulation experiments and parameter settings were presented in Additional file LncRIndiv were higher than those from RankComp method. Moreover, the RankComp reached a lower consistency score at about 81% level compared with those got by LncRIndiv method. Also, we showed the DE lncRNAs identified by the LncRIndiv and Rankcomp in venn diagram  are marked with symbols in the Additional file  samples . Here, wP < 0.05, hypergeometric test). The results showed that 61 of the 119 lncRNAs showed significantly consistent changes between copy number alteration and deregulation of expression in individual LUAD patients . When selecting lncRNAs with copy number alterations using FDR < 0.1 and FDR < 0.05, DE lncRNAs also showed consistent changes between copy number alteration and deregulation of expression in individual LUAD patients . Thus, the significant concordance between the differential expression and copy number alteration of lncRNAs indicated the high reliability of the results derived by LncRIndiv.Four hundred and twenty nine LUAD samples were screened using the Affymetrix Genome-Wide Human SNP array 6.0 platform. Among the 1123 DE lncRNAs derived from sequencing dataset, 285 lncRNAs were in the regions with significant amplifications or deletions in LUAD patients. One hundred and nineteen of the 285 lncRNAs showed concordant expression changes with copy number alterations in LUAD samples, which meant that the lncRNAs with amplification (deletion) showed up-regulation (down-regulation). Then, we tested whether patients with up-regulation (down-regulation) of lncRNA were significantly overlapped with patients with copy number gain (loss) (P < 0.05). Then, we performed a forward selection procedure to obtain a merged prognostic signature that achieved optimal prognostic performance (see C1orf132 and TMPO-AS1) with a C-index of 0.641 was obtained. Patients in the high-risk group (n = 47) had significant shorter overall survival than those in the low-risk group , P = 1.03 × 10−3, univariate cox analysis) or TMPO-AS1 , P = 1.96 × 10−2, univariate cox analysis) and the rest LUAD patients were classified into the low-risk group. Compared with the low-risk patient group, the lncRNA C1orf132 was down-regulated in the high-risk patients , FDR < 0.05, respectively. We found that 96.38% of overlapped genes between the two DEGs lists were consistent in their deregulation directions (up-regulation or down-regulation). Next, using the GO-function method [FDR < 0.05). Twenty-four biological process terms overlapped between the two term lists, including “DNA replication”, “cell cycle”, and “cell division” and so on, which couldn’t be expected by chance . Results of highly overlapping DEGs and GO terms between the two datasets suggest that the 2-lncRNA is a robust prognostic signature for stage I or II LUAD patients without adjuvant therapy.Furthermore, we used T-test to detect DEGs between the high- and low-risk patients in each dataset (GSE50081 and GSE31210) at the n method , we furtFDR < 0.05, “Cell cycle” pathway  and “Cell adhesion molecules (CAMs)” pathway  were significantly enriched with the lncRNA-DEGs. Twenty-two DEGs were annotated in the cell cycle pathway , and interactions between DEGs and their first neighbors were presented in Fig. Moreover, GO enrichment results suggest that the two lncRNAs may regulate the “cell cycle”, “cell division” of cancer cells to affect the prognosis of LUAD patients. Thus, we further investigated the regulation relationship between lncRNAs and lncRNA-DEGs, which might be the potential mechanism to induce the poor prognosis of LUAD. Based on the GO-function analysis, we performed the KEGG pathway enrichment using the overlapped DEGs between the high- and low-risk groups derived from the GSE31210 and GSE50081. Under the control of CDC25A, an oncogene, was significantly correlated with poor overall survival in non-small cell lung cancer [CDC25A was positively co-expressed with lncRNA TMPO-AS1  and is freely available in https://github.com/FuduanPeng/LncRIndiv (LncRIdiv_1.0.zip for Windows system and LncRIndiv_1.0.tar.gz for Linux system).Availability and Implementation: LncRIndiv to detect the DE lncRNAs in individual cancer patients, which is not limited by the platform, data normalization methods and batch effects. In the method LncRIndiv, we used the CV of rank rather than