The shinySISPA can be accessed from Unlike gene set profiling, sample profiling is a challenge due to the heterogeneity between, and within the tumor patient samples. Identification of homogenous groups of samples or molecular subtypes is commonly approached using clustering methods  and the shiny web application framework is implemented using the Shiny R package (SISPA is written in the R programming language (http://shinygispa.winship.emory.edu/shinySISPA/) running the Shiny Server program designed to host R Shiny applications. This tool has been extensively tested on Windows 7 and Mac Pro 10 operating system with firefox and google chrome browser. Given a dataset of 377 samples and 16 genes under the two-feature analysis, it took three seconds to obtain shinySISPA defined sample groups and less than a second to generate the waterfall plot. The time it takes to generate sample profile diagnostic plots depends on the number of genes in a set; it took less than 10 seconds for 16 genes in both sets of a two-feature analysis. As a note, speed at which results are generated is also dependent on the internet connectivity.The tool is hosted on a 64bit CentOS 6 server  and thus, a single-feature analysis refers to use of a single data type, while a two-feature uses a combination of two data types and so forth.(1)Uploading the data. User inputs the data for each feature containing the genes and samples of interest. The same samples are required for each feature, though the gene sets may differ between features.(2)Specifying a molecular profile. A molecular profile is a series of increasing (“up”) or decreasing (“down”) genomic changes within each feature. In(3)Selecting the number of breaks to define sample groups. User can specify the change point detection method ((4) method .The results are output in four separate tabs:Input Data. Summarizes the user input data in terms of the input number of genes, number of samples, and box plot distribution by data type.(1)SISPA Results. Outputs the table of defined sample groups with their gene set enrichment score for the selected analysis type and molecular profile of interest. The scatter plot on the right displays all the change points detected within the data-set, samples falling in the topmost change point are the samples with the profile activity. The frequency plot at the rightmost bottom represents the distribution of the number of samples with and without the profile activity.(2)Waterfall Plot. To visualize the sample groups that correlate with the profile of interest. Samples with the profile activity have the highest score and are shown in orange filled bars, while samples without the profile activity are shown with grey-filled bars.(3)Sample Profile. Represents the diagnostic plots to visualize the distribution of the user-input data overall by the identified sample groups. It also allows the users to view data distribution for a selected gene in the set within each data type to assess what genes in particular satisfy the profile versus samples without profile.(4)All results generated during the process are directly downloadable on the user’s local computer. A detailed manual with tool settings are provided in theMultiple Myeloma Research Foundation (MMRF) CoMMpass study, we downloaded data from 377 newly diagnosed patients at pre-treatment with available clinical outcomes, RNA-Seq expression, and DNA-copy number variations from theMMRF Research Gateway portal. Based on our two-feature analysis, 7 of the 370 MM patients were defined with profile activity (P<0.0001) associated with poor survival as compared to the 370 samples without the profile activity ).We applied shinySISPA to profile newly diagnosed multiple myeloma (MM) patients for decreased gene expression and copy number based on a GISPA -derived gene set characterizing the IgH translocation in the MM cell lines  This work proposes a web-based tool allowing to identify a subgroup of samples that share similar molecular profiles based on gene expression information and visualize the results in various plots. It is written in R program language. This tool would be useful for easily dealing with clinical data and genomic data. One weakness is to only define two sample groups but not multiple groups.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors clarified and answered my previous concerns, and I have no further comments.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors introduce a Shiny application that are developed as a user-friendly tool for conducting omics-data analysis with their published methodology work. The article provides detailed description of the inputs and outputs for the shiny application, and shows working example for the use of this application. This article is index-able with some necessary modifications.With a glimpse on the methodology paper, I found the terms such as “molecular profile”, “feature” are defined misleadingly in this article.What is a change point model?Before introducing the shiny application, the authors should provide a brief summary of their methodology work, including clear definitions of important terminologies and a description of their model. It would greatly help the readers to understand the contents followed subsequently.For the input element (4), where to select the number of breaks? Is it the max Q allowed? What is the max Q? What are the options for “Changes Using” in the bottom right of Figure 1?For the output element (2), it is better to include a figure with the description.Why GISPA is used? What’s the relation of GISPA and SISPA?How many genes are used? Is it 16?Where are the 7 patients in Figure 1? Are they the orange bars?What is “using change point v2.2.2”?P<0.0001) associated with poor survival as compared to the 300 samples without the profile activity ).” Previously mentioned, there are 377 patients. Why are 70 samples missing?“We found seven samples with profile activity to be significantly ”, it is hard to locate where it is referring to since there are many grey colors in the figure. Also, please add labels to the subfigures that are referred in the text. The following are my major comments.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Referee Report 16 Apr 2018Yun Zhang, Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA Approved with ReservationsThe article provides detailed description of the inputs and outputs for the shiny application, and shows working example for the use of this application. This article is index-able with some necessary modifications.The authors introduce a Shiny application that are developed as a user-friendly tool for conducting omics-data analysis with their published methodology work.The following are my major comments.1) Before introducing the shiny application, the authors should provide a brief summary of their methodology work, including clear definitions of important terminologies and a description of their model. It would greatly help the readers to understand the contents followed subsequently.Response: We have provided a reference to our methods paper that includes detailed information on the SISPA approach.  In this paper, our focus is upon introducing the application of the method in terms of tool development and implementation by providing detailed examples and information on data input and output/results.  Considering this focus, along with space constraints, we have opted to not repeat the already published method description and instead, reference it. With a glimpse on the methodology paper, I found the terms such as “molecular profile”, “feature” are defined misleadingly in this article.Response: We have intentionally used the terms “molecular profile” and “feature” in the same context as in the published methods papers, whereby “molecular profile” refers to change of either increase (“up”) or decrease (“down”) and “feature” refers to a specific data type .  We have also provided examples of the term “molecular profile” to further clarify the context.In this paper:Selecting the analysis type)      “A “feature” corresponds to a specific data type  and thus, a single-feature analysis refers to use of a single data type, while a two-feature uses a combination of two data types and so forth.”       “A “molecular profile” is a series of increasing (“up”) or decreasing (“down”) genomic changes within each feature…”  that references a specific data type  as a feature.”    “The Cancer Genome Atlas (TCGA) nomenclature , pp. 1–19.http://www.jstatsoft.org/v58/i03/.Killick R and Eckley IA (2014). “changepoint: An R Package for Changepoint Analysis.”changepoint: An R package for changepoint analysis. R package version 2.2.2,https://CRAN.R-project.org/package=changepoint.Killick R, Haynes K and Eckley IA (2016).2) For the input element (4), where to select the number of breaks? Is it the max Q allowed? What is the max Q? What are the options for “Changes Using” in the bottom right of Figure 1?Response: Yes, the allotted maximum number of breaks is specified using the “Max Q Allowed”.et al., 2016) to find the optimal break points within the estimated profile sample score . The changes can be found using mean(“mean”), variance (“var”) or both (“meanvar”) with the user-specified changepoint method  given the allotted maximum number of change points . Note that the number of change points identified may differ for the same dataset depending on the change point R package version installed on the system. Currently we are running changepoint version 2.2.2 on our hosting server...”“In input element (4), users can modify the Changepoint Input  For the output element (2), it is better to include a figure with the description.Response: The output elements  screenshot including the figures are explained in detail in the supplementary file 1. Please see pg# 7-11 under “Result” Section.Why GISPA is used? What’s the relation of GISPA and SISPA?Response:4) In the “Use case” section,   GISPA  is a method designed to define gene sets with similar, a priori specified molecular profile. While, SISPA  is a method designed to define sample groups with similar gene set a priori specified molecular profile. Both GISPA and SISPA method are published in Nucleic Acid Research .   GISPA was used to identify genes with decreased expression and decreased copy change molecular profile in a multiple myeloma cell line with IgH translocation. This gene set is published in the methodology paper Nucleic Acid Research .   Here, we extracted RNA-seq expression, and copy number change data for GISPA derived gene set characterizing the IgH translocation on 377 newly diagnosed patients enrolled in the coMMpass clinical trial to define samples with a similar gene set profile, i.e., decreased expression with copy loss. This example data is provided with this paper.  Pg# 4 last paragraph under “Use case” describes the use of application of SISPA using GISPA derived gene sets.       2. How many genes are used? Is it 16?Response: Yes. The number of genes in the expression and copy number variation data is 16.       3. Where are the 7 patients in Figure 1? Are they the orange bars?Response: The patients with and without profile activity are highlighted in “Samples Supporting the Gene Set Profile” labeled section of the Figure 1. Yes, the 7 patients are highlighted in orange-filled bars.       4. What is “using change point v2.2.2”?Response: “using change point v2.2.2”? means that we have used changepoint R package version 2.2.2.P<0.0001) associated with poor survival as compared to the 300 samples without the profile activity ).” Previously mentioned, there are 377 patients. Why are 70 samples missing?       