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Love, Michael I.

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Love

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Michael I.

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Love, Michael I.
Author's Publications: search.filters.isAuthorOfPublication.e67b186b-52a1-435a-9b49-5a7ec7247741

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Now showing 1 - 6 of 6
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    MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens
    (BioMed Central, 2014) Li, Wei; Xu, Han; Xiao, Tengfei; Cong, Le; Love, Michael I.; Zhang, Feng; Irizarry, Rafael; Liu, Jun; Brown, Myles; Liu, X Shirley
    We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0554-4) contains supplementary material, which is available to authorized users.
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    Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
    (F1000Research, 2015) Soneson, Charlotte; Love, Michael I.; Robinson, Mark D.
    High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the transcriptome of cells. Many transcriptomic studies aim at comparing either abundance levels or the transcriptome composition between given conditions, and as a first step, the sequencing reads must be used as the basis for abundance quantification of transcriptomic features of interest, such as genes or transcripts. Several different quantification approaches have been proposed, ranging from simple counting of reads that overlap given genomic regions to more complex estimation of underlying transcript abundances. In this paper, we show that gene-level abundance estimates and statistical inference offer advantages over transcript-level analyses, in terms of performance and interpretability. We also illustrate that while the presence of differential isoform usage can lead to inflated false discovery rates in differential expression analyses on simple count matrices and transcript-level abundance estimates improve the performance in simulated data, the difference is relatively minor in several real data sets. Finally, we provide an R package ( tximport) to help users integrate transcript-level abundance estimates from common quantification pipelines into count-based statistical inference engines.
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    A benchmark for RNA-seq quantification pipelines
    (BioMed Central, 2016) Teng, Mingxiang; Love, Michael I.; Davis, Carrie A.; Djebali, Sarah; Dobin, Alexander; Graveley, Brenton R.; Li, Sheng; Mason, Christopher E.; Olson, Sara; Pervouchine, Dmitri; Sloan, Cricket A.; Wei, Xintao; Zhan, Lijun; Irizarry, Rafael
    Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users.
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    Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
    (BioMed Central, 2014) Love, Michael I.; Huber, Wolfgang; Anders, Simon
    In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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    RNA-Seq workflow: gene-level exploratory analysis and differential expression
    (F1000Research, 2015) Love, Michael I.; Anders, Simon; Kim, Vladislav; Huber, Wolfgang
    Here we walk through an end-to-end gene-level RNA-Seq differential expression workflow using Bioconductor packages. We will start from the FASTQ files, show how these were aligned to the reference genome, and prepare a count matrix which tallies the number of RNA-seq reads/fragments within each gene for each sample. We will perform exploratory data analysis (EDA) for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.
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    Erratum to: A benchmark for RNA-seq quantification pipelines
    (BioMed Central, 2016) Teng, Mingxiang; Love, Michael I.; Davis, Carrie A.; Djebali, Sarah; Dobin, Alexander; Graveley, Brenton R.; Li, Sheng; Mason, Christopher E.; Olson, Sara; Pervouchine, Dmitri; Sloan, Cricket A.; Wei, Xintao; Zhan, Lijun; Irizarry, Rafael