the absolute expression levels of partner lncRNAs in our previous work [LncRIndiv method. The LncRIndiv performed well in the independent pairwise LUAD datasets and the simulation data.Aberrant expressions of lncRNAs in cancer patients have been comprehensively reported . The expous work , which cLncRIndiv method also identified some DE lncRNAs that were well characterized by other studies    to influence miRNA activity and thereby regulate the target transcripts containing miRNA-binding sites [C1orf132 may act as ceRNA with the tumor suppressors RBL2 and CCND3, which have been showed with significant positive correlation with the expression of C1orf132 in the  and CCND3  . As examples shown in Additional file LncRIndiv method could reveal a new robust prognosis-related lncRNA signature for stage I or II LUAD patients without adjuvant therapy, which was validated in other independent microarray datasets. The LncRIndiv method could also be used in other cancer types with abundant sequencing expression profile of lncRNAs. Finally, by KEGG pathway enrichment and correlations analysis between lncRNAs and DEGs, we found that the lncRNAs (TMPO-AS1 and C1orf132) could affect the prognosis of LUAD by deregulating cell cycle pathway genes. Although the results are interesting and meaningful, it is lack of biological experiments for further validation. We will continue to investigate the biological mechanisms that how the lncRNAs regulate the cell cycle genes during the carcinogenesis in our future work.Nevertheless, our present method also has some limitations. First of all, although the consistency score are relatively high, LncRIndiv. Based on the differential expression profiles of lncRNAs in individual LUAD patients derived by our method, we identified a new robust lncRNA prognostic signature consisting of C1orf132 and TMPO-AS1 for stage I and II LUAD patients without adjuvant therapy. This new signature did not rely on pre-setting thresholds for prognostic prediction and performed well in independent datasets.We developed a rank-based method that was not limited by expression platforms or normalization techniques to detect differentially expressed lncRNAs in individual LUAD patients and reached good performance in both simulated data and real data. The up-regulation (down-regulation) of lncRNAs in individual LUAD samples, were significantly consistent with the copy number amplifications (deletions), supporting the DE lncRNAs detected in individuals by Additional file 1: Table S1.Information of probes, Ensembl ID/RefSeq ID and symbol for each lncRNA annotated from Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133 Plus 2.0). (XLSX 151 kb)Additional file 2:Table S2. The LUAD datasets used for application of LncRIndiv. Table S3. The paired normal-cancer LUAD sample data used for evaluating the performance of LncRIndiv. Table S4. The datasets of stage I and II LUAD patients without adjuvant therapy. (DOC 95 kb)Additional file 3: Table S5.Detail information of differentially expressed lncRNAs identified based on microarray data. (XLSX 275 kb)Additional file 4: Table S6.Detail information of differentially expressed lncRNAs identified based on RNA-Seq data. (XLSX 230 kb)Additional file 5:Figure S1. The hetamap of differentially expressed (DE) lncRNAs for microarray data (A) and sequencing data (B), respectively. Figure S2. Venn diagram to show the overlapped differentially expressed lncRNAs identified by LncRIndiv and RankComp using microarray data (A) and sequencing data (B). Figure S3. Kaplan-Meier estimates the overall survival in the training dataset and two independent validation datasets based on the differential expression of (A) C1orf132 and (B) TMPO-AS1, respectively. Figure S4. Cell cycle pathway annotated with differentially expressed genes. Figure S5. Cell adhesion molecules pathway annotated with differentially expressed genes. Figure S6. Sub-network of cell adhesion molecules pathway regulated by C1orf132. Figure S7. Expression levels of lncRNAs in the pair-wise LUAD patients for (A) LINC00341 and (B) AC005083.1. (DOC 3470 kb)Additional file 6:Table S7. The consistency score under top 3, 5 and 7 reversal pairs in pair-wise datasets. Table S8. Comparison of LncRIndiv and RankComp methods using simulation data. Table S9. Information of lncRNAs with significant consistence between differential expression status and copy number alteration. Table S10. Information of genes co-expressed with TMPO-AS1 and C1orf132 in cell cycle pathway in GSE50081. Table S11. Differentially expressed lncRNAs identified by LncRIndiv method supported by experimental evidence. Table S12. Information of competing endogenous RNA and miRNA with the lncRNA C1orf132. Table S13.. Sensitivity, specificity, and F-score in simulated data under different scenarios. (DOC 257 kb)


