title: The choice of the filtering method in microarrays affects the inference regarding dosage compensation of the active X-chromosome.
abstract: BACKGROUND
  The hypothesis of dosage compensation of genes of the X chromosome, supported by previous microarray studies, was recently challenged by RNA-sequencing data. It was suggested that microarray studies were biased toward an over-estimation of X-linked expression levels as a consequence of the filtering of genes below the detection threshold of microarrays.
  
  
  RESULTS
  To investigate this hypothesis, we used microarray expression data from circulating monocytes in 1,467 individuals. In total, 25,349 and 1,156 probes were unambiguously assigned to autosomes and the X chromosome, respectively. Globally, there was a clear shift of X-linked expressions toward lower levels than autosomes. We compared the ratio of expression levels of X-linked to autosomal transcripts (X∶AA) using two different filtering methods: 1. gene expressions were filtered out using a detection threshold irrespective of gene chromosomal location (the standard method in microarrays); 2. equal proportions of genes were filtered out separately on the X and on autosomes. For a wide range of filtering proportions, the X∶AA ratio estimated with the first method was not significantly different from 1, the value expected if dosage compensation was achieved, whereas it was significantly lower than 1 with the second method, leading to the rejection of the hypothesis of dosage compensation. We further showed in simulated data that the choice of the most appropriate method was dependent on biological assumptions regarding the proportion of actively expressed genes on the X chromosome comparative to the autosomes and the extent of dosage compensation.
  
  
  CONCLUSIONS
  This study shows that the method used for filtering out lowly expressed genes in microarrays may have a major impact according to the hypothesis investigated. The hypothesis of dosage compensation of X-linked genes cannot be firmly accepted or rejected using microarray-based data.
authors.affiliation: INSERM UMRS 937, Pierre and Marie Curie University (UPMC, Paris 6) and Medical School, Paris, France.
authors.forename: Raphaële
authors.initials: R
authors.lastname: Castagné
authors.forename: Maxime
authors.initials: M
authors.lastname: Rotival
authors.forename: Tanja
authors.initials: T
authors.lastname: Zeller
authors.forename: Philipp S
authors.initials: PS
authors.lastname: Wild
authors.forename: Vinh
authors.initials: V
authors.lastname: Truong
authors.forename: David-Alexandre
authors.initials: DA
authors.lastname: Trégouët
authors.forename: Thomas
authors.initials: T
authors.lastname: Munzel
authors.forename: Andreas
authors.initials: A
authors.lastname: Ziegler
authors.forename: François
authors.initials: F
authors.lastname: Cambien
authors.forename: Stefan
authors.initials: S
authors.lastname: Blankenberg
authors.forename: Laurence
authors.initials: L
authors.lastname: Tiret
mesh_terms: D000818:Animals; D041321:Chromosomes, Human, X / Q000235:genetics*; D004303:Dosage Compensation, Genetic / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling; D050172:Genes, X-Linked / Q000235:genetics; D018392:Genomic Imprinting; D057809:HEK293 Cells; D006801:Humans; D008297:Male; D051379:Mice; D008957:Models, Genetic; D009000:Monocytes / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism
chemical_list: D012333:RNA, Messenger