ImpulseDE: detection of differentially expressed genes in time series data using impulse models

Sander, Jil; Schultze, Joachim L.; Yosef, Nir

doi:10.1093/bioinformatics/btw665

Cited by 39 publications

(46 citation statements)

References 21 publications

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“…Conversely, we observed a substantial overlap between the H3K27ac peaks and the ATAC-seq peaks, with an overall 60% (23,294) of the H3K27ac peaks overlapping an accessible region at the same point in time. Using strict criteria [FDR< 0.05; FDR<0.01 Methods; table S1; (23,24)], as in the gene expression analysis, we found 2,435 ATAC-seq and 2,024 H3K27ac peaks that were differentially enriched between time points, henceforth referred to as temporal H3K27ac or ATAC-seq peaks. Similar clustering of H3K27me3 peaks showed weaker temporal signal (Methods) and a smaller number of temporal peaks ( fig.…”

Section: The Neural Induction-associated Regulomementioning

confidence: 99%

Massively parallel characterization of regulatory dynamics during neural induction

Inoue

Kreimer²,

Ashuach³

et al. 2018

Preprint

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The molecular components governing neural induction remain largely unknown. Here, we applied a suite of genomic and computational tools to comprehensively identify these components. We performed RNA-seq, ChIP-seq (H3K27ac, H3K27me3) and ATAC-seq on human embryonic stem cells (hESCs) at seven early neural differentiation time points (0-72 hours) and identified thousands of induced genes and regulatory regions. We analyzed the function of ~2,500 selected regions using massively parallel reporter assays at all time points. We found numerous temporal enhancers that correlated with similarly timed epigenetic marks and gene expression. Development of a prioritization method that incorporated all genomic data identified key transcription factors (TFs) involved in neural induction. Individual overexpression of eleven TFs and several combinations in hESCs found novel neural induction regulators. Combined, our results provide a comprehensive map of genes and functional regulatory elements involved in neural induction and identify master regulator TFs that are instrumental for this process.One Sentence SummaryUsing numerous genomic assays and computational tools we characterized the dynamic changes that take place during neural induction.

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Section: The Neural Induction-associated Regulomementioning

confidence: 99%

Massively parallel characterization of regulatory dynamics during neural induction

Inoue

Kreimer²,

Ashuach³

et al. 2018

Preprint

Self Cite

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“…Figure 3a illustrates this process using populations 2 and 3 in the tree as an example. Notably, in the continuous mode, this formulation can give rise to a rich set of patterns of changes in gene expression from root ('progenitor cells') to leaves ('target cells'), including the commonly observed impulse profile 28,29 (Figure S1c-d). As an alternative, we also implemented a mode for simulating continuous data by which gene expression from root to leaves is determined explicitly by an impulse function.…”

Section: The Second Knob: Extrinsic Variation Via Extrinsic Variabilimentioning

confidence: 99%

SymSim: simulating multi-faceted variability in single cell RNA sequencing

Zhang

Yosef

2018

Preprint

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The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in-silico platforms for evaluation and validation. Simulated datasets which resemble the properties of real datasets can aid in method development and prioritization as well as in questions in experimental design by providing an objective ground truth. Here, we present SymSim, a simulator software that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation that is indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. Unlike other simulators, the parameters that govern the simulation process directly represent meaningful properties such as mRNA capture rate, the number of PCR cycles, sequencing depth, or the use of unique molecular identifiers. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population and how this number deviates from the theoretical lower bound as the quality of the data decreases. SymSim is publicly available as an R package and allows users to simulate datasets with desired properties or matched with experimental data.

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“…To test LPWC, we simulated time series gene expression data using an impulse model called ImpulseDE [16]. Impulses are one common type of temporal pattern in gene expression data [1].…”

Section: Simulated Time Seriesmentioning

confidence: 99%

“…The Short Time-series Expression Miner (STEM) enumerates temporal template profiles and matches genes to them so it works best for short time series (3-8 timepoints) [14]. DynaMiteC [15] clusters genes by fitting them to prototype impulse models [16], but impulses are only one type of common temporal pattern [1]. DynOmics uses fast Fourier transform to model expression values using mixtures of cyclic patterns [17].…”

Section: Introductionmentioning

confidence: 99%

Lag Penalized Weighted Correlation for Time Series Clustering

Chandereng

Gitter

2018

Preprint

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Motivation:The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure. Results: We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns pairs of time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies. Availability: The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT

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ImpulseDE: detection of differentially expressed genes in time series data using impulse models

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 39 publications

References 21 publications

Massively parallel characterization of regulatory dynamics during neural induction

Massively parallel characterization of regulatory dynamics during neural induction

SymSim: simulating multi-faceted variability in single cell RNA sequencing

Lag Penalized Weighted Correlation for Time Series Clustering

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