Single-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills.We have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.http://garmiregroup.org/granatum/appGranatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at The online version of this article (doi:10.1186/s13073-017-0492-3) contains supplementary material, which is available to authorized users. Single-cell high-throughput RNA sequencing (scRNA-Seq) is providing new opportunities for researchers to identify the expression characteristics of individual cells among complex tissues. From bulk cell RNA-Seq, scRNA-Seq is a significant leap forward. In cancer, for example, scRNA-Seq allows tumor cells to be separated from healthy cells , and priSoftware addressing these computational challenges typically requires the ability to use a programming language like R , 6, limiThe front-end and the back-end of Granatum are written in R  and builLayout and interactivity for the protein–protein interaction (PPI) network modules is implemented using the visNetwork package . PreviewThe expression matrix and the metadata sheet are stored separately for each user. The metadata sheet refers to groups, batches, or other properties of the samples in the corresponding expression matrix. All modules share these two types of tables. Other variables shared across all modules include the log-transformed expression matrix, the filtered and normalized expression matrix, the dimensionally reduced matrix, species (human or mouse), and the primary metadata column.Batch effect is defined as the unwanted variation introduced in processing or sequencing in potentially different conditions . To remoThis method adjusts the batch effect using empirical Bayes frameworks, and is robust in the presence of outliers or for small sample sizes . It is omedi for sample i. Second, it calculates the mean of medi for each batch, denoted as batchMeanb for batch b:First, this method calculates the median expression of each sample, denoted as i ∈ batchb and m is the number of samples:Finally, it multiplies each batch by a factor that pulls the expression levels towards the global geometric mean of the sample medians. When sample_beforei and sample_afteri denote the expression levels for all genes within sample i before and after batch-effect removal.where Z-score threshold is used to automatically detect outliers. The z-score of a cell is calculated by calculating the Euclidean norm of the cell’s vector of expression levels, after scaling all genes to have unit standard deviation and zero mean . Over-din is the number of genes, m is the number of samples, and k is the number of clusters.The following description of clustering algorithms assumes that n-by-m) is factorized into two non-negative matrices H (n-by-k) and W (k-by-m). The highest-valued k entry in each column of W determines the membership of each cluster [The log-transformed expression matrix  is done on either the log-transformed expression matrix or the 2-by-ion in R . The heai,j between sample i and sample j is defined as:The method assesses heterogeneity of the data using a two-step process. First, it calculates a distance matrix using the correlation distance. The correlation distance DSi and Sj are the i-th and j-th column (sample) of the expression matrix. Next, Rtsne R package [where  package  uses thiThe principal component analysis algorithm, implemented as “prcomp” function in R, decomposes the original data into linearly uncorrelated variables (components) using orthogonal transformation. The components are then sorted by their variance. The two components with the largest variances (PC1 and PC2) are extracted for visualization .k = 2 to k = 10, the percentage of the explained variance (EV) is calculated. To find the elbow-point k = m where the EV plateaus, a linear elbow function is fit to the k-EV data points. This piecewise function consists of a