MicroRNA Array Normalization: An Evaluation Using a Randomized Dataset as the Benchmark

<div><p>MicroRNA arrays possess a number of unique data features that challenge the assumption key to many normalization methods. We assessed the performance of existing normalization methods using two microRNA array datasets derived from the same set of tumor samples: one dataset was generated using a blocked randomization design when assigning arrays to samples and hence was free of confounding array effects; the second dataset was generated without blocking or randomization and exhibited array effects. The randomized dataset was assessed for differential expression between two tumor groups and treated as the benchmark. The non-randomized dataset was assessed for differential expression after normalization and compared against the benchmark. Normalization improved the true positive rate significantly in the non-randomized data but still possessed a false discovery rate as high as 50%. Adding a batch adjustment step before normalization further reduced the number of false positive markers while maintaining a similar number of true positive markers, which resulted in a false discovery rate of 32% to 48%, depending on the specific normalization method. We concluded the paper with some insights on possible causes of false discoveries to shed light on how to improve normalization for microRNA arrays.</p></div

Results of differential expression analysis of the test data before and after normalization, in comparison with the benchmark data.

<p>Results of differential expression analysis of the test data before and after normalization, in comparison with the benchmark data.</p

Density curves for the benchmark data and the test data with or without normalization.

<p>Each density curve represents the data for one array. Arrays for endometrial samples are colored in blue, and arrays for ovarian samples in red.</p

Scatter plot comparing pooled standard deviations in the benchmark data and that in the test data for (A) no normalization, (B) median normalization, and (C) quantile normalization.

<p>Black “x”: true positive markers. Red “x”: false positive markers. Blue “x”: false negative markers. Black dots: true negative markers.</p

Scatter plot comparing mean differences (ovarian mean – endometrial mean) in the benchmark data and that in the test data for (A) no normalization, (B) median normalization, and (C) quantile normalization.

<p>Black “x”: true positive markers. Red “x”: false positive markers. Blue “x”: false negative markers. Black dots: true negative markers.</p

Results of differential expression analysis of the test data before and after a combination of ComBat and normalization, in comparison with the benchmark data.

<p>Results of differential expression analysis of the test data before and after a combination of ComBat and normalization, in comparison with the benchmark data.</p

Patch for including ice shelf cavities ice shelf cavities in the unstructured-grid, Finite Volume Community Ocean Model

The patch for implementing iceshelf cavities into FVCOM4.0 consits of 1. A text file, git_diff_FVCOM4.0_iceshelf.txt, containing all the code modifications related to the inclusion of ice shelf cavities into FVCOM4.0 source code, generated by git diff. The FVCOM4.0 source code that has been used for this implementataion is published on https://gitlab.ecosystem-modelling.pml.ac.uk/fvcom/uk-fvcom/tags/FVCOM-v4.0. Alternatively, the source can also be obatined from the official FVCOM website http://fvcom.smast.umassd.edu/ and is available to all members of the FVCOM community. 2. A folder, R1CON, contains the input files, namelists and apps options to run the testcase R1CON. Do not hesitate to contact the authors for further instructions / help for applying FVCOM-ice-shelf applications

Data_Sheet_3_Utility of Serum EB Virus Zta Antibody in the Diagnostic of Nasopharyngeal Carcinoma: Evidences From 2,126 Cases and 15,644 Controls.docx

We collected previous published data and performed a systematical assessment for the diagnostic value of serum Zta antibody in NPC patients. Using bivariate-mixed effect model, we calculated the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnosis odds ratio (DOR), and summary receiver operating characteristics curve (AUC) and their 95% confidence intervals (CIs). We also performed subgroup analysis to explore the heterogeneity. We included 23 studies including 24 pieces of data and 17,770 study subjects (2,126 cases and 15,644 controls). The overall combined sensitivity was 0.85 (95%CI: 0.80–0.89) and the combined specificity was 0.90 (95%CI: 0.87–0.93). The summarized AUC was 0.94 with 95%CI of 0.92–0.96. The PLR was 8.9 (95%CI: 6.4–12.2) and the NLR was 0.17(95%CI: 0.12–0.23). The diagnostic odds ratio was 53 (95%CI: 32–87). For publication year, the sensitivity was 0.88 (95%CI: 0.84–0.91) and the specificity was 0.90 (95%CI: 0.84–0.93) for ≤2006. The AUC, PLR, NLR and DOR were 0.94, 8.8, 0.13, and 64. The pooled results were similar for >2006 group. For different sample size, the pooled AUC was 0.94 for ≤Median and was 0.95 for >Median that were very close to the overall estimations. For different population setting, no overlap was found in the sensitivity (0.84 vs. 0.87), specificity (0.90 vs. 0.84), PLR (8.7 vs. 5.5), NLR (0.16 vs. 0.08–0.33), DOR (49 vs. 35), and AUC (0.94 vs. 0.92) between Asian and others. The serum EBV antibody examination has high diagnostic accuracy for early-stage NPC. The diagnostic accuracy seems not to be influenced by sample size, publication year, and ethnic. Considering the few numbers of study with non-Asian population, the present results need to be confirmed in other population setting.</p

