The in-depth understanding of human microbiome has dramatically reshaped our understanding of the relationship between human health and microbiome. A tremendous number of studies have demonstrated that microbiome residing in human body are key contributors in modulating host physiology and metabolism. As the second genome of human being, microbiome is thought to be responsible for the complex pathophysiology nature of various diseases, including neurological, metabolic and immunity disorders, etc. Undeniably, the revolution in DNA sequencing technologies has enabled us to generate massive amounts of microbial data and accelerate the progression of studies and researches to explore the relationship between microbiome and human health. Thus, a growing number of hospitals and medical centers endeavored largely to recruit volunteers and collect bio-samples associated with microbiome. For example, the Human Microbiome Project (HMP) in 2007 expanded our understanding over the microbiome of healthy human body and its physiological roles in human genetic and metabolic landscapes. Furthermore, emerging evidence indicate that microbiome could serve as additional biomarkers as diagnostic and therapeutic targets, for example 30 bacteria taxa identified from a cohort study could distinguish patients with early hepatocellular carcinoma with AUC of 80.64% and Bacteroides vulgatus may alter bile acid metabolism to improve risk of polycystic ovary syndrome. In this regard, there is an urgent necessity to integrate microbial data into clinical practice for evidence-based medicine.

With the advancement of NGS and bioinformatics in basic and clinical biomedicine investigation, mathematics and statistical approaches in microbial downstream analysis are able to provide us comprehensive information of the relationship between human microbiome and human health and diseases. For example, diversity metric was introduced from ecology to access microbiota richness, while machine learning technology was popularly used for bacteria biomarkers screening. In order to perform such measurements, clinical researchers usually have to take additional bioinformatics courses, which significantly obstruct the progression and frustrate amateurs without computational and coding experience. First, clinical meta-data generally consists of a wild range of information including but not limited to age, BMI, gender and medical diagnostics, which brings about giant challenges for researchers to estimate and select proper features and determine inclusion criteria. Moreover, in many retrospective studies, due to the complexity of subjects in hospital, clinicians are not able to clearly determine grouping information based on meta data, which challenges clinical researchers, especially various missing value in meta data. Second, a large scale of microbial data always contains various information bias. For example, low abundance and occurrence taxa are often observed in microbial data analysis, which may due to experimental contamination, sequence alignment error, and other factors. Normally, these taxa are filtered in downstream analysis according to study design and researchers’ experience due to the lack of a well-recognized protocol, which may lead to biased and poorly reproducible results. Especially, due to poor coding abilities, clinical researchers may find unexpected difficulties without knowing in this filter process. Third, although many existing softwares and R packages have been developed and integrated multiple method from various field, none of them are specially designed for clinical studies and couldn’t address problems, such as data missing, data filtering and sample regrouping easily and efficiently. Moreover, due to large and comprehensive function and workflow, clinical researchers may spend additional time to learn and modify clinical data. The manual step to select the most appropriate parameters is still puzzling and tedious, and inconsistent application of such tools may reduce the reproducibility of the results. Thus, an efficient and convenient tool to meet the fast-developed clinical microbial studies is necessary.

Here, EasyMicroPlot incorporates packages used in basic and clinical microbial studies for data analysis and visualization. In this package, regular downstream analysis covering core tasks of metagenomic analysis could be performed efficiently and conveniently in this field.