linearly increasing piece from 0 to m, and a constant piece from m to 10. The algorithm iterates from m = 1 to 10 and identifies m which gives the best coefficient of determination (R2) of linear regression as the “elbow point”.This method is inspired by a similar approach implemented in SCRAT . In the We include four differential expression (DE) algorithms in Granatum: NODES , SCDE 3, EdgeR , and Limfgsea R-package implements the gene set enrichment analysis (GSEA) algorithm with optimizations for speedup [enrichment score, which quantifies the relevance of a gene set  term) to a particular group of selected genes . The p value is calculated for each gene set according to the empirical distribution, followed by Benjamini–Hochberg multiple hypothesis tests [The  speedup , 37. GSEis tests .CellDataSet required for monocle’s input, we set the expressionFamily to negbinomial.size. We use reduceDimension function to reduce the dimensionality by setting max_components to 2.We use Monocle (version 2.2.0) in our pseudo-time construction step. When building the http://garmiregroup.org/granatum/app). The project is fully open source, and its source code can be found at http://garmiregroup.org/granatum/code.Granatum is by far the most comprehensive graphic-user-interface (GUI)-based scRNA-Seq analysis pipeline with no requirement of programming knowledge Table . It alloWe have systematically compared Granatum with 12 other existing tools to demonstrate its versatile functions (Table https://zhiji.shinyapps.io/scrat/), ASAP [http://sake.mhammell.tools/), it is the only GUI pipeline that supports multiple dataset submission as well as batch effect removal. (2) Each step can be reset for re-analysis. (3) Certain steps  can be bypassed without affecting the completion of the workflow. (4) Subsets of the data can be selected for customized analysis. (5) Outlier samples can be identified either automatically (by setting a pre-set threshold) or manually (by clicking/lassoing the samples from the PCA plot or the correlation t-SNE plot). (6) Multiple cores can be utilized in the differential expression module for speed-up. (7) Both GSEA and network analysis can be performed for the differentially expressed genes in all pairs of subgroups, following clustering analysis. (8) Pseudo-time construction is included, giving insights into relationships between the cells. Besides the features above, a number of enhanced functionalities make Granatum more flexible than other freely available tools Table . (1) Unl/), ASAP , and SakIn this report, we mainly use a previously published data set as an example . This reP-dataset) provided by 10x Genomics, which comprises 6000 peripheral blood mononuclear cells . When the imputation step is not included, the running time scales linearly with the number of cells, regardless of platform  with different sizes of datasets, we first simulated expression matrices with 200, 400, 800, or 1600 cells using the Splatter package, based on the parameters estimated from the K-dataset [There are ten main steps in Granatum Fig. . In the metadata sheet. A metadata sheet is a table describing the groups, batches, or other properties of the samples in the corresponding expression matrix. Users may upload multiple matrices sequentially. Currently, Granatum accepts either human or mouse species for downstream functional analysis. After uploading the input files, users can preview the matrix and metadata tables to validate that the dataset is uploaded correctly.Granatum accepts one or more expression matrices as input. Each expression matrix may be accompanied by a Samples obtained in batches can create unwanted technical variation, which confounds the biological variation . It is tTo show that median alignment can effectively remove the batches, we randomly select half of the cells in K-dataset and multiply the expression levels by 3, thus creating two artificial batches 1 and 2. The PCA plot shows that, due to the batch effect, cells of the same type are separated by batch . Granatum includes four commonly used normalization algorithms: quantile normalization, geometric mean normalization, size-factor normalization , 43, andThe box plots allow observation of various degrees of stabilization Fig. . The oriA unique challenge in analyzing scRNA-Seq data is the dropout events, which introduce large number