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A Zipf-plot based normalization method for high-throughput RNA-seq data.

ABSTRACT: Normalization is crucial in RNA-seq data analyses. Due to the existence of excessive zeros and a large number of small measures, it is challenging to find reliable linear rescaling normalization parameters. We propose a Zipf plot based normalization method (ZN) assuming that all gene profiles have similar upper tail behaviors in their expression distributions. The new normalization method uses global information of all genes in the same profile without gene-level expression alteration. It doesn't require the majority of genes to be not differentially expressed (DE), and can be applied to data where the majority of genes are weakly or not expressed. Two normalization schemes are implemented with ZN: a linear rescaling scheme and a non-linear transformation scheme. The linear rescaling scheme can be applied alone or together with the non-linear normalization scheme. The performance of ZN is benchmarked against five popular linear normalization methods for RNA-seq data. Results show that the linear rescaling normalization scheme by itself works well and is robust. The non-linear normalization scheme can further improve the normalization outcomes and is optional if the Zipf plots show parallel patterns.

SUBMITTER: Wang B

PROVIDER: S-EPMC7144957 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Zipf-plot based normalization method for high-throughput RNA-seq data.

Wang Bin B

PloS one 20200409 4

Normalization is crucial in RNA-seq data analyses. Due to the existence of excessive zeros and a large number of small measures, it is challenging to find reliable linear rescaling normalization parameters. We propose a Zipf plot based normalization method (ZN) assuming that all gene profiles have similar upper tail behaviors in their expression distributions. The new normalization method uses global information of all genes in the same profile without gene-level expression alteration. It doesn' ...[more]

PMID: 32271772

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Project description:Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

Project description:BackgroundHigh throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.ResultsWe introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells.ConclusionsAccording to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Project description:BACKGROUND:Single nucleotide polymorphisms (SNP) have been applied as important molecular markers in genetics and breeding studies. The rapid advance of next generation sequencing (NGS) provides a high-throughput means of SNP discovery. However, SNP development is limited by the availability of reliable SNP discovery methods. Especially, the optimum assembler and SNP caller for accurate SNP prediction from next generation sequencing data are not known. RESULTS:Herein we performed SNP prediction based on RNA-seq data of peach and mandarin peel tissue under a comprehensive comparison of two paired-end read lengths (125?bp and 150?bp), five assemblers (Trinity, IDBA, oases, SOAPdenovo, Trans-abyss) and two SNP callers (GATK and GBS). The predicted SNPs were compared with the authentic SNPs identified via PCR amplification followed by gene cloning and sequencing procedures. A total of 40 and 240 authentic SNPs were presented in five anthocyanin biosynthesis related genes in peach and in nine carotenogenic genes in mandarin. Putative SNPs predicted from the same RNA-seq data with different strategies led to quite divergent results. The rate of false positive SNPs was significantly lower when the paired-end read length was 150?bp compared with 125?bp. Trinity was superior to the other four assemblers and GATK was substantially superior to GBS due to a low rate of missing authentic SNPs. The combination of assembler Trinity, SNP caller GATK, and the paired-end read length 150?bp had the best performance in SNP discovery with 100% accuracy both in peach and in mandarin cases. This strategy was applied to the characterization of SNPs in peach and mandarin transcriptomes. CONCLUSIONS:Through comparison of authentic SNPs obtained by PCR cloning strategy and putative SNPs predicted from different combinations of five assemblers, two SNP callers, and two paired-end read lengths, we provided a reliable and efficient strategy, Trinity-GATK with 150?bp paired-end read length, for SNP discovery from RNA-seq data. This strategy discovered SNP at 100% accuracy in peach and mandarin cases and might be applicable to a wide range of plants and other organisms.

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A Zipf-plot based normalization method for high-throughput RNA-seq data.

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A Zipf-plot based normalization method for high-throughput RNA-seq data.

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