{'fraction_non_alphanumeric': 0.018018018018018018, 'fraction_numerical': 0.001287001287001287, 'mean_word_length': 5.078125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Simulating and cleaning gene expression data using RUVcorr in the context of inferring gene co-expression

The R package RUVcorr allows the simulation and cleaning of gene expression data using global removal of unwanted variation (RUV) [1] when the aim is the inference of gene co-expression. Besides the RUV procedure, the package offers extensive plotting options related to its application and can simulate realistic gene expression data with a known gene correlation structure. Although the procedures in the RUVcorr package have so far only been applied to microarray gene expression data it should be feasible to apply it to RNA-seq data as well, as long as suitable read-count summaries have been generated and the coverage is sufficient, however this remains untested.