of false zeros in the expression matrix . These eDue to high noise levels in scRNA-Seq data, Brennecke et al.  recommenk) ranging from 2 to 10, and determine the best number as the elbow-point k, the starting point of the plateau for explained variance (EV). If Hclust is selected, a pop-up window shows a heatmap with hierarchical grouping and dendrograms.Clustering is a routine heuristic analysis for scRNA-Seq data. Granatum selects five commonly used algorithms: non-negative matrix factorization , k-meansk cluster labels on the samples  on the correlation t-SNE plot, using K-dataset. The generated clusters perfectly correspond to the original cell type labels in this case.Next, the two unsupervised PCA and correlation t-SNE plots superimpose the resulting http://www.genecards.org/) [After the clustering step, Granatum allows DE analysis on genes between any two clusters. It currently includes four commonly used DE methods, namely NODES , SCDE 3, Limma , and edgds.org/) . The “DoNext, gene set enrichment analysis (GSEA) with either KEGG pathways or Gene Ontology (GO) terms , 51–53 cTo evaluate the differences between Granatum and a similar graphical scRNA-Seq pipeline, ASAP , we compTrypanosoma brucei passes through the blood–brain barrier and causes neurological damage by inducing cytokines. Despite the differences, some signaling pathways are identified by both pipelines with known associations with tumorigenesis, such as the PPAR signaling pathway [Both pipelines agree on most DE genes called Fig.  but each pathway  and the  pathway .Unlike ASAP, SAKE, and SCRAT, Granatum implements a protein–protein interaction (PPI) network to visualize the connections between the DE genes Fig. . By defaLastly, Granatum has included the Monocle algorithm , a widelThe field of scRNA-Seq is evolving rapidly in terms of both the development of instrumentation and the innovation of computational methods. However, it becomes exceedingly hard for a wet-lab researcher without formal bioinformatics training to catch up with the latest iterations of algorithms . This baGranatum is our attempt to fill this void. It is, to our knowledge, the most comprehensive solution that aims to cover the entire scRNA-Seq workflow with an intuitive graphical user interface. Throughout the development process, our priority has been to make sure that it is fully accessible to researchers with no programming experience. We have strived to achieve this by making the plots and tables self-explanatory, interactive, and visually pleasant. We have sought inputs from our single-cell bench-side collaborators to ensure that the terminologies are easy to understand by them. We also supplement Granatum with a manual and online video that guide users through the entire workflow, using example datasets. We also seek feedback from community via Github pull-requests, emails discussions, and user surveys.Currently, Granatum targets bench scientists who have their expression matrices and metadata sheets ready. However, we are developing the next version of Granatum, which will handle the entire scRNA-Seq data processing and analysis pipeline, including FASTQ quality control, alignment, and expression quantification. Another caveat is the lack of benchmark datasets in the single-cell analysis field currently whereby the different computational packages can be evaluated in an unbiased fashion. We thus resort to empirical comparisons between Granatum and packages such as ASAP. In the future, we will enrich Granatum with capacities to analyze and integrate other types of genomics data in single cells, such as exome-seq and methylation data. We will closely update Granatum to keep up with the newest development in the scRNA-Seq bioinformatics field. We welcome third-party developers to download the source code and modify Granatum, and will continuously integrate and improve this tool as the go-to place for single-cell bench scientists.We have developed a graphical web application called Granatum which enables bench researchers with no programming expertise to analyze state-of-the-art scRNA-Seq data. This tool offers many interactive features to allow routine computational procedures with a great amount of flexibility. We expect that this platform will empower bench-side researchers with more independence in the fast-evolving single cell genomics field.


