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Author

Yifang Hu

Other affiliations: University of Melbourne
Bio: Yifang Hu is a academic researcher from Walter and Eliza Hall Institute of Medical Research. The author has contributed to research in topic(s): Haematopoiesis & Progenitor cell. The author has an hindex of 23, co-authored 34 publication(s) receiving 16561 citation(s). Previous affiliations of Yifang Hu include University of Melbourne.

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Topics: Haematopoiesis, Progenitor cell, Leukemia ...read more
Papers
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Journal ArticleDOI
Matthew E. Ritchie1, Belinda Phipson2, Di Wu3, Yifang Hu1  +4 moreInstitutions (5)
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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13,819 citations


Journal ArticleDOI
Yifang Hu1, Gordon K. Smyth1Institutions (1)
TL;DR: ELDA is a software application for limiting dilution analysis (LDA), with particular attention to the needs of stem cell assays, which is the first limiting dilutions analysis software to provide meaningful confidence intervals for all LDA data sets, including those with 0% or 100% responses.

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Abstract: ELDA is a software application for limiting dilution analysis (LDA), with particular attention to the needs of stem cell assays It is the first limiting dilution analysis software to provide meaningful confidence intervals for all LDA data sets, including those with 0% or 100% responses Other features include a test of the adequacy of the single-hit hypothesis, tests for frequency differences between multiple data sets, and the ability to take advantage of cases where the number of cells in the sample is counted exactly A webtool at http://bioinfwehieduau/software/elda/ provides an easy user interface

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1,364 citations


Journal ArticleDOI
Linda M. Wakim1, Amanda Woodward-Davis1, Ruijie Liu2, Yifang Hu2  +4 moreInstitutions (3)
TL;DR: Comparing the gene expression profiles of Trm cells isolated from the brain with those of circulating memory T cellsisolated from the spleen after an acute virus infection indicates that Trm Cells are a distinct memory T cell population disconnected from the circulatingMemory T cell pool and display a unique molecular signature that likely results in optimal survival and function within their local environment.

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Abstract: Tissue resident memory (Trm) CD8 T cells represent a newly described memory T cell population. We have previously characterized a population of Trm cells that persists within the brain after acute virus infection. Although capable of providing marked protection against a subsequent local challenge, brain Trm cells do not undergo recall expansion after dissociation from the tissue. Furthermore, these Trm cells do not depend on the same survival factors as the circulating memory T cell pool as assessed either in vivo or in vitro. To gain greater insight into this population of cells, we compared the gene expression profiles of Trm cells isolated from the brain with those of circulating memory T cells isolated from the spleen after an acute virus infection. Trm cells displayed altered expression of genes involved in chemotaxis, expressed a distinct set of transcription factors, and overexpressed several inhibitory receptors. Cumulatively, these data indicate that Trm cells are a distinct memory T cell population disconnected from the circulating memory T cell pool and display a unique molecular signature that likely results in optimal survival and function within their local environment.

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267 citations


Journal ArticleDOI
Jacob T. Jackson1, Yifang Hu1, Ruijie Liu1, Frederick Masson1  +16 moreInstitutions (2)
06 Jul 2011-The EMBO Journal
TL;DR: It is shown that Id2 is broadly expressed in all cDC subsets with the highest expression in CD103+ and CD8α+ lineages and that Irf‐8 and Batf3 regulate distinct stages in DC differentiation during the development of cDCs.

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Abstract: Dendritic cells (DCs) have critical roles in the induction of the adaptive immune response. The transcription factors Id2, Batf3 and Irf-8 are required for many aspects of murine DC differentiation including development of CD8α+ and CD103+ DCs. How they regulate DC subset specification is not completely understood. Using an Id2-GFP reporter system, we show that Id2 is broadly expressed in all cDC subsets with the highest expression in CD103+ and CD8α+ lineages. Notably, CD103+ DCs were the only DC able to constitutively cross-present cell-associated antigens in vitro. Irf-8 deficiency affected loss of development of virtually all conventional DCs (cDCs) while Batf3 deficiency resulted in the development of Sirp-α− DCs that had impaired survival. Exposure to GM-CSF during differentiation induced expression of CD103 in Id2-GFP+ DCs. It did not restore cross-presenting capacity to Batf3−/− or CD103−Sirp-α−DCs in vitro. Thus, Irf-8 and Batf3 regulate distinct stages in DC differentiation during the development of cDCs. Genetic mapping DC subset differentiation using Id2-GFP may have broad implications in understanding the interplay of DC subsets during protective and pathological immune responses.

