SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization.
Ontology highlight
ABSTRACT:
SUBMITTER: Katayama S
PROVIDER: S-EPMC3810855 | biostudies-other | 2013 Nov
REPOSITORIES: biostudies-other
Publications
SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization.
Katayama Shintaro S Töhönen Virpi V Linnarsson Sten S Kere Juha J
Bioinformatics (Oxford, England) 20130831 22
<h4>Motivation</h4>Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition; therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differential expression, to enable spike-in normalization and statistical testing based on the estimated absolute number of transcripts per cell for single-cell RNA-seq methods.<h4>Availability and implem ...[more]
Similar Datasets
Project description:High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.
| S-EPMC2838869 | biostudies-literature
Project description:BackgroundHigh-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects.ResultsIn this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors.ConclusionsSimulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN", which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .
| S-EPMC6441199 | biostudies-literature
Project description:BackgroundNext generation sequencing technologies are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key to analyzing massive and complex sequencing data. In order to derive gene expression measures and compare these measures across samples or libraries, we first need to normalize read counts to adjust for varying sample sequencing depths and other potentially technical effects.ResultsIn this paper, we develop a normalization method based on iterating median of M-values (IMM) for detecting the differentially expressed (DE) genes. Compared to a previous approach TMM, the IMM method improves the accuracy of DE detection. Simulation studies show that the IMM method outperforms other methods for the sample normalization. We also look into the real data and find that the genes detected by IMM but not by TMM are much more accurate than the genes detected by TMM but not by IMM. What's more, we discovered that gene UNC5C is highly associated with kidney cancer and so on.
| S-EPMC4181730 | biostudies-literature
Project description:With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.
| S-EPMC9315519 | biostudies-literature
Project description:Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.
| S-EPMC8701051 | biostudies-literature
Project description:By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.
| S-EPMC5668938 | biostudies-literature
Project description:Spike-in normalization is a powerful approach to assess global changes in data obtained from genomic mapping of DNA-associated proteins by methods such as ChIP-sequencing (ChIP-seq) or CUT&RUN. While multiple spike-in methods provide detailed documentation, the implementation of these approaches often omit critical quality control steps and veer from the established procedures. Here, we show that proper application of spike-in normalization can increase quantification accuracy across a spectrum of conditions. Next, we outline how misuse of spike-in approaches can create erroneous biological interpretations and provide guidelines to minimize pitfalls when applying this approach to normalize data from protein-DNA interaction results.
2024-08-05 | GSE273915 | GEO
Project description:UNLABELLED: BACKGROUND:Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment. RESULTS:A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best - on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets. CONCLUSIONS:The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.
| S-EPMC3526407 | biostudies-literature
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress
Project description:MotivationMicrobial communities have been proved to have close relationship with many diseases. The identification of differentially abundant microbial species is clinically meaningful for finding disease-related pathogenic or probiotic bacteria. However, certain characteristics of microbiome data have hurdled the accuracy and effectiveness of differential abundance analysis. The abundances or counts of microbiome species are usually on different scales and exhibit zero-inflation and over-dispersion. Normalization is a crucial step before the differential abundance test. However, existing normalization methods typically try to adjust counts on different scales to a common scale by constructing size factors with the assumption that count distributions across samples are equivalent up to a certain percentile. These methods often yield undesirable results when differentially abundant species are of low to medium abundance level. For differential abundance analysis, existing methods often use a single distribution to model the dispersion of species which lacks flexibility to catch a single species' distinctiveness. These methods tend to detect a lot of false positives and often lack of power when the effect size is small.ResultsWe develop a novel framework for differential abundance analysis on sparse high-dimensional marker gene microbiome data. Our methodology relies on a novel network-based normalization technique and a two-stage zero-inflated mixture count regression model (RioNorm2). Our normalization method aims to find a group of relatively invariant microbiome species across samples and conditions in order to construct the size factor. Another contribution of the paper is that our testing approach can take under-sampling and over-dispersion into consideration by separating microbiome species into two groups and model them separately. Through comprehensive simulation studies, the performance of our method is consistently powerful and robust across different settings with different sample size, library size and effect size. We also demonstrate the effectiveness of our novel framework using a published dataset of metastatic melanoma and find biological insights from the results.Availability and implementationThe R package 'RioNorm2' can be installed from Github athttps://github.com/yuanjing-ma/RioNorm2.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7332570 | biostudies-literature