5. “We found seven samples with profile activity to be significantly  to compare survival curves of the identified two sample groups for downstream clinical interpretation. We found seven samples with profile activity to be significantly (P<0.0001) associated with poor survival as compared to the 370 samples without the profile activity ).”“Based on our two-feature analysis, 7 of the 370 MM patients were defined with profile activity  by identifying changes in variance using change point v2.2.2. Furthermore, we used CASAS ( Rupji5) For the reproducibility of the work, please upload a default dataset in the shiny application, so that the readers/users can replicated the analysis for the first time.Response: All users can access and analyze the example dataset  used in the paper by choosing the “Example data” from the Upload Input option on the web-interface.  The data is also available to download from GitHub (https://github.com/BhaktiDwivedi/shinySISPA). Users are able to obtain the same exact results, i.e., samples with and without profile activity using the current default settings implemented in shinySISPA web tool.Also, I provide my minor comments below.1.  Please consider a thorough revision of the English writing for this scientific article. There are a number of grammatical errors, and sentences do not flow smoothly. Please avoid using colloquial words in scientific writing.Response: We have reviewed the manuscript and do not identify any such problem. If the reviewer still feels strongly about it, we kindly request specific examples to be cited from the text.2. Please modify the legend of Figure 1. In the brackets “(shown in grey)”, it is hard to locate where it is referring to since there are many grey colors in the figure. Also, please add labels to the subfigures that are referred  in the text.Response: We have addressed and incorporated these changes. Please see updated Figure 1 and Figure legend.Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? PartlyAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? NoIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? PartlyAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Acute myeloid leukemia (AML) is a disease with marked molecular heterogeneity and a high early death rate. Our aim was to investigate an integrated Gene expression, Mirna and miRNA-mRNA Interactions (GMI) signature for improving risk stratification of AML.N = 147). Univariate and multivariate Cox proportional hazards regression analyses were applied to evaluate the prognostic value of GMI signature.We identified differentially expressed genes by pooling a large number of 861 human AML patients and 75 normal cases. We then used miRWalk to identify the functional miRNA-mRNA regulatory module. The GMI signature based random survival forest (RSF) prognosis model was developed from training data set and evaluated in independent patient cohorts from The Cancer Genome Atlas (TCGA) dataset (p = 0.019). Similar results were obtained with reversed training and testing datasets . The GMI signature score contributed more information about recurrence than standard clinical covariates.We identified 139 differentially expressed genes between normal and abnormal AML samples. We discovered the functional miRNA-mRNA regulatory module which participate in the network of cancer progression. We named 23 differentially expressed genes and 16 validated target miRNAs as the GMI signature. The RSF model-based scores separated independent patient cohorts into two groups with significantly different overall survival  contains supplementary material, which is available to authorized users. CEBPA) [FLT3-ITD) [NPM1) [GATA binding protein 2(GATA2) mutations [WT1) mutations [Acute myeloid leukemia (AML) is a malignant disease of the bone marrow and typically represents functionally and phenotypically various cells in the same patient. Gene mutations identified distinct cytogenetically defined subsets of AML and unraveled the heterogeneous disorder in terms of genetic basis. Karyotype , mutatioCEBPA) , 3, inteLT3-ITD) , recurre) [NPM1) , GATA biutations , and Wilutations  are relautations . Hence, Several methods have been developed for the analysis of multiple molecular data to identify cancer-driven signatures and predict clinical outcome. Previous studies have demonstrated that microRNA signatures were identified to be associated with cytogenetics, prognosis and therapeutic targets in AML –11 and mTo address these questions, we collected 25 publicly available gene expression data sets containing 861 human AML patients and 75 normal cases. By pooling such a large amount of data, we aimed to identify differentially expressed genes for describing different gene expression patterns between normal samples and AML samples. Furthermore, we wanted to discover the functional regulatory networks for identifying potential regulations between mRNA and miRNA in biological processes. Based on the observation that miRNA-mRNA interactions were biologically relevant, we hypothesized that a functional miRNA-mRNA regulatory module could better represent underlying biological characteristics and may lead to a more powerful prognosis model. Moreover, we tried to develop Random survival forest (RSF) model using The Cancer Genome Atlas (TCGA) dataset based on prognostic miRNA/mRNA signatures. The results demonstrated that the model-based patient stratification provided useful predictive information for AML patient subgroups.Figure https://www.ncbi.nlm.nih.gov/pubmed) with the terms “acute myeloid leukemia”, “gene expression”, “prognosis” and “signature” for published articles. This search retrieved the relevant Gene Expression Omnibus (GEO) database for further analysis. Raw microarray data were obtained from publicly available AML gene expression datasets in NCBI GEO. We collected AML samples of 25 gene expression datasets with Affymetrix Human Genome U133 plus 2.0 microarrays. The normalization was conducted on all samples in a single set using Robust MultiChip Analysis (RMA) algorithm [We searched PubMed . We collected the mRNA sample data , the miRNA sample data  and the clinical sample data (N = 993) of AML from TCGA database. Log transformation (base 2) was used to re-scale mRNA and miRNA expression, followed by a Z-score transformation. Transcriptomic data of GMI signature were obtained and processed from TCGA. Specifically, 147 AML samples were selected based on the present expression values of 23 gene expression signatures from 187 RNA-seq expression dataset and 16 miRNAs from 301 miRNA expression dataset.Transcriptomic data from the AML cohort were downloaded from the TCGA site (N = 74) and TCGA Part Two  cohorts for further analysis. TCGAPO cohort includes 41 samples  and 33 samples (dead) for overall survival (OS) of patients. At the same time, TCGAPT cohort contains 33 samples  and 40 samples (dead) for OS of cancer patients. Each dataset was used as a training-set in turn and developed models were evaluated against the other dataset. Table Data collection and process was shown in Fig. t-tests and corrected by Benjamini-Hochberg [p-values < 0.01. Adjusted statistical significance was then set at q-values < 0.01 with FDR correction for multiple testing where relevant. Fold Change value of 2.5 was further used as a cut-off to identify up- and down-regulated genes.To identify differentially expressed genes, differential expression analysis was used to assess differences in gene expression between an experimental group and a control group assessed by two-tailed Student’s Hochberg . The falhttp://mirwalk.umm.uni-heidelberg.de/) [miRDB and TargetScan for miRNAs target selection. The functionally correlated miRNA-mRNA regulatory module was then identified and constructed for further analysis.To identify miRNA-mRNA interactions, we used the significantly differentially expressed genes (mRNAs) as seeds and identified the target-validated miRNAs from the miRWalk2.0 database (erg.de/) . To invehttp://www.linkedomics.org) contains multiple molecular data and clinical data for different cancer types from the TCGA project, which systematically interpret and explore the complex relationships between the vast amount of clinical and molecular attributes [http://gepia.cancer-pku.cn/) was used for efficiently analyzing the RNA sequencing expression data from the TCGA data [The LinkedOmics database  are formed and the one with the best C-index value is identified as the optimized parameters. The C-index represents a probability of the concordance between predicted and observed survival, which is a typical metric for quantifying the predictive ability of a survival model. The developed RSF prognosis model based on the optimal parameters was then evaluated on the independent dataset where the RSF-based score was derived for each sample.We used random survival forest (RSF) method for developing a prognosis model . An R imp value less than 0.05 was considered statistically significant.The calculated C-index values evaluated the association between the RSF-based score and real prognosis of the patients. Standard Kaplan–Meier survival curves were generated for different risk patient groups on the basis of the RSF-based scores. The median score was used to stratify patients into high-risk and low-risk score groups, and the log-rank test was utilized to assess the survival difference between two different risk groups. The statistical test was two-sided and the estimated p-value) as a function of the log2 fold-change in the gene expression of AML.In the microarray analysis, 139 genes were found to be differentially expressed between 75 normal bone marrow samples and 861 AML samples  to identify the correlated miRNAs which may target them. We then identified 37 mRNAs with matched 116 miRNAs  including ABLIM1, ATXN1, CDK6, IGF2BP2, IGF2R, PLXNC1, and hsa-mir-107  revealed patient stratification with statistical significance on overall survival analysis. Interestingly, we observed that ABLIM1 and ATXN1 were significantly associated with overall survival for these different prognosis prediction tools.Similarly, we applied GEPIA for the prognosis of the GMI signature in AML from TCGA project. The patients were stratified into different prognosis subsets in a sample by determining the expression level of markers  is used to measure the increase (or decrease) in prediction error for the forest ensemble when a variable is randomly “noised-up” . VIMP evntree = 10, mtry = 20) were selected to build the RSF prognosis model. RSF-based scores were then calculated for individual sample. The calculated RSF-based scores displayed 78% concordance (C-index = 0.78) in the light of the real survival data in the training cohort. Based on the RSF-based scores, the samples were divided into a “high-risk” group with above-median scores and a “low-risk” group with below-median scores. As shown in Fig. p = 1.263e-11). The low-risk group had 3-year overall survival rate of 100% and the high-risk group possessed 3-year overall survival rate of 26%. The developed RSF prognosis model was applied for the independent test dataset and predictive performance was evaluated using the cohort TCGAPT. As shown in Fig. p = 0.019) than the low-risk group. The overall survival at 3 years between