The effect of candidate genes identified by in silico analysis on survival was then assessed using biopsy specimens taken at the time of initial diagnosis of localized and metastatic PACa. We identified PEG10 as a candidate biomarker. Data from clinical samples suggested that increased expression of PEG10 at the time of initial diagnosis was linked to shorter survival time. Interestingly, PEG10 overexpression also correlated with expression of chromogranin A and synaptophysin, markers for neuroendocrine prostate cancer, a type of treatment-resistant prostate cancer. These results indicate that PEG10 is a novel biomarker for shorter survival of patients with PACa. Also, PEG10 expression at the time of initial diagnosis may predict focal neuroendocrine differentiation of PACa. Thus, PEG10 may be an attractive target for biomarker-driven cancer therapy. Thus, bioinformatics-to-clinic sequential analysis is a valid tool for identifying targets for precision oncology.Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. To identify target molecules implicated in shorter survival of patients with PACa, we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step  Precision oncology, also called biomarker-driven therapy, has greatly improved clinical outcomes in recent years. Biomarker-driven therapies such as trastuzumab for HER2-positive breast cancer and imatinib for chronic myeloid leukemia , 2 highlProstate adenocarcinoma (PACa) is one of the most common cancers in men. In the U.S., the 5 year relative survival rate of early stage PACa is >99% ; yet PACTo identify biomarkers of poor prognosis in PACa, we developed a bioinformatics-to-clinic sequential analysis approach and used it to identify a candidate biomarker linked to shorter survival. To evaluate the predictive power of our analytical approach, we examined biopsy samples to see if expression of our identified gene, PEG10, at the time of initial diagnosis affected clinical outcome. PEG10 was also of interest because a recent report implicates it in neuroendocrine differentiation of PACa . TherefoTo identify biomarkers that predict shorter survival of patients with PACa, we used two analytical procedures based on the TCGA dataset for localized PACa, followed by validation in clinical samples Table . First, The TCGA dataset was divided into groups based on RFS at 1, 3, or 5 years Figure . The numWe calculated the RFS HR for each gene that was overexpressed z-score ≥1) using a Cox proportional hazard model. Before calculating the HR, the number of cases showing overexpression of each gene was examined. If a gene was overexpressed in less than 5% of the total cohort, it was excluded from further analysis because a HR calculated from a disproportionate distribution is not reliable. Thus, 2527 genes (13.3% of the identified genes) were excluded. Next, we identified genes responsible for shorter RFS (HR >1) among genes with a significant p-value for RFS HR . Thus, increased PEG10 expression was chosen as the candidate marker for shorter RFS in those with localized PACa with a Gleason score ≥8.The definition of overexpression in bioinformatics analysis is complex. It is not known whether z-score ≥1 is the most appropriate definition of overexpression. Additionally, we excluded genes overexpressed in less than 5% of the population. However, this exclusion may also result in inaccurate estimations. Therefore, we investigated whether increased expression of PEG10 is linked to RFS using a different definition of increased expression: a z-score ≥1, ≥1.5, or ≥2, without the exclusion of genes overexpressed in less than 5% of the population. Again, increased expression of PEG10 was identified as a biomarker for shorter RFS when using the dataset based on localized PACa .We investigated the impact of PEG10 overexpression (z-score ≥1) on RFS using the TCGA dataset, different statistical models, Kaplan-Meier analysis, and the log-rank test. Overexpression of PEG10 was linked to shorter RFS  Figure . The effTo investigate whether expression of PEG10 protein in biopsy samples taken at the time of initial diagnosis was also linked to RFS of those with localized or metastatic PACa, we collected PACa biopsy samples from patients diagnosed at St. Marianna University Hospital between 2003 and 2014 Table . IncreasA recent report suggests that PEG10 expression is increased in PACa with focal neuroendocrine differentiation . Thus, wTo apply biomarker-driven cancer therapy to PACa, we developed a method called bioinformatics-to-clinic sequential analysis. A bioinformatic approach can be a useful tool; however, it has several limitations. One such limitation is that data that fit the study model perfectly are often not available. In the current study, the dataset required for our model needed to include not only localized, but also metastatic, PACa. A dataset including both localized and metastatic PACa was available, but it contained only 21 cases. Thus, we used a TCGA dataset that included only localized PACa; however, the sample number (201 cases) was acceptable for bioinformatics analysis. Next, we used the clinical samples that included both localized and metastatic PACa to ascertain whether biomarkers of a poor prognosis identified by the bioinformatics approach  were applicable to the clinical samples from patients with localized or metastatic PACa.in silico analysis), the definition of “shorter survival” is complex. To identify DEGs in this study, we divided the dataset according to 1, 3, or 5 year survival. It is still not known if these criteria are the most appropriate. This is an important consideration because extraction of genes that emerge in the differential settings may avoid under- or over-diagnosis. Another issue related to DEGs is that DEGs are identified by a comparison of the average value for each group. If a particular gene has a strong link to survival when overexpressed, but overexpression in the population is low, the average value of the gene’s expression in a cohort of shorter survival will not be high. Thus, identification of DEGs may not always give the most accurate estimation of genes relevant to survival time. The second step, a calculation of HR, may also be problematic. In this study, we calculated HR in several definitions of increased expression. Even in the different settings, increased expression of PEG10 was linked to shorter survival. However, it is still not known which definition is appropriate. As described above, there are problems with in silico analysis; therefore, to compensate for these we developed the bioinformatics-to-clinic sequential analysis approach to check for concordance between different analytical procedures. Indeed, our results indicate that our approach is reliable and provides a high-quality outcome.When identifying DEGs . To identify DEGs, raw counts were entered into the TCC package in R [http://www.cbioportal.org) were used to calculate the HR [Gene count data from PACa TCGA samples (RNA sequencing) were downloaded from the Genomic Data Commons Data Portal ; anti-chromogranin A , and anti-synaptophysin .Immunohistochemistry (IHC) was performed as described previously . BrieflyExperiments with clinical samples were approved by the St. Marianna University clinical ethics committee, and all participants gave informed consent .


