Abstract 5276: Normalization of miRNA-sequencing data.
Bibliographic record
Abstract
Abstract microRNAs (miRNA) are endogenous, small non-coding nucleotides that negatively regulate gene expression post-transcriptionally. Through interactions with Argonaute (Ago) proteins, they form the RNA-induced silencing complex (RISC) and can recognize and bind to the 3’UTR of mRNAs in a sequence-specific manner, leading to translational inhibition or mRNA degradation. Over 30% of human protein-coding genes are predicted to be conserved targets of miRNAs. Consequently, changes in their expression are likely to be associated with the development and progression of diseases, including cancer. An increasing number of studies are utilizing high-throughput sequencing over microarrays for the expression profiling of miRNAs. Processing of the raw sequencing data usually involves filtering based on quality measures, trimming for adapters and mapping the reads to a (genome or miRNA) reference. Then, normalization of the data is crucial before any downstream analysis can be performed. Normalization is the process of removing sources of variation, which are of non-biological origins (stemming from sample handling, library preparation, imaging and so on), and can affect the measured expression levels. An effective normalization method should minimize technical and experimental bias without introducing noise; the differences that remain should be truly biological effects. Several normalization methods for miRNA-seq data have been proposed, including linear scaling, non-linear scaling, quantile normalization and variance stabilization normalization. These methods differ in terms of complexity and the assumptions made. However, no standard technique has been recommended. Read counts from each experiment are usually simply adjusted for differences in sequencing depth (library size) to reads-per-million (RPM). Unfortunately, the performance and appropriateness of any of the normalization methods cannot be assessed using real data because the true values are not known. To this end, we have used a 12x12 Latin Square design to spike in 12 different oligonucleotides with known nominal concentrations, into a complex mixture of human miRNAs. These spike-in pools were subjected to all the preparatory steps of small RNA library construction for sequencing on the Illumina HiSeq2000. Preliminary results show that the spike-in sequences can be recovered successfully from the data. Using this data set, the relative merits of different normalization procedures are being assessed based on measures of bias, variance and improved sensitivity and specificity for the detection of differentially expressed miRNAs. The goal is to identify an optimal normalization method for miRNA-seq data, which would reduce variance without increasing bias. Citation Format: Shirley Tam, Richard de Borja, Ming-Sound Tsao, John D. McPherson. Normalization of miRNA-sequencing data. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5276. doi:10.1158/1538-7445.AM2013-5276
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.044 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".