low-risk and high-risk group was 63 and 35% respectively.In the RSF prognosis model development, five-fold cross validation was used to optimize the parameters for the RSF algorithm, and a full model based on the complete dataset was developed using the optimal parameters. The best performing parameters (p = 2.403e-12). The 3-year overall survival rate was 81% in the low-risk group compared with 17% in the high-risk group. The TCGAPT-derived RSF prognosis model was test on independent test dataset TCGAPO. As shown in Fig. p = 0.038). The overall survival at 3 years was 71% for the low-risk group in comparison with 57% for the high-risk group. These results confirmed that the GMI signature-based prognosis models could predict AML recurrence.To further evaluate the effectiveness of the GMI signature, we reversed the training and testing datasets by constructing RSF prognosis model based on TCGAPT dataset and testing their performance on TCGAPO dataset. Analogous results were achieved as shown in Fig. p = 0.019) in the univariate analysis and still preserved the significance (p = 0.02) in the multivariate analysis. As can be seen from the Table p < 0.05), demonstrating the potential value of two risk factors. The GMI signature score was more highly prognostic with overall survival than individual clinical variables and risk factors when the log-rank p values were observed and compared. In summary, the GMI signature score represented a prognostic signature strongly associated with a higher risk of tumor recurrence.Samples of TCGAPT were analyzed using univariate and multivariate analyses with Cox’s proportional hazards regression to evaluate the prognostic value of the GMI signature score in combination with individual clinical variables  and risk factors . The GMI signature-based RSF model from TCGAPO was developed to test TCGAPT and achieved the GMI signature score of the samples. Univariate and multivariate Cox’s regression analyses were summarized in Table miR-449 causes down-regulation of oncogene CDK6 which stimulates cell proliferation in gastric cancer [miR-448 suppresses proliferation and invasion by regulating IGF1R in colorectal cancer cells [miR-378 family members target IGF1R, a key signaling molecule in rhabdomyosarcoma [ABLIM1 and ATXN1 were significantly associated with overall survival for AML. In fact, loss of CIC or ATXN1L modulates sensitivity to MEK inhibition in RAS-mutant cancers [SRGN is crucial for regulating actin cytoskeletal organization associated with cell migration for cancer metastasis [SRGN is accompanied by down-regulation of ABLIM1, LIMA1, CFL1, RAC1, RAC2 and RHOA, concomitant with decreased cell motility [MicroRNAs play crucial regulatory roles in mediating mRNA degradation with a sequence-specific manner . Most prc cancer , miR-448er cells , and miRosarcoma . Based o cancers . It demotastasis . Interesmotility .p = 5.325e-3) and miRNA signature based RSF prognosis model . The RSF prognosis model was used for the independent test dataset TCGAPT and predictive performance was measured. No significant difference between the two different risk groups was evident for mRNA signature  and miRNA signature . These results demonstrated that RSF prognosis model based on mRNA signature or miRNA signature did not result in comparable performance as that from the GMI signature. The miRNA-mRNA interactions play an important role for achieving the predictive performance.The RSF prognosis models based on mRNA or miRNA expression signatures from GMI signature were developed in the cohort TCGAPO. There was a significant difference between two different risk groups with mRNA signature based RSF prognosis model  are identified with MiRWalk. The RSF prognosis models based on 11 genes and 31 miRNAs were developed in the cohort TCGAPO and independently tested in the cohort TCGAPT. There was a significant difference between two different risk groups in the training cohort . Further test revealed that the difference between the high-risk and low-risk group was significant . Thus, 11 genes and 31 miRNAs could predict recurrence risk and inform patient prognosis in AML.It has previously been observed that a 17-gene stemness score (LSC17 signature) could predict recurrence risk in AML patients . The 11 − 4, 10− 3, 10− 2, 10− 1, 100, 101, 102, 103, 104}. Five-fold cross validation was used to identify the optimized parameters according to the C-index value. In the TCGAPO dataset, the calculated SSVM scores showed 73% concordance (C-index = 0.73) with the real survival data. The significant differences were observed in overall survival analysis between the high-risk group and low-risk group . When applying SSVM model on the validation dataset TCGAPT, scores showed 56% concordance (C-index = 0.56) when compared to the real survival data. The patients were separated into two different groups with overall survival . The results indicated that RSF model achieved clearly superior performance compared to SSVM model.We developed Survival Support Vector Machine (SSVM) model based on the GMI signature for survival analysis in comparison with RSF method. Two parameters c and σ were implemented in the SSVM model, and different parameter combinations were formed for model development from each parameter among the candidate set {10For the prioritized gene expression signatures, lack of concordance is a common observation in clinical trial , 65. HowWe used the pooled analysis of gene expression profiling data from 861 patients to identify differentially expressed gene expression signatures. We applied miRWalk approach to integrate multiple types of transcriptomic data and discover the functional miRNA-mRNA regulatory module. In the development of prognosis model, the GMI signature-based RSF model was used to derive the prognostic risk score and accordingly stratify the patients into a high-risk and low-risk group. The results demonstrated that the RSF prognosis model measured underlying biological characteristics which are predictive of clinical outcomes and informed the treatment in AML. In conclusion, the GMI signature based RSF prognosis model can help facilitate rational design of clinical studies by patient stratification. Notwithstanding its great potential, the GMI signature score is limited by the quality of currently available data. An important future work is to validate the clinical usefulness of the GMI signature for the developed prognosis model in AML.Additional file 1:Development and validation of GMI signature based random survival forest prognosis model to predict clinical outcome in acute myeloid leukemia (XLSX 32 kb)Additional file 2:Figure S1. Illustration of the volcano plot for identifying the differentially expressed genes. Figure S2. MiRWalk2.0 identify miRNA-mRNA interactions from an array of experimentally verified and predicted miRNA-target interaction pairs. FigureS3. Kaplan–Meier curve analysis of 20 expression signatures from GMI with LinkedOmics for the overall survival in AML patients. Figure S4. Kaplan–Meier curve analysis of GMI signature with GEPIA for the overall survival in AML patients. (DOCX 1029 kb)


There are many prognostic gene signature models in clear cell renal cell carcinoma (ccRCC). However, different results from various methods and samples are hard to contribute to clinical practice. It is necessary to develop a robust gene signature for improving clinical practice in ccRCC.A method was proposed to integrate least absolute shrinkage and selection operator and multiple Cox regression to obtain mRNA and microRNA signature from the cancer genomic atlas database for predicting prognosis of ccRCC. The gene signature model consisted by 5 mRNAs and 1 microRNA was identified. Prognosis index (PI) model was constructed from RNA expression and median value of PI is used to classified patients into high- and low-risk groups.P < .001]. As the gene signature was mainly consisted by mRNA, the validation data can use transcriptomic data to verify. For comparison of the performance with previous works, other gene signature models and 4 datasets of ccRCC were retrieved from publications and public database. For estimating PI in each model, 3 indicators including HR, concordance index , and the area under the curve of receiver operating characteristic for 3 years were calculated across 4 independent datasets.The results showed that high-risk patients showed significantly decrease survival comparison with low-risk groups [hazard ratio (HR) =7.13, 95% confidence interval = 3.71–13.70, The comparison results showed that the integrative model from our study was more robust than other models via comprehensive analysis. These findings provide some genes for further study their functions and mechanisms in ccRCC tumorigenesis and malignance, and may be useful for effective clinical decision making of ccRCC patients. In 10 years, the morbidity and mortality of patients with renal cancer have not been significantly changed. One of the important reasons is that RCC is a highly heterogeneous set of disease. Of these subtypes of RCCs, The clear-cell renal cell carcinoma (ccRCC) is one of most common subtypes, accounting for approximately 70% to 80% of the whole RCC. Thus, identification of robust biomarkers for ccRCC prognosis is necessary.Renal cell carcinoma (RCC) is a frequent malignant tumor of the adult kidney. According to cancer statistics in 2018, 65,340 new kidney cancer patients and 14,970 deaths.–24 How do we decide which gene signature is effective against the current ccRCC, which gene signature is more universal? In this study, a least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis method was combined with multivariate Cox regression to obtain a set of gene biomarkers and compared it with other gene signature from publications. In addition, there are many studies in microRNA prognostic signature.,25–28 Thus, we also integrated microRNA and mRNA expression for predicting prognosis of ccRCC.In an age of precision medicine, molecular subtype and gene signature can provide new insight for clinical strategy and drug development. High-throughput gene sequencing technology provides us with a powerful tool to find genetic differences among different patients. Therefore, different strategies can be used to treat the patient in molecular level. Nevertheless, different approaches using by different groups have produced many different prognostic biomarkers for ccRCC. find 3 genes for prognosis of ccRCC, Boguslawska et al find 10 genes and Zhan et al find 5 genes. In our study, we found 5 mRNAs and 1 microRNA can predict prognosis of ccRCC. Interestingly, each of the gene signature has no intersection, but they performed well in validating the data set.In this study, we select data from 2 common databases of the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database as training data sets and validating datasets, respectively. Other 3 gene signatures were also tested with these 2 database sets. To exclude methodological heterogeneity, we selected 3 studies of gene prognostic biomarkers that were obtained using Cox regression. In the 3 studies, Yao et alHere we demonstrate that the gene signature from LASSO combined multivariate Cox regression has more stability and universal in prognosis of ccRCC. These may be helpful for selecting high-risk ccRCC patients for better clinical decision making and provide useful biomarkers for downstream experimental.22.1https://portal.gdc.cancer.gov/). The