With the development of high-throughput technology, the researchers can acquire large number of expression data with different types from several public databases. Because most of these data have small number of samples and hundreds or thousands features, how to extract informative features from expression data effectively and robustly using feature selection technique is challenging and crucial. So far, a mass of many feature selection approaches have been proposed and applied to analyse expression data of different types. However, most of these methods only are limited to measure the performances on one single type of expression data by accuracy or error rate of classification.In this article, we propose a hybrid feature selection method based on Multiple Kernel Learning (MKL) and evaluate the performance on expression datasets of different types. Firstly, the relevance between features and classifying samples is measured by using the optimizing function of MKL. In this step, an iterative gradient descent process is used to perform the optimization both on the parameters of Support Vector Machine (SVM) and kernel confidence. Then, a set of relevant features is selected by sorting the optimizing function of each feature. Furthermore, we apply an embedded scheme of forward selection to detect the compact feature subsets from the relevant feature set.We not only compare the classification accuracy with other methods, but also compare the stability, similarity and consistency of different algorithms. The proposed method has a satisfactory capability of feature selection for analysing expression datasets of different types using different performance measurements.The online version of this article (doi:10.1186/s13040-017-0124-x) contains supplementary material, which is available to authorized users. With the development of transcriptomics research, especially the widely used high-throughput microarray chip and RNA sequencing technology, a large number of transcriptome data have been obtained by measuring the expressions of genes or miRNAs simultaneously. Researchers can acquire these different expression data from several public databases, such as Gene Expression Omnibus (GEO) , StanforFiltering methods, which are independent of the classifier, select relevant features only dependent the intrinsic properties of expression data. Glaab et al. applied an ensemble filter method which combines several selection schemes to an ensemble feature ranking 2. In a practical application, different kernels can combined. The features are selected and evaluated using 10-fold Cross-Validation (CV) on a variety of datasets through different feature selection methods including SVM-RFE 2 are conducted. The results of mean effectiveness and max effectiveness on three types of datasets are shown  of dataset D and another list of feature sets M2 on samples Ω1, Ω2, …, Ωk. Let M1 is better than M2 in union stability of feature space. For every two samples Ωi, Ωj ∈ {Ω1, Ω2, …, Ωk}, let M1 is better than M2 in independent stability of feature space.The stability of feature space generated from a feature selection algorithm reflects the robustness of the method on different samples of the same type of data . For a ln of feature sets union stability and independent stability according to the process mentioned above.In our verification, we set the union stability on three types of datasets through different methods are shown  is a kernel which can map the feature space to a high dimensional space. There are several popular kernels, such as linear kernel K = , radial basis function kernels K = d and inhomogeneous polynomial kernels K = [ + 1]d. After obtaining the α, we can predict the label of a new data point by the following formula d.Firstly, we apply MKL to select the relevant feature set. To implement MKL approach, we select the SimpleMKL method in  to obtaiThen the optimal objective function is defined as follows:J value for each feature from the total feature set S in the process of optimizing W via J list for features list is computed to measure the relevance between features and samples. Finally, we sort the J list in ascend and obtain the ranked features list Sr. Then, the top n* features are selected and the feature set Using SimpleMKL, we can obtain the Z is the set containing the selected features, such as Z = {f1, f2, …, fn}. In this article, the JZ is calculated by using SimpleMKL method [An embedded scheme of the sequential forward selection is utilized to search the compact feature subsets from the relevant feature set ssifiers . We use L method , which sL method .r features from J0 is set to + ∞ and the subset Z is set to empty. We search each feature in the