Microbiome is defined as the collective genomes of an ecological community of microorganisms. Data analysis of microbiome data is challenged by the compositional and sparse nature of the data and the inherent dependency between microorganisms. In addition, microbiome studies suffer from unwanted sources of variation termed "batch effects" that may confound the effect of interest (e.g. treatment). Batch effects may be biological (e.g. diets, husbandry), technical (technician, sequencing) and / or computational (software, bioinformatics pipeline). They are often unavoidable in practice despite good experimental designs. We present different methods to remove or account for batch effects. These methods have different application types depending on their assumptions and the overall analysis aims. However, most were developed for microarray or RNA-Seq data. We present several strategies to choose the appropriate methods to adjust for batch effects and assess the efficiency of these approaches for batch effect removal in microbiome data.