mRNA dataset is performed by the Illumina Hiseq platform. And microRNA dataset is also performed by Illumina Hiseq platform. In ccRCC cohort of TCGA database, RNASeqV2 expression data contain 20,530 genes and microRNA expression data contain 1046 microRNAs. The patients without survival time and event information were excluded. The samples with mRNA expression contained 510 patients with primary tumor and 70 patients with solid tissue normal. The samples with microRNA expression contained 254 patients with primary tumor and 71 patients with solid tissue normal. We used both mRNA and miRNA samples as training dataset (n = 239). Validation data are collected from GEO database. GSE22541 contains 68 samples which include 24 primary and 44 metastasis samples. And this dataset employed Affymetrix Human Genome U133 Plus 2.0 Array. This work did not directly use tissues from patients or animals.The microRNA expression proliferation and mRNA expression proliferation are collected TCGA database  <0.01 as candidate genes for next step. The microRNA and mRNA differential expression are assayed by R package by “limma”2.3Firstly, univariate Cox regression and survival analysis are applied to analyze clinical factors and each differential expression gene. For clinical factors, we employed univariate Cox regression to test hazard ratio (HR). And survival analysis using Kaplan-Meier is applied to analyze clinical factor significant difference by log-rank (2-sided test). For estimating the clinical factor in ccRCC patients, HR >1 is considered as risk increasing group and HR <1 is considered as risk-decreasing factor.2.4P < .05 as candidate for multivariate Cox regression.For univariate Cox regression clinical factor, we screened the factor with Wald test P < .05 as candidate for LASSO Cox regression. By univariate Cox regression filtering, 2498 mRNAs and 18 miRNAs were selected to submit to LASSO. LASSO is performed by R package of “glmnet.” LASSO is employed to filter 2498 mRNAs and 18 miRNAs, respectively. After 10,000 iterations and 10 folds cross-validation, 16 mRNAs and 9 miRNAs are obtained.For mRNA and miRNA expression, we filtered the RNA with Wald test 2.5For investigating integrative model of miRNAs and mRNAs, ccRCC patients who both include mRNA and miRNA expression are selected. There are 239 samples including for assay the integrative model. We employed multivariate Cox regression for 25 RNAs (16 mRNAs and 9 microRNAs), and we obtained 5 mRNAs and 1 microRNA-independent predictors for ccRCC.2.6Prognostic model search was employed by PubMed. The following terms were searched in PubMed: . After above filtering, 3 studies were included in this study.2.7A prognosis index (PI) as an integrated indicator of candidate RNAs for each ccRCC patient was constructed. The PI was computed as a linear combination of the RNA expression value and weighted by LASSO Cox regression coefficients. βi is the regression coefficient of the ith variable. Xi is the value of the ith variable. In this study, Xi is the log2-transformed expression value of each RNA and βi is the LASSO Cox regression coefficient of the ith RNA.where 2.8https://xenabrowser.net/datapages/) which is built by UCSC. In addition, the samples in gene expression array has little overlap in training data samples (overlap n = 5). Thus, the microarray data can be used as an independent dataset. In addition, GSE22451 is an independent dataset which downloads from GEO database.For assessment performance of gene signature model, gene expression array (AgilentG4502A_07_3) and RNAseq (IlluminaHiSeq) were selected. The RNAseq data were divided into 2 parts. One part includes both miRNA and mRNA samples (n = 239) that were used to be training data in this study. Another part contained all ccRCC patients (n = 510). The gene expression array contained other ccRCC patients (n = 72). All ccRCC cohorts in TCGA data were downloaded from Cancer browser  of the receiver operating characteristic (ROC), which was conducted using the survival ROC package in R software. Another indicator called concordance index (C-index) was conducted using “Hmisc” package.2.10 (http://www.targetscan.org/) and miRanda (Release 19) (http://www.microrna.org/) methods were employed to predict target. Moreover, TargetScan and miRanda tool were used considering both conserved and nonconserved targets.Many computational prediction approaches are available recently such as TargetScan, miRanda, PicTar, TarBase, RNAHybrid, etc, which are mostly based on complementarity, thermodynamics, or experimental validation. In this study, TargetScan (Release 7.1)2.11 TF target genes were identified using the approach developed by Kathrin et al, via defining the ±1000 bp sequence around transcription start sites as the promoter region. The genes with promoter regions completely overlapped with TF binding sites were considered as TF targets. To further enhance the reliability of TF, we calculated the correlation between predicted TF and target genes in TCGA dataset. Pearson correlation was used to estimate the relation between TFs and targets. Generally, TFs were considered to promote the expression of their targets. So, TFs expressions have positive correlations with their target genes (r > 0.3), and visualization of regulation network is used by Cytoscape software (version 3.5.1).Each gene signature model contains a very small number of genes. It is difficult to enrich pathway through Gene Ontology (GO) analysis. Thus, the transcription factor (TF) of each gene in gene signature model is to predict and combines them to analyze to ClusterProfiler for GO analysis.33.1In this study, 4 gene signature models were validated in 4 datasets, and baseline information of patients with ccRCC is listed in Table 3.2With LASSO combined multivariate Cox regression, 6 RNAs were obtained, and the result is listed in Table P < .001]. The HR of PI was greater than HRs of grade  or T stage . The patients with ccRCC were ranked by PI value . The identified RNA expressions in high-risk and low-risk are listed in Figure As a linear combination of the expression values of 6 RNAs, the PI was significantly associated with OS in ccRCC [HR = 7.13, 95% confidence interval (CI) = 3.71–13.70, 3.3For validation of the result, the GSE22541 dataset is employed to be an independent data to test above result. The dataset contains 24 primary ccRCC tumor and disease-free survival (DFS) time of patients. Although this data set does not have OS time, DFS data can also reflect patient outcomes. We just employed mRNA data to validate the results due to lack of microRNA data. The validation result is shown in Figure P = .03). The PI was significantly associated with DFS in independent data of ccRCC . The value of AUC = 0.762 (3 years) also indicated that the model performed well. Above results demonstrated that the integrative model could effectively classify patients.From Figure 3.4For further validation the result, we tested the model in other ccRCC data in TCGA. Moreover, we also validate the other 3 models in 4 data sets. These 3 gene signature models that were published previously are listed in Table For estimating the performance of various gene signature models, 3 indicators  of prognostic models need to be calculated. These 3 indicators were analyzed correlation in each other. Thus, the relationship of these 3 indicators in 4 models was assayed  to evaluate the effect of all models in the 4 datasets  as robust gene signature model that could effectively predict the prognosis for ccRCC. In addition, we also found a regulation pair of hsa-mir-374a and ANK3 from TargetScan.Our present study combined LASSO and multivariate Cox regression to calculate a prognostic gene signature model from integrative microRNA and mRNA expression of TCGA dataset. The other platform of TCGA and GEO dataset as validation datasets were employed to validate the results. Previous study has provided many biomarkers for predicting prognosis of ccRCC. In this study, we proposed 5 mRNAs and 1 microRNA  can reduce the risk of colorectal cancer. Our findings in kidney cancer also showed similar results , so we hypothesized that hsa-mir-374a could reduce the risk of death. These 6-gene signatures showed robust ability in predicting prognosis of ccRCC.Of these genes, Generally, gene signature prediction for prognosis mainly derived from Cox regression. However, different data preprocess and steps for Cox regression might lead to different results. This study combined the genes with differential expression, univariate Cox regression, LASSO, and multivariate Cox regression method to obtain gene signature for prognosis of ccRCC. In addition, 3 indictors including HR, C-index, and value of AUC were employed to estimate all models in systems level Fig. . The resAlthough our results show more advantages, it does not mean that other models are not good. Among the various gene signature models previously proposed, prognosis is thought to be predictive. In fact, different gene signature has similar pathway and its own special function. The similar pathways are possible to perform similar functions that affect prognosis. The different pathways may represent the heterogeneity of ccRCC.,41. Although this work could not reveal the relationship between IBD and prognosis of ccRCC, the result might provide a new insight for further study about the ccRCC.In the work, the integrate model mainly involved in viral infection and inflammatory bowel disease (IBD)-related pathways. From literature review, there are few reports about viral infection associated with ccRCC. However, there are many reports about the relationship between IBD and renal cancerIn addition, the gene expression data and clinical data of available ccRCC are very limited, which results in difficulty to further verify. We just used different platforms of TCGA dataset and GEO dataset as independent datasets for training and validation. For further validation of different, we test other 3 gene signature models (from Cox regression method) in different datasets. Moreover, the integrate model indeed showed greater stability and versatility in the TCGA and GSE22541 datasets.Despite the limited data available, the data we obtained may have bias. However, the gene markers obtained by LASSO coupled multivariate Cox regression are indeed more stable in various public databases. In this study, we propose the optimization steps for analyzing gene prognostic markers by Cox regression. In addition, when gene markers are too scarce to enrich their functions by GO analysis, we can further analyze GO functional enrichment by predicting their TFs. We expect to find more and more stable genetic markers by this way to provide a more scientific reference for drug development and clinical decision-making.The publications retrieval from staff in Evidence Based Medicine Center is appreciated by the authors.Conceptualization: Peng Chang, Kehu Yang.Data curation: Jingyun Zhang.Formal analysis: Peng Chang, Juan Ling.Funding acquisition: Zhitong Bing.Investigation: Jinhui Tian, Xiuxia Li, Yumin Li.Methodology: Peng Chang, Xiuxia Li.Project administration: Juan Ling.Resources: Jingyun Zhang, Long Ge.Software: Zhitong Bing, Jinhui Tian, Jingyun Zhang, Long Ge.Supervision: Kehu Yang.Validation: Zhitong Bing.Visualization: Jinhui Tian, Yumin Li.Writing – original draft: Peng Chang, Zhitong Bing, Yumin Li.Writing – review and editing: Peng Chang, Kehu Yang.