feature subset, such as f1, f2, …, fn, and compute the objective functions fi which generates the largest Z. Then, the algorithm selects the feature fj which generates the largest ΔJ reduction from the set Z. The process of incremental selection will repeat until ΔJ ≤ 0 or the given iterations. The process of selecting compact feature subsets is defined (See Additional file Then, a forward process is used to to select the subset with With the development of high-throughput microarray chip and RNA sequencing technology, we can obtain a large number of expression data with different types. The researchers can acquire these data from several public databases, such as GEO, SMD, ArrayExpress and TCGA. However, because the transcriptomics experiments have high cost, most of these data have samples with small size and tens thousands genes or hundreds miRNAs. How to extract informative features from expression data effectively and robustly is a challenging and crucial problem for expression data analysis. Feature selection technique had been widely applied to select a subset of relevant features and eliminate redundant, irrelevant and noisy features.In general, most feature selection methods can be divided into three categories: filter, wrapper and embedded. Filter methods independent of the classifier, select relevant features only relying on the intrinsic properties of expression data. Filter methods contain two subclasses: univariate and multivariate. Univariate methods are processed by filtering single feature and multivariate methods are used to select features by considering combination of features. The advantages of univariate methods are fast, scalable and independent of the classifier, and the disadvantages of these methods are thoughtlessness of feature dependencies and ignoring the interaction with the classifier. The advantages of multivariate methods contain: feature dependencies, independent of the classifier and better computational complexity than wrapper methods. But the multivariate methods are slower and less scalable than univariate methods. Wrapper approaches, which can be divided into deterministic and randomized types, generate the scores for features and select them based on the classifier. The deterministic methods, which are simple, have less computational complexity and more risk of over fitting than randomized methods. But they are more prone to get a result of local optimum than randomized methods. Embedded approaches, which have lower computational complexity than wrapper methods, select optimal feature subset based on classifier construction in the combined space of feature subsets and hypotheses.Most of above methods construct the feature selection model on individual expression data simply, and they rarely consider the effectiveness and stability on expression data from different type of expression data. In order to overcome the disadvantages of above methods, a hybrid feature selection method based on multiple kernel learning is proposed. We evaluate performance of method on expression dataset of different types. Except for comparing the classification accuracy with other methods, we also compare the performances of different algorithms through measuring the stability, similarity and consistency. The experimental results show that the proposed method has a satisfactory capability of feature selection for different expression datasets analysis.The kernel methods and other machine learning methods always have the problem of overfitting, especially in small sample size. And, one of characteristics of gene expression data is high dimensional and small sample size. There are commonly used methodologies to avoid overfitting on machine learning: Regularization, Cross-Validation, Early Stopping and Pruning. The regularization part adds a cost term for bringing in more features with the objective function. Hence, regularization can make the coefficients for many variables to zero and hence avoid the overfitting. Cross validation can identify the methods, which may have poor performance generating by overfitting training on several datasets. The methods of early stopping try to prevent overfitting by controlling the number of iterations on iterative method. Pruning methods, which remove the nodes with little predictive power, are used for several methods based on tree. In this article, we used regularization and sequential forward selection method to decrease the influence of overfitting on small sample size. With the lower price of Mircoarray and RNA sequencing, the samples are more and more obtained from individual experiment, such as the new experiment of RNA sequencing on single-cell, which can handle more than 4000 samples . So, in