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A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.

ABSTRACT: Understanding the differences between microarray and RNA-Seq technologies for measuring gene expression is necessary for informed design of experiments and choice of data analysis methods. Previous comparisons have come to sometimes contradictory conclusions, which we suggest result from a lack of attention to the intensity-dependent nature of variation generated by the technologies. To examine this trend, we carried out a parallel nested experiment performed simultaneously on the two technologies that systematically split variation into four stages (treatment, biological variation, library preparation and chip/lane noise), allowing a separation and comparison of the sources of variation in a well-controlled cellular system, Saccharomyces cerevisiae. With this novel dataset, we demonstrate that power and accuracy are more dependent on per-gene read depth in RNA-Seq than they are on fluorescence intensity in microarrays. However, we carried out quantitative PCR validations which indicate that microarrays may demonstrate greater systematic bias in low-intensity genes than in RNA-seq.

SUBMITTER: Robinson DG

PROVIDER: S-EPMC4787771 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.

Robinson David G DG Wang Jean Y JY Storey John D JD

Nucleic acids research 20150630 20

Understanding the differences between microarray and RNA-Seq technologies for measuring gene expression is necessary for informed design of experiments and choice of data analysis methods. Previous comparisons have come to sometimes contradictory conclusions, which we suggest result from a lack of attention to the intensity-dependent nature of variation generated by the technologies. To examine this trend, we carried out a parallel nested experiment performed simultaneously on the two technologi ...[more]

PMID: 26130709

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Project description:We developed a new lossless compression method for WIG data, named smallWig, offering the best known compression rates for RNA-seq data and featuring random access functionalities that enable visualization, summary statistics analysis and fast queries from the compressed files. Our approach results in order of magnitude improvements compared with bigWig and ensures compression rates only a fraction of those produced by cWig. The key features of the smallWig algorithm are statistical data analysis and a combination of source coding methods that ensure high flexibility and make the algorithm suitable for different applications. Furthermore, for general-purpose file compression, the compression rate of smallWig approaches the empirical entropy of the tested WIG data. For compression with random query features, smallWig uses a simple block-based compression scheme that introduces only a minor overhead in the compression rate. For archival or storage space-sensitive applications, the method relies on context mixing techniques that lead to further improvements of the compression rate. Implementations of smallWig can be executed in parallel on different sets of chromosomes using multiple processors, thereby enabling desirable scaling for future transcriptome Big Data platforms.The development of next-generation sequencing technologies has led to a dramatic decrease in the cost of DNA/RNA sequencing and expression profiling. RNA-seq has emerged as an important and inexpensive technology that provides information about whole transcriptomes of various species and organisms, as well as different organs and cellular communities. The vast volume of data generated by RNA-seq experiments has significantly increased data storage costs and communication bandwidth requirements. Current compression tools for RNA-seq data such as bigWig and cWig either use general-purpose compressors (gzip) or suboptimal compression schemes that leave significant room for improvement. To substantiate this claim, we performed a statistical analysis of expression data in different transform domains and developed accompanying entropy coding methods that bridge the gap between theoretical and practical WIG file compression rates.We tested different variants of the smallWig compression algorithm on a number of integer-and real- (floating point) valued RNA-seq WIG files generated by the ENCODE project. The results reveal that, on average, smallWig offers 18-fold compression rate improvements, up to 2.5-fold compression time improvements, and 1.5-fold decompression time improvements when compared with bigWig. On the tested files, the memory usage of the algorithm never exceeded 90 KB. When more elaborate context mixing compressors were used within smallWig, the obtained compression rates were as much as 23 times better than those of bigWig. For smallWig used in the random query mode, which also supports retrieval of the summary statistics, an overhead in the compression rate of roughly 3-17% was introduced depending on the chosen system parameters. An increase in encoding and decoding time of 30% and 55% represents an additional performance loss caused by enabling random data access. We also implemented smallWig using multi-processor programming. This parallelization feature decreases the encoding delay 2-3.4 times compared with that of a single-processor implementation, with the number of processors used ranging from 2 to 8; in the same parameter regime, the decoding delay decreased 2-5.2 times.The smallWig software can be downloaded from: http://stanford.edu/~zhiyingw/smallWig/smallwig.html, http://publish.illinois.edu/milenkovic/, http://web.stanford.edu/~tsachy/.zhiyingw@stanford.eduSupplementary data are available at Bioinformatics online.

Project description:BackgroundDirect cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons.MethodsHerein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+ and CD5- cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG).ResultsWhen using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies.ConclusionslcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.

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A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.

Publications

A nested parallel experiment demonstrates differences in intensity-dependence between RNA-seq and microarrays.

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