After using this dLDA to learn these cancer-topics, we can then express each patient as a distribution over a small number of cancer-topics, then use this low-dimensional “distribution vector” as input to a learning algorithm—here, we ran the recent survival prediction algorithm, MTLR, on this representation of the cancer dataset. We initially focus on the METABRIC dataset, which describes each of n = 1,981 breast cancer patients using the r = 49,576 gene expression values, from microarrays. Our results show that our approach (dLDA followed by MTLR) provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this “dLDA+MTLR” approach by running it on the n = 883 Pan-kidney (KIPAN) dataset, over r = 15,529 gene expression values—here using the mRNAseq modality—and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent “D-calibrated” measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach. The dLDA+MTLR source code is available at https://github.com/nitsanluke/GE-LDA-Survival.Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional gene expression data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient’s cancer, we represent each patient (≈ document) as a mixture over cancer-topics, where each cancer-topic is a mixture over gene expression values (≈ words). This required some extensions to the standard LDA model— Using the random variable D for the time of death for the patient described by x:t0 = 0—i.e., PrW(D ≥ 0 | x) = 1—down to smaller values as the time t increases. d ∈ ℜ≥0 as a binary vector (of classification labels) y(d) = , where each yj(d) ∈ {0, 1} encodes that patient’s survival status at each time interval : yj(d) = 0 (no death yet) for all j with tj < d and yj(d) = 1 (death) for all tj ≥ d. The learning system attempts to optimizem (the number of time points) is set to the square-root of the number of instances in all our experiments.This project considered 3 ways to learn a survival model: The standard approaches—Cox and Regularized Cox (RCox)—are overviewed in Appendix A.5. This subsection describes the relatively-new MTLR  system, er curve , but incW, we can then use et al. ) appearing below in Given the learned parameters e et al.  presentsr et al. . Note thLatent Dirichlet Allocation (LDA) is a widely used generative model , with malearning process first identifies the latent topics—that is, the topic-words distributions corresponding to each latent topic—based on the words that frequently co-occur across multiple documents; n.b., it just uses the documents themselves, but not the labels. For example, it might find that many documents with the word “ball” also included “opponent” and “score”; and vice versa. Similarly, “finances”, “transaction”, and “bank” often co-occur, as do “saint”, “belief” and “pray”. Speaking loosely, the topic-model-learner might then form one topic, βi. To simplify the presentation, here we are showing their expected values, these The LDA n-tuple over the n words; we call this n-tuple topic-words distributionK topics.This K topics—perhaps document f1 would be decomposed as Θ(f1) =  = —these are parameters for a Dirichlet distribution, which are non-negative, but do not add up to 1. These are different for different documents—e.g., perhaps f2 is expressed as Θ(f2) = , etc. This is the document-topic distribution {Θ(fj)}j=1‥m over the m documents.The learner would then map each document into a “distribution” over this set of K. Given this, the LDA learning process finds the inherent structure present in the data—i.e., a model  and also the number of latent topics, The same way certain sets of words often co-occur in a document, similarly sets of genes are known to be co-regulated: under some condition (corresponding to a “c_topic”), every gene in that set will have some additional regulation—some will be over-expressed, each by its own amount, and the others will be under-expressed. Moreover, just as a natural language (NL) topic typically involves relatively few words, most c_topics effectively involve relatively few genes. Also, just like a document may involve a mixture of many topics, each to its own degree, so a patient’s cancer often involves multiple c_topics; see work on cancer subclones . This haet al. [ first identifies the set of relevant c_topics seBasisρ=dLDA]Here, As noted above, the ≈50,000 expression values for each patient is so large that most standard learning algorithms would overfit. We consider two ways to reduce the dimensionality. One standard approach, Principal Component Analysis (PCA), is discussed in Appendix A.4. Here, we discuss a different approach, dLDA, that uses the Latent Dirichlet Analysis.reProcess routine computes z-scores XGE; the ComputeBasisρ=dLDA][ would find the top principle components of the seBasisρ=PCA][ and UseBasisρ=PCA] and UseModel[Ψ=RCox] produce standard risk scores for each patient, obtained by applying the learned Cox  model to the patient’s clinical and gene expression features; see Appendix A.5.Each of the various subroutines are described in an appendix: Pρ ∈ {dLDA, PCA} and three different survival prediction algorithms Ψ ∈ {MTLR, Cox, RCox}. This allows us to explore 2 × 3 frameworks, on the two different datasets (METABRIC and KIPAN). For each, the learner uses internal internal cross-validation to find the optimal parameters. Below we report the results of each optimized model on the held-out set, focusing on the Concordance Index (CI)—a discriminator measure. We also discuss a calibration measure of these results; see Appendix B.2.As noted above, we intentionally designed our learning and performance systems Figs  and 6 toomputeBasisρ=dLDA] shows that the resulting ensemble model achieved a CI of 0.7293 on the test data. While that score is slightly better than the performance of our best model (E]), note that all of our tuning was performed solely on the training (n = 1586) data, while their team made major design choices for their model using the entire METABRIC cohort , on which it was then evaluated.In 2012, Cheng et al.  won the et al. [ and UseBasisρ=PCA][ algorithmfunction ComputeBasisρ=dLDA]—i.e., δ is sampled from the uniform distribution over the interval . This LDA learning process }i for each of these K c_topics—revealing GEt’s intrinsic structure. Recall these are just the parameters for Dirichlet distribution; note they must be positive, but do not add up to 1. The Compute_dLDA returns the expected values of the gene expression values drawn from this posterior distribution: This routines also needs to set the priors for the  process  uses theK* and best encoding technique t*, we can then compute the c_topic distribution for a new patient (based on her gene expression xGE); see Figs seBasis, which in turn runs Use_dLDA (the LDA inference procedure) on the preprocessed gene expression data patient-c_topic Dirichlet distribution jth c_topic . The PCA transformation projects the initial “raw” features into a different space, which then can be used to select the top components based on the univariate Cox regression. Note this SuperPC+ is (still) computationally efficient, as it is based on PCA, which is efficient: Even though gene expression data is high dimensional , the rank of the GE matrix will be (at most) min{p, n} = n. Therefore, PCA can be performed without many computational restraints on the whole gene expression dataset, as here the PCA time complexity is O(n3). After performing PCA on the GE dataset, we can then identify the most important principal components by computing a Cox score statistic for the univariate association between each principal component and the survival time. In our experiments, we select the threshold η for the p-value of the Cox score by internal cross-validation (wrt concordance), and retained all PCs having a p-value lower than this η—finding η = 5e-4 for the METABRIC dataset and η = 5e-2 for KIPAN. These selected PC components form the basis set BGE[ρ = PCA].This algorithm first calculates the univariate Cox score statistic of each individual gene against the survival time, then retains just the subset of genes whose score exceeds a threshold, determined by internal cross-validation. Then it computes PCA on the dataset containing only those selected genes, then projects each patient onto the first one (or two) components. The main disadvantage of the SuperPC algorithm is that the individual genes selected from the univariate selection process might not perform the best in a multivariate  model, perhaps because many of these top-ranked genes may be highly correlated with one another –of genes , 37. InsseBasisρ=PCA] describes the ith subject, where is the indicator function, which is 1 if the proposition ϕ is true, and 0 otherwise. A pair of patients is “comparable” if we can determine which died first –i.e., if both are uncensored, or when one patient is censored after the observed death time of the other; this corresponds to the set of ordered pairs of indices Ψ. This CI(f) score is a real value between 0 to 1, where 1 means all comparable pairs are predicted correctly. CI can be viewed as a general form of the Mann–Whitne–Wilcoxon statistic and is similar to the Area Under the ROC (Receiver Operator Curve), AUC, of classification problems , and another quarter to be in , etc. Stated more precisely,We consider a novel measure of the calibration of such survival curves, called D-calibration (“D” for “Distribution”) . To motiPri(·) = PrW (· | xi) (from Pri(⋅) curve crosses 0.50.A single KM curve is designed to represent a cohort of many patients. The MTLR system, however, computes a different survival curve for each patient—call it xi) from . But theW) that 1/2 of patients will die before their respective median survival time—i: Pri(di) ≤ 0.5}| ≈ n/2. Continuing the arguments from above, we therefore expect the obvious analogue to By the same argument suggested above, we expect (for a good model G is the number of subgroups (here 4), where the gth subgroup has N/g, for N = ∑gNg total patients), the corresponding predicted number of events in each group gth subgroup. Under the null hypothesis .We can now test whether a model is D-calibrated by using the Hosmer-Lemeshow (HL)  goodnesspothesis , this HLPri(di) probabilities into 20 bins: . (3) This analysis deals only with uncensored data; Haider et al. [Notes: (1) This evaluation criterion only applies to models that produce survival distributions, which means it directly applies to the MTLR models. For the Cox and RCox models, we used the Kalbfleisch-Prentice baseline hazard estimator  to produr et al.  discusseThis appendix presents additional results: First, Appendix C.1 evaluates the Latent process decomposition (LPD) method, then Appendix C.2 provides D-calibration results of our various models.et al. [Rogers et al.  introduci.e., dLDA produces better features from the gene expression data.We then used the model based on these 10 latent process; the resulting concordance results, on the hold-out dataset, was 0.6915 (Cox), 0.6077 (RCox) and 0.6995 (MTLR). Comparing this to the “B” and “C” rows of There are two other reasons to prefer our dLDA-approach: (1) LPD has large time and memory requirements. (2) Moreover as our dLDA directly uses the LDA model, it can utilize all available off-the-shelf implementations, across several technology platforms with efficient and scalable implementation .i.e., excluding the “E” and “F” rows from i.e., had a HL p-value > 0.05) in all 12 situations, for METABRIC and KIPAN—for all feature groups {A, B, C, D}, and all 3 learning algorithms {Cox, RCox, MTLR}. We note that we found that Cox failed this test on other datasets, including BRCA.


