2012
DOI: 10.1093/bib/bbs046
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A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

Abstract: During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differ… Show more

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Cited by 1,070 publications
(1,030 citation statements)
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References 25 publications
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“…Statistical cutoffs were established at Po0.05 and P-values were corrected for false discovery rate. Because of current controversy surrounding use of reads per kilobase per million (Dillies et al, 2013), normalization methods were validated using a series of established housekeeping genes (Supplementary Figure 1). The genome plot was generated using BRIG (Alikhan et al, 2011).…”
Section: Bioinformatic Analysismentioning
confidence: 99%
“…Statistical cutoffs were established at Po0.05 and P-values were corrected for false discovery rate. Because of current controversy surrounding use of reads per kilobase per million (Dillies et al, 2013), normalization methods were validated using a series of established housekeeping genes (Supplementary Figure 1). The genome plot was generated using BRIG (Alikhan et al, 2011).…”
Section: Bioinformatic Analysismentioning
confidence: 99%
“…Apart from the main sRNAbench programme, a differential expression module based on edgeR (Robinson et al, 2010) was used to generate an expression matrix of all miRNAs detected. Note that by using edgeR, sRNAbench applies implicitly TMM normalisation in the detection of differentially expressed small RNAs, which was reported to be among the most stable methods [42,43]. …”
Section: Methodsmentioning
confidence: 99%
“…Overexpression of SPINK1 in PCa exhibits outlier-expression in ~10-15% of the total PCa cases (Tomlins et al, 2008). Thus, to stratify patients with increased expression of SPINK1, we sorted TCGA patients' samples on the basis of increasing SPINK1 expression (descending order), and divided the dataset into four equal parts by employing Quartile-based normalization method (73), the top 25% of the patients (N=119) corresponding to the upper quartile (QU, log2 (RPM+1)>5.468 or log2 (normalized count+1)>1.892), were assigned as SPINK1 high or SPINK1-positive patient samples and the lower quartile (QL, log2 (RPM+1)<1.124 or log2 (normalized count+1)<-2.611), were considered as SPINK1 low or SPINK1-negative samples. Also, we found about 18 patients with outlier expression of SPINK1 with log2 (RPM+1) of greater than 11.984, which were included in the heat map representation of SPINK1 positive TCGA patients in Figure 1B.…”
Section: Integrative Analyses For Tcga-prad Datamentioning
confidence: 99%