EFSIS: Ensemble Feature Selection Integrating Stability

Ensemble learning that can be used to combine the predictions from multiple learners has been widely applied in pattern recognition, and has been reported to be more robust and accurate than the individual learners. This ensemble logic has recently also been more applied in feature selection. There are basically two strategies for ensemble feature selection, namely data perturbation and function perturbation. Data perturbation performs feature selection on data subsets sampled from the original dataset and then selects the features consistently ranked highly across those data subsets. This has been found to improve both the stability of the selector and the prediction accuracy for a classifier. Function perturbation frees the user from having to decide on the most appropriate selector for any given situation and works by aggregating multiple selectors. This has been found to maintain or improve classification performance. Here we propose a framework, EFSIS, combining these two strategies. Empirical results indicate that EFSIS gives both high prediction accuracy and stability.Comment: 20 pages, 3 figure

Biomarker Discovery Using Statistical and Machine Learning Approaches on Gene Expression Data

My PhD is affiliated with the dCod 1.0 project (https://www.uib.no/en/dcod): decoding the systems toxicology of Atlantic cod (Gadus morhua), which aims to better understand how cods adapt and react to the stressors in the environment. One of the research topics is to discover the biomarkers which discriminate the fish under normal biological status and the ones that are exposed to toxicants. A biomarker, or biological marker, is an indicator of a biological state in response to an intervention, which can be for example toxic exposure (in toxicology), disease (for example cancer), or drug response (in precision medicine). Biomarker discovery is a very important research topic in toxicology, cancer research, and so on. A good set of biomarkers can give insight into the disease / toxicant response mechanisms and be useful to find if the person has the disease / the fish has been exposed to the toxicant. On the molecular level, a biomarker could be "genotype" - for instance a single nucleotide variant linked with a particular disease or susceptibility; another biomarker could be the level of expression of a gene or a set of genes. In this thesis we focus on the latter one, aiming to find out the informative genes that can help to distinguish samples from different groups from the gene expression profiling. Several transcriptomics technologies can be used to generate the necessary data, and among them, DNA microarray and RNA sequencing (RNA-Seq) have become the most useful methods for whole transcriptome gene expression profiling. Especially RNA-Seq has become an attractive alternative to microarrays since it was introduced. Prior to analysis of gene expression, the RNA-Seq data needs to go through a series of processing steps, so a workflow which can automate the process is highly required. Even though many workflows have been proposed to facilitate this process, their application is usually limited to such as model organisms, high-performance computers, computer fluent users, and so on. To fill these gaps, we developed a maximally general RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow), which is applicable to a wide range of applications and requires little programming skills. It takes the sequencing data as input, and maps them to either transcriptome or genome for quantification, and after that the gene expression profile can be achieved which afterwards goes through normalization and statistical tests to find out the differentially expressed genes. This work was presented in Paper I and Paper II. Differential expression analysis used in RASflow, together with other univariate methods are widely used in biomarker discovery for their simplicity and interpretability. But they rely on a hypothesis that variables are independent, so they can only identify variables that possess significant information by themselves. However, biological processes usually involve many variables that have complex interactions. Multivariate methods which take the interactions between variables into consideration are therefore also popular for biomarker discovery. To study whether there is a significant advantage of one over the other, we conducted a comparative study of various methods from these two categories and evaluated these methods on two aspects: stability and prediction accuracy, we found that a method’s performance is quite data-dependent. This work was presented in Paper III. Since the biomarker discovery methods perform quite differently on different datasets, then how to choose the most appropriate one for a particular dataset? One solution is to use the function perturbation strategy to combine the outputs from multiple methods. Function perturbation is capable of maintaining prediction accuracy compared with the original individual methods, but its stability is not satisfactory enough. On the other hand, data perturbation uses a similar ensemble learning logic: it firstly generates multiple datasets by resampling the original dataset and then combines the results from those datasets. Data perturbation has been proven to improve the stability of the biomarker discovery method. We therefore proposed a framework which combines function perturbation with data perturbation: Ensemble Feature Selection Integrating Stability (EFSIS) which achieves both high prediction accuracy and stability. This work was presented in Paper IV

RASflow: an RNA-Seq analysis workflow with Snakemake

Background With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene/transcript in each of the analyzed samples. A number of workflows are available to help researchers perform these steps on their own data, or on public data to take advantage of novel software or reference data in data re-analysis. However, many of the existing workflows are limited to specific types of studies. We therefore aimed to develop a maximally general workflow, applicable to a wide range of data and analysis approaches and at the same time support research on both model and non-model organisms. Furthermore, we aimed to make the workflow usable also for users with limited programming skills. Results Utilizing the workflow management system Snakemake and the package management system Conda, we have developed a modular, flexible and user-friendly RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow). Utilizing Snakemake and Conda alleviates challenges with library dependencies and version conflicts and also supports reproducibility. To be applicable for a wide variety of applications, RASflow supports the mapping of reads to both genomic and transcriptomic assemblies. RASflow has a broad range of potential users: it can be applied by researchers interested in any organism and since it requires no programming skills, it can be used by researchers with different backgrounds. The source code of RASflow is available on GitHub: https://github.com/zhxiaokang/RASflow. Conclusions RASflow is a simple and reliable RNA-Seq analysis workflow covering many use cases.publishedVersio

Crowdsourcing in the Digital Humanities: An Action Research on the Shengxuanhuai Manuscript Transcription

In recent years, there has been an emerging trend in the GLAMs (Galleries, Libraries, Archives and Museums) to leverage crowdsourcing to improve the collection, organization, and evaluation of valuable resources. Although a se-ries of notable crowdsourcing projects in the digital humanities have been launched worldwide, there are few academic studies on investigating the im-plementation and evaluation of such cases. To fill up the research gap, this study aims at conducting a field exploration on the real case called the Shengxuanhuai Manuscript Transcription Initiative (Transcribe Sheng for short). In this poster, action research will be carried out to explore the vari-ous stages of Transcribe Sheng project. Our attempts may shed light on the design and evaluation principles of the crowdsourcing in the digital humani-ties

Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data

Biomarkers are of great importance in many fields, such as cancer research, toxicology, diagnosis and treatment of diseases, and to better understand biological response mechanisms to internal or external intervention. High-throughput gene expression profiling technologies, such as DNA microarrays and RNA sequencing, provide large gene expression data sets which enable data-driven biomarker discovery. Traditional statistical tests have been the mainstream for identifying differentially expressed genes as biomarkers. In recent years, machine learning techniques such as feature selection have gained more popularity. Given many options, picking the most appropriate method for a particular data becomes essential. Different evaluation metrics have therefore been proposed. Being evaluated on different aspects, a method’s varied performance across different datasets leads to the idea of integrating multiple methods. Many integration strategies are proposed and have shown great potential. This chapter gives an overview of the current research advances and existing issues in biomarker discovery using machine learning approaches on gene expression data.publishedVersio