High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within the data, cluster analysis provides an intuitive alternative. The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products.A mixture of multivariate Poisson-log normal (MPLN) model is developed for clustering of high-throughput transcriptome sequencing data. Parameter estimation is carried out using a Markov chain Monte Carlo expectation-maximization (MCMC-EM) algorithm, and information criteria are used for model selection.https://github.com/anjalisilva/MPLNClust.The mixture of MPLN model is able to fit a wide range of correlation and overdispersion situations, and is suited for modeling multivariate count data from RNA sequencing studies. All scripts used for implementing the method can be found at The online version of this article (10.1186/s12859-019-2916-0) contains supplementary material, which is available to authorized users. RNA sequencing (RNA-seq) is used to determine the transcriptional dynamics of a biological system by measuring the expression levels of thousands of genes simultaneously , 2. ThisClustering algorithms can be classified into two broad categories: distance-based or model-based approaches . DistancG is the total number of clusters, fg(·) is the distribution function with parameters 𝜗g, and πg>0 is the mixing weight of the gth component such that zig is used for cluster membership, such that zig equals 1 if the ith observation belongs to component g and 0 otherwise. The predicted cluster memberships at the maximum likelihood estimates of the model parameters are given by the maximum a posteriori probability, MAPThe probability distribution function of a mixture model is lgorithm .Clustering of gene expression data allows identifying groups of genes with similar expression patterns, called gene co-expression networks. Inference of gene networks from expression data can lead to better understanding of biological pathways that are active under experimental conditions. This information can also be used to infer the biological function of genes with unknown or hypothetical functions based on their cluster membership with genes of known functions and pathways . Over thThe Poisson distribution is used to model discrete data, including expression data from RNA-seq studies. However, the multivariate extension of the Poisson distribution can be computationally expensive. As a result, the univariate Poisson distribution is often utilized in clustering algorithms, which leads to the assumption that samples are independent conditionally on the components , 12, 14.The multivariate Poisson-log normal (MPLN) distribution  is a mulHere, a novel mixture model-based clustering method is presented for RNA-seq using MPLN distributions. The proposed clustering technique is explored in the context of clustering genes. The performance of the method is evaluated through data-driven simulations and real data.To illustrate the applicability of mixtures of MPLN distributions, it is applied to a RNA-seq dataset. For comparison purposes, three model-based clustering methods were also used. These include HTSCluster , 14, PoiM values (TMM) [k-means for HTSCluster and MBCluster.Seq. An option to specify normalization or initialization method was not available for Poisson.glm.mix, thus default settings were used. Note, for MBCluster.Seq, G=1 cannot be run.Typically, only a subset of differentially expressed genes is used for cluster analysis. Normalization factors representing library size estimate for samples for all methods were obtained using trimmed mean of es (TMM) , 24 fromIn the context of real data clustering, it is not possible to compare the clustering results obtained from each method to a ‘true’ clustering of the data as such classification does not exist. To identify if co-expressed genes are implicated in similar biological processes, functions or components, an enrichment analysis was performed on the gene clusters using the Singular Enrichment Analysis tool available on AgriGO . SingulaPhaseolus vulgaris L.) at three developmental stages: early (E), intermediate (I) and mature (M). A summary of this dataset is provided in Table In the study by Freixas-Coutin et al. , RNA-seqT1, data was clustered for a range of G=1,…,11 using k-means initialization with 3 runs.  Since model selection criteria selected G=2 or G=11 for HTSCluster, Poisson.glm.mix, and MBCluster.Seq, further clustering runs were performed for these methods using ranges of T2 :G=1,…,20; T3 :G=1,…,30; T4 :G=1,…,40; T5 :G=1,…,50 and T6 :G=1,…,100. The clustering results are summarized in Table G=1 cannot be run for MBCluster.Seq, slope heuristics could not be applied for T1.The raw read counts for genes were obtained from Binary Alignment/Map files using samtools  and HTSeG=4, with the exception of the AIC, which selected a G=5 model in T1. Recall that the AIC is known to favor more complex models with more parameters. A cross tabulation comparison of G=4 model with that of G=5 did not reveal any significant patterns, but rather random classification results were observed. For the G=4 model, each cluster contained 71, 731, 415 and 119 genes respectively, and the expression patterns of these models are provided in Fig. G=2 was selected. This is the lowest cluster size considered in the range of clusters for this method as G=1 cannot be run for MBCluster.Seq. For G=2 model, Cluster 1 contained 467 genes and Cluster 2 contained 869 genes , it was evident that the highest cluster size is selected for HTSCluster and Poisson.glm.mix. No changes were observed for MBCluster.Seq, NB, as the lowest cluster size, G=2, is selected. All information criteria  gave similar results, suggesting a high degree of certainty in the assignment of genes into clusters, i.e., that the posterior probabilities T1,…,T6. For this reason, overfitting and underfitting methods were run for G=1,…,100, as in T6, but for 20 different times. Results for both information criteria and slope heuristics are provided in Table During er runs T,…,T6, itG=4 model of mixtures of MPLN distributions, G=14 model of MBCluster.Seq, Poisson, and G=2 model of MBCluster.Seq, NB. The GO enrichment analysis results for all models are provided in Additional file G=2,G=4, and G=14 models, respectively. Among the models, clear expression patterns were evident for the G=14 model, and this can be attributed to the fact that there are more clusters present in this model. However, only 5 of the 14 clusters exhibited significant GO terms.Due to model selection issues with over and under fitting, downstream analysis was only conducted using the G=4 model of the mixtures of MPLN distributions, because comparing the cluster composition of genes across different methods, with respect to biological context, is beyond the scope of this article. For the G=4 model, Cluster 1 genes were highly expressed in intermediate developmental stage, compared to other developmental stages, regardless of the variety . Proanthocyanidins have been shown to convert from colorless to visible pigments during oxidation [Cluster 3 genes showed higher expression in early developmental stage, compared to other developmental stages, regardless of the variety. Here, genes belonged to oxidoreductase activity, enzyme activity, binding and dehydrogenase activity. Finally, Cluster 4 genes were more highly expressed in the darkening variety relative to the non-darkening variety. The GO enrichment analysis identified Cluster 4 genes as containing biosynthetic genes. Further examination identified that many of these genes were annotated as flavonoid/proanthocyanidin biosynthesis genes in the xidation . Beans wxidation , 30.n=1000 observations and d=6 samples generated using mixtures of MPLN distributions. The covariance matrices for each setting were generated using the genPositiveDefMat function in clusterGeneration package, with a range specified for variances of the covariance matrix [To simulate data that mimics real data, the library sizes and count ranges in simulated datasets were ensured to be within the same 5–95% ranges as those observed for real data. For the simulation study, three different settings were considered. In simulations 1 and 2, 50 datasets with one underlying cluster and 50 datasets with two underlying clusters were generated, respectively. In simulation 3, 30 datasets with three underlying clusters were generated. All datasets had e matrix .zig for all methods was done using the k-means algorithm with 3 runs. For simulation 1, π1=1 and a clustering range of G=1,…,3 was considered. For simulation 2, π1=0.79 and a clustering range of G=1,…,3 was considered. For simulation 3, π1=0.3 and π2=0.5, and a clustering range of G=2,…,4 was considered. In addition to model-based methods, three distance-based methods were also used: k-means [Comparative studies were conducted to evaluate the ability to recover the true underlying number of clusters. For this purpose, the following model-based methods were used: HTSCluster, Poisson.glm.mix and MBCluster.Seq. Initialization of  k-means , partiti k-means  and hier k-means  was alsoG=1 cannot be run, and the corresponding row of results has been left blank on Table G=1,2,3 was selected for Poisson.glm.mix, m = 3 in simulation 1, an ARI value of one was obtained because all runs resulted in only one cluster (others were empty clusters). Distance-based methods and the graph-based method resulted in low ARI values.The adjusted Rand index (ARI) values obtained for mixtures of MPLN were equal to or very close to one, indicating that the algorithm is able to assign observations to the proper clusters, i.e., the clusters that were originally used to generate the simulation datasets. Note, for MBCluster.Seq, n=200 observations and d=6 samples generated using mixtures of negative binomial distributions. Comparative studies were conducted as specified earlier. Initialization of zig for all methods was done using the k-means algorithm with 3 runs. Here, π1=0.79 and a clustering range of G=1,…,3 was considered. The clustering results are summarized in Table In this simulation, 50 datasets with two underlying clusters were generated. All datasets had A model-based clustering technique for RNA-seq data has been introduced. The approach utilizes a mixture of MPLN distributions, which has not