Data_Sheet_2_Utility of Serum EB Virus Zta Antibody in the Diagnostic of Nasopharyngeal Carcinoma: Evidences From 2,126 Cases and 15,644 Controls.docx

We collected previous published data and performed a systematical assessment for the diagnostic value of serum Zta antibody in NPC patients. Using bivariate-mixed effect model, we calculated the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnosis odds ratio (DOR), and summary receiver operating characteristics curve (AUC) and their 95% confidence intervals (CIs). We also performed subgroup analysis to explore the heterogeneity. We included 23 studies including 24 pieces of data and 17,770 study subjects (2,126 cases and 15,644 controls). The overall combined sensitivity was 0.85 (95%CI: 0.80–0.89) and the combined specificity was 0.90 (95%CI: 0.87–0.93). The summarized AUC was 0.94 with 95%CI of 0.92–0.96. The PLR was 8.9 (95%CI: 6.4–12.2) and the NLR was 0.17(95%CI: 0.12–0.23). The diagnostic odds ratio was 53 (95%CI: 32–87). For publication year, the sensitivity was 0.88 (95%CI: 0.84–0.91) and the specificity was 0.90 (95%CI: 0.84–0.93) for ≤2006. The AUC, PLR, NLR and DOR were 0.94, 8.8, 0.13, and 64. The pooled results were similar for >2006 group. For different sample size, the pooled AUC was 0.94 for ≤Median and was 0.95 for >Median that were very close to the overall estimations. For different population setting, no overlap was found in the sensitivity (0.84 vs. 0.87), specificity (0.90 vs. 0.84), PLR (8.7 vs. 5.5), NLR (0.16 vs. 0.08–0.33), DOR (49 vs. 35), and AUC (0.94 vs. 0.92) between Asian and others. The serum EBV antibody examination has high diagnostic accuracy for early-stage NPC. The diagnostic accuracy seems not to be influenced by sample size, publication year, and ethnic. Considering the few numbers of study with non-Asian population, the present results need to be confirmed in other population setting.</p

Data_Sheet_1_Utility of Serum EB Virus Zta Antibody in the Diagnostic of Nasopharyngeal Carcinoma: Evidences From 2,126 Cases and 15,644 Controls.doc

We collected previous published data and performed a systematical assessment for the diagnostic value of serum Zta antibody in NPC patients. Using bivariate-mixed effect model, we calculated the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnosis odds ratio (DOR), and summary receiver operating characteristics curve (AUC) and their 95% confidence intervals (CIs). We also performed subgroup analysis to explore the heterogeneity. We included 23 studies including 24 pieces of data and 17,770 study subjects (2,126 cases and 15,644 controls). The overall combined sensitivity was 0.85 (95%CI: 0.80–0.89) and the combined specificity was 0.90 (95%CI: 0.87–0.93). The summarized AUC was 0.94 with 95%CI of 0.92–0.96. The PLR was 8.9 (95%CI: 6.4–12.2) and the NLR was 0.17(95%CI: 0.12–0.23). The diagnostic odds ratio was 53 (95%CI: 32–87). For publication year, the sensitivity was 0.88 (95%CI: 0.84–0.91) and the specificity was 0.90 (95%CI: 0.84–0.93) for ≤2006. The AUC, PLR, NLR and DOR were 0.94, 8.8, 0.13, and 64. The pooled results were similar for >2006 group. For different sample size, the pooled AUC was 0.94 for ≤Median and was 0.95 for >Median that were very close to the overall estimations. For different population setting, no overlap was found in the sensitivity (0.84 vs. 0.87), specificity (0.90 vs. 0.84), PLR (8.7 vs. 5.5), NLR (0.16 vs. 0.08–0.33), DOR (49 vs. 35), and AUC (0.94 vs. 0.92) between Asian and others. The serum EBV antibody examination has high diagnostic accuracy for early-stage NPC. The diagnostic accuracy seems not to be influenced by sample size, publication year, and ethnic. Considering the few numbers of study with non-Asian population, the present results need to be confirmed in other population setting.</p