previously been used for model-based clustering of RNA-seq data. The transcriptome data analysis showed the applicability of mixture model-based clustering methods on RNA-seq data. Information criteria selected the highest cluster size considered in the range of clusters for HTSCluster and Poisson.glm.mix. For MBCluster.Seq, NB, the lowest cluster size considered in the range of clusters was selected. This could potentially imply that these mixtures of Poisson and NB models are not providing a good fit to the data. However, further research is needed in this direction, including the search for other model selection criteria. The GO enrichment analysis  identified enriched terms in 75% of the clusters resulting from mixtures of MPLN distributions, whereas only 50% of clusters from MBCluster.Seq, NB and 36% of the clusters from MBCluster.Seq, Poisson contained enriched GO terms.Using simulated data from mixtures of MPLN distributions, it was illustrated that the algorithm for mixtures of MPLN distributions is effective and returned favorable clustering results. It was observed that other model-based methods from the current literature failed to identify the true number of underlying clusters a majority of the time. Clustering trends similar to those observed for transcriptome data analysis were observed for other model-based methods during the simulation data analysis. Distance-based methods failed to assign observations to proper clusters, as evident by the low ARI values. The graph-based method, Louvain, also failed to identify the true number of underlying clusters.G=2 is selected via information criteria, when MBCluster.Seq, NB is used for clustering.Using simulated data from mixtures of negative binomial distributions, it was illustrated that the algorithm for mixtures of MPLN distributions is effective and returned favorable clustering results. The distance-based methods also assigned observations to proper clusters resulting high ARI values. It was observed that other model-based methods from the current literature, as well as the graph-based method, failed to identify the true number of underlying clusters a majority of the time. Although the correct numbers of clusters were selected by MBCluster.Seq, proper cluster assignment has not taken place as evident by the low ARI values. Note that although MBCluster.Seq, NB is based on negative binomial distributions, it has low ARI (approx. 0). This could be because the implementation of the approach by  availablOverall, the transcriptome data analysis together with simulation studies show superior performance of mixtures of MPLN distributions, compared to other methods presented.The mixture model-based clustering method based on MPLN distributions is an excellent tool for analysis of RNA-seq data. The MPLN distribution is able to describe a wide range of correlation and overdispersion situations, and is ideal for modeling RNA-seq data, which is generally overdispersed. Importantly, the hidden layer of the MPLN distribution is a multivariate Gaussian distribution, which accounts for the covariance structure of the data. As a result, independence does not need to be assumed between variables in clustering applications.curse of dimensionality as high-dimensional RNA-seq datasets become frequently available.The scripts used to implement this approach are publicly available and reusable such that they can be simply modified and utilized in any RNA-seq data analysis pipeline. Further, the vector of library size estimates for samples can be relaxed and the proposed clustering approach can be applied to any discrete dataset. A direction for future work would be to investigate subspace clustering methods to overcome the j, a fixed, known constant, sj, representing the normalized library sizes is added to the mean of the Poisson distribution. Thus, for genes i∈{1,…,n} and samples j∈{1,…,d}, the MPLN distribution is modified to give The sequencing depth can differ between samples in an RNA-seq study. Therefore, the assumption of equal means across conditions is unlikely to hold. To account for the differences in library sizes across each sample G-component mixture of MPLN distributions can be written Θ= denotes all model parameters and fY denotes the distribution of the gth component with parameters μg and Σg. The unconditional moments of the MPLN distribution can be obtained via conditional expectation results and standard properties of the Poisson and log normal distributions. For a G-component mixture of MPLN distributions, the mean of Yj is σjjg represents the diagonal elements of Σg, for j=1,…,d. Now, A Zi such that Zig=1 if an observation i belongs to group g and Zig=0 otherwise, for i=1,…,n;g=1,…,G. The complete-data consist of , the observed and missing data. Here, z is a realization of Z. The complete-data log-likelihood for the MPLN mixture model is To estimate the parameters, a maximum likelihood estimation procedure based on the EM algorithm is used. In the context of clustering, the unknown cluster membership variable is denoted by 𝜗g=, for g=1,…,G. Because the first term of  in  in  is 3\dof is difficult. Therefore, the E-step cannot be solved analytically. Here, an extension of the EM algorithm, called Monte Carlo EM (MCEM) [t and for each observation yi a random sample of size B, i.e., f to find a Monte Carlo approximation to the conditional expectation of complete-data log-likelihood given observed data. Here, each iteration from the MCEM simulation is represented using k, where k=1,…,B. As the values from initial iterations are discarded from further analysis to minimize bias, the number of iterations used for parameter estimation is N, where N<B. Thus, a Monte Carlo approximation for f is unknown. Therefore, an alternative MCEM based on Markov chains, Markov chain Monte Carlo expectation-maximization (MCMC-EM) is proposed. MCMC-EM is implemented via Stan, which is a probabilistic programming language written in C++. The R interface of Stan is available via RStan.Due to the integral present in , evaluatM (MCEM) , can be entclass1pt{minimaθig is a multivariate Gaussian distribution and the likelihood follows a Poisson distribution. Within RStan, the warmup argument is set to half the number of total iterations, as recommended [Bayesian approaches to mixture modeling offer the flexibility of sampling from computationally complex models using MCMC algorithms. For the mixtures of MPLN distributions, the random sample ommended . The warθig and group membership variable Zig, respectively, are updated in E-step as follows Using MCMC-EM, the expected value of potential scale reduction factor [effective number of samples [potential scale reduction factor less than 1.1 and an effective number of samples value greater than 100 [To determine whether the MCMC chains have converged to the posterior distribution, two diagnostic criteria are used. One is the n factor  and the  samples . The algthan 100 . If boththan 100 . The Monthan 100 , which cthan 100  is applithan 100 . If not μg and Σg, the mean and cov functions in R are applied to the input dataset, respectively, and log of the resulting values are used. For initialization of k-means and random. For k-means initialization, k-means clustering is performed on the dataset and the resulting group memberships are used for the initialization of i. The mixtures of MPLN algorithm is then run for 10 iterations and resulting s, is calculated using the calcNormFactors function from the edgeR package [For initialization of parameters  package .Gmin: Gmax, each cluster size, G, is independent and there is no dependency between them. Therefore, each G can be run in parallel, each one on a different processor. Here, the algorithm for mixtures of MPLN distributions is parallelized using parallel package [Coarse grain parallelization has been developed in the context of model-based clustering of Gaussian mixtures . When a  package  and fore package . Paralle𝜗, n is the number of observations, and a posteriori classification given K represents the number of free parameters in the model, calculated as K=(G−1)+(Gd)+Gd(d+1)/2, for G clusters. These model selection criteria differ in terms of how they penalize the log-likelihood. Rau et al. [The Bayesian information criterion (BIC)  remains  used by , \docum(ICL) of , \documu et al.  make useu et al. , 52. FolAdditional file 1Expression patterns of different models. The expression patterns for different models of cranberry RNA-seq dataset. (PDF 1631 kb)Additional file 2GO analysis of different models. GO enrichment analysis results for the different models selected for cranberry RNA-seq dataset. (XLSX 17 kb)Additional file 3Parameter estimation results of simulated data. Parameter estimation results of mu and sigma values for simulated data using mixtures of MPLN distributions. (PDF 77 kb)


For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.Here, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value.From the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).The online version of this article (10.1186/s12859-018-2495-5) contains supplementary material, which is available to authorized users. With the rapid development of high-throughput biotechnologies, we were easily able to collect a large amount of gene expression data with many subjects of biology or medicine . Here, wFor samples of gene expression data sets, their dimensionality often resulted in their different types to be isometric by Euclidean distance . Thus, iHere, for samples of gene expression data sets, we used MG-PCC algorithm to divide them into different mini-groups, where the similarities of samples were defined by PCC measure, and the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups. That is, for any sample of a mini-group, its nearest neighbor was in the mini-group also. Moreover, we used t-SNE-SSP maps to display the relationships of mini-groups, where t-SNE-SSP map was selected from a series of t-SNE maps of standardized samples, these t-SNE maps had different perplexity parameter, and the initialization dimensions of these t-SNE maps were thirty. In t-SNE, the perplexity might be viewed as a knob that sets the number of effective nearest neighbors. It was comparable with the number of nearest neighbors that was employed in many manifold learners , 22.Furthermore, for gene clusters that were generated from PCC, we attempted to use t-SNE-SGI maps to display them, where t-SNE-SGI maps were selected from a series of t-SNE maps of standardized genes. Compared to t-SNE-SSP maps, t-SNE-SGI map was selected from these t-SNE maps that had the same perplexity parameter, but different initialization dimensions, where the perplexity parameter of these t-SNE maps were the dimensions of genes. In fact, for gene expression data sets under different samples, their genes were mass and dense, and the performance of t-SNE with these data sets required a larger perplexity.Here, we used A-value to select the t-SNE-SSP and t-SNE-SGI maps, where A-value was modeled from areas of clustering projections, and a t-SNE map was selected as t-SNE-SSP(or t-SNE-SGI) if its A-value was the smallest compared to others. Furthermore, for clusters with different clustering number, their t-SNE-SGI maps might come from the different t-SNE maps.To evaluate the reliability of the MG-PCC and t-SNE-SSP, we applied them to gene expression data sets of lung cancers , 24. ResData and data sourceThe first data set GDS3837 provides insight into potential prognostic biomarkers and therapeutic targets for non-small cell lung carcinoma, where it has 54674 genes, 60 normal and 60 tumor samples that are taken from nonsmoking females , 24. TheAk is the k-th matrix, the i-th row of Ak represents the i-th gene, the j-th column represents the j-th sample, genes of Ak are filtered by T-test(Hypothesis testing for the difference in means of two types of samples), and the detail of Ak is summarized in Table Ak, where data-k is the k-th sample data set, and data-k is transposed matrix of Ak. And then, 5 gene data sets are constructed by Ak, where data-(k+5) is the k-th gene data set, and data-(k+5) only contains tumor samples of Ak. That is, Ak is represented by , and data-(k+5) is Bk, where Bk and Ck contains tumor and normal samples, respectively.Here, we firstly use GDS3257 and GDS3837 to construct 5 matrixes, where Xi to represent the i-th sample of data-k, and Yj to represent the j-th gene of data-(k+5). That is, Xi is the i-th row of data-k, and Yj is the j-th row of data-(k+5), where Here, we use Xi and Yj are standardized into SSi and SGj, where SSi and SGj are called as S-sample and S-gene of Xi and Yj respectively, and Here, Xj1 and Xj2, they are used to construct the first mini-group, where Here, for ρ is PCC between Xi and Xj, and u is the number of samples. For Xj3, it is put into the first mini-group if it satisfies t−1)(t>3) samples, Xjt is put into the first mini-group if it satisfies When the first mini-group contains  belong to the first mini-group. Continuously, the first mini-group is completely built until no sample satisfies Eq. , , 5), thaFor samples of each mini-group, we plot the boundary of their projections by a closed line, where the closed line is called as boundary-line of the mini-group, the boundary-line forms a convex hull of their projections, and the area of the convex hull is called as A-value of the mini-group. Here, we use A-value to describe the consistency between samples and their projections, where ai is A-value of the i-th mini-group, a is A-value of the data set, v is the number of mini-groups.In general, for adjacent mini-groups, there is often some overlap for their convex hulls. Thus, A-value is smaller, the consistency between points and projections is more valid.Using t-SNE requires tuning some parameters, notably the perplexity and initialization dimension. Although t-SNE results are robust to the settings of parameters, in practice, we still have to interactively choose parameters by visually comparing results under multiple settings. For mini-groups and clusters of samples that are generated from PCC, we empirically validate that t-SNE maps of the standardized samples with an appropriate perplexity can clearly display them, where the initialization dimension of these t-SNE maps is thirty. But for PCC clusters of genes, t-SNE maps of S-genes with an appropriate initialization dimension can give them good visualizations, where the perplexity parameter of these t-SNE maps is the dimensions of genes.k, and the value of k ranges from 3 to 30. That is, for t-SNE-SS(t), it is selected as t-SNE-SSP if its A-value is the smallest compared to other t-SNE-SS(k). Similarly, for PCC clusters of genes, their t-SNE-SGI map is selected from a series of t-SNE-SG(i) maps by A-value also, where t-SNE-SG(i) is t-SNE map of S-genes, its perplexity parameter of these t-SNE maps is the dimensions of genes, its initialization dimensions is i, the value of i ranges from 3 to the dimensions of genes.Here, for mini-groups and clusters of samples that are generated from PCC, their t-SNE-SSP map is selected from a series of t-SNE-SS(k) maps by A-value, where t-SNE-SS(k) is t-SNE map of the standardized samples, its initialization dimensions are thirty, its perplexity parameter is http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html) to evaluate the consistency of clusters between PCC and Euclidean distance, where clusters of Euclidean distance are seen as the gold standard of genes. In general, Accuracy is a simple and transparent evaluation measure, RI penalizes both false positive and false negative decisions during clustering, F-Measure in addition supports differential weighting of these two types of errors, and NMI can be information theoretically interpreted, where the detailed explanation of these four criteria are explained see in [http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html) and their matlab codes are available at Additional file For t-SNE maps, since they are able to give good visualizations for clusters of Euclidean distance, they can be successful in complementing these PCC clusters that are relative consistency with Euclidean ones. Here, we use Accuracy, F-Measure, RI(Rand index) and NMI , 26  and t-SNE-SS(30) maps  maps were obtained by Eq. , where tMoreover, for 2 clusters of data-4 and data-5 according to normal and tumor samples, their A-values of different t-SNE-SS(k) maps were showed in Fig. Here, for gene clusters of data-7 and data-9, their A-values of t-SNE-SG(i) maps were showed in Fig. By Accuracy, F-Measure, RI and NMI, we demonstrated that PCC and Euclidean clusters of S-genes were relative consistent, which enabled t-SNE-SG(i) maps for displaying PCC clusters. But for t-SNE map with the randomly choosing parameters, it could give poor visualization for PCC clusters, which could lead to misinterpretation of clusters. Here, we used A-value to quantify the quality of t-SNE-SG(i) maps, which enabled t-SNE-SGI maps to project genes of the same clusters together, and neighbor clusters in adjacent regions.Here, we used data-1, 2, 3 and 4 to assess the biological reliability of t-SNE-SSP maps. According to population membership of samples, these four data sets were mapped on t-SNE-SSP maps respectively  map, where sample neighbors were defined by PCC. For sample neighbors of data-1, 2, 3 and 4, they were displayed on Fig. For gene expression data sets under samples, the hierarchical clustering were used to display their sample neighbors usually , but theFigure In fact, for t-SNE method that had used to construct gene neighbors, where the initialization dimension of these t-SNE was dimension of genes , 20. HerBased on GDS3837, GDS3257 and GDS3054, nine differentially expressed genes that were associated with lung cancer had been extracted, where these 9 genes that were smoking independent, and they were AGER, CA4, EDNRB, FAM107A, GPM6A, NPR1, PECAM1, RASIP1 and TGFBR3 . Here, wFor samples of gene expression data sets of cancers, there are no clear boundary between subtypes of samples usually . The reaTo efficiently display mini-groups of samples that are generated from MG-PCC algorithm, we firstly verify that PCC and Euclidean clusters of the standardized samples are more consistent compared to the original and normalized ones, and PCC of the standardized samples are the same as the original and normalized ones. Since t-SNE maps have been successful in displaying clusters of Euclidean distance, t-SNE maps of the standardized samples can give good visualizations for mini-groups also. However, for t-SNE maps of the standardized samples, they have significant difference for different parameters, and most of them give poor visualizations for mini-groups also. To select the optimal t-SNE maps of mini-groups, t-SNE-SSP are constructed secondly, where t-SNE-SSP maps are selected from these t-SNE maps of the standardized samples with different perplexity parameter. Results show that that t-SNE-SSP maps give mini-groups of samples good visualizations, and give PCC clusters of samples good visualizations also. However, for t-SNE-SSP maps, when we use them to display PCC clusters of genes, they give fuzzy visualizations. The reason may be that the dimensions of samples are far more than ones of genes. To efficiently map PCC clusters of genes, t-SNE-SGI maps are constructed, where t-SNE-SGI maps are selected from these t-SNE maps of the standardized genes with different initialization dimensions. By several gene expression data sets of cancers, we verify that SNE-SGI maps can give PCC clusters of genes good visualizations. Furthermore, we use t-SNE-SS(m) and t-SNE-SG(n) maps to display the nearest neighbor of samples and genes respectively, which make the relationships between samples(or genes) easy to visualize and understand. In total, for gene expression data sets of cancers, these four types of t-SNE maps identify them easy and intuitive.In this article, we use MG-PCC algorithm to divide samples of gene expression data sets into mini-groups, and t-SNE-SSP to display the relationships of these mini-groups. Moreover, we provide t-SNE-SGI maps to display PCC clusters of genes, and t-SNE-SS(m) and t-SNE-SG(n) maps to display the nearest neighbor of samples and genes respectively. In total, for MG-PCC algorithm and these four types of t-SNE maps, they can help us to understand the entire gene expression data sets when they coordinate with each other.Additional file 1MATLAB algorithm. A freely available MATLAB implemented to perform MG-PCC, t-SNE-SS, t-SNE-SG and draw the nearest sample(or gene) neighbors for a data set. (ZIP 6873 kb)