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127 citations


Journal ArticleDOI
05 Aug 2010-Blood
TL;DR: It is shown that mice with loss of function mutations in PRC2 components display enhanced HSC/progenitor population activity, whereas mutations that disrupt PRC1 or pleiohomeotic repressive complex are associated with HSC or progenitor cell defects.

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Abstract: Polycomb group (PcG) proteins are transcriptional repressors with a central role in the establishment and maintenance of gene expression patterns during development. We have investigated the role of polycomb repressive complexes (PRCs) in hematopoietic stem cells (HSCs) and progenitor populations. We show that mice with loss of function mutations in PRC2 components display enhanced HSC/progenitor population activity, whereas mutations that disrupt PRC1 or pleiohomeotic repressive complex are associated with HSC/progenitor cell defects. Because the hierarchical model of PRC action would predict synergistic effects of PRC1 and PRC2 mutation, these opposing effects suggest this model does not hold true in HSC/progenitor cells. To investigate the molecular targets of each complex in HSC/progenitor cells, we measured genome-wide expression changes associated with PRC deficiency, and identified transcriptional networks that are differentially regulated by PRC1 and PRC2. These studies provide new insights into the mechanistic interplay between distinct PRCs and have important implications for approaching PcG proteins as therapeutic targets.

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122 citations


Cited by
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Journal ArticleDOI
Matthew E. Ritchie1, Belinda Phipson2, Di Wu3, Yifang Hu1  +4 moreInstitutions (5)
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

...read more

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

...read more

13,819 citations


Journal ArticleDOI
TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.

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Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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4,666 citations


Journal ArticleDOI
07 May 2016-The Lancet
TL;DR: Treatment with atezolizumab resulted in a significantly improved RECIST v1.1 response rate, compared with a historical control overall response rate of 10%, and Exploratory analyses showed The Cancer Genome Atlas (TCGA) subtypes and mutation load to be independently predictive for response to atezolediazepine.

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Abstract: Summary Background Patients with metastatic urothelial carcinoma have few treatment options after failure of platinum-based chemotherapy. In this trial, we assessed treatment with atezolizumab, an engineered humanised immunoglobulin G1 monoclonal antibody that binds selectively to programmed death ligand 1 (PD-L1), in this patient population. Methods For this multicentre, single-arm, two-cohort, phase 2 trial, patients (aged ≥18 years) with inoperable locally advanced or metastatic urothelial carcinoma whose disease had progressed after previous platinum-based chemotherapy were enrolled from 70 major academic medical centres and community oncology practices in Europe and North America. Key inclusion criteria for enrolment were Eastern Cooperative Oncology Group performance status of 0 or 1, measurable disease defined by Response Evaluation Criteria In Solid Tumors version 1.1 (RECIST v1.1), adequate haematological and end-organ function, and no autoimmune disease or active infections. Formalin-fixed paraffin-embedded tumour specimens with sufficient viable tumour content were needed from all patients before enrolment. Patients received treatment with intravenous atezolizumab (1200 mg, given every 3 weeks). PD-L1 expression on tumour-infiltrating immune cells (ICs) was assessed prospectively by immunohistochemistry. The co-primary endpoints were the independent review facility-assessed objective response rate according to RECIST v1.1 and the investigator-assessed objective response rate according to immune-modified RECIST, analysed by intention to treat. A hierarchical testing procedure was used to assess whether the objective response rate was significantly higher than the historical control rate of 10% at an α level of 0·05. This study is registered with ClinicalTrials.gov, number NCT02108652. Findings Between May 13, 2014, and Nov 19, 2014, 486 patients were screened and 315 patients were enrolled into the study. Of these patients, 310 received atezolizumab treatment (five enrolled patients later did not meet eligibility criteria and were not dosed with study drug). The PD-L1 expression status on infiltrating immune cells (ICs) in the tumour microenvironment was defined by the percentage of PD-L1-positive immune cells: IC0 ( Interpretation Atezolizumab showed durable activity and good tolerability in this patient population. Increased levels of PD-L1 expression on immune cells were associated with increased response. This report is the first to show the association of TCGA subtypes with response to immune checkpoint inhibition and to show the importance of mutation load as a biomarker of response to this class of agents in advanced urothelial carcinoma. Funding F Hoffmann-La Roche Ltd.

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2,369 citations


Journal ArticleDOI
Mihaela Pertea1, Daehwan Kim1, Geo Pertea1, Jeffrey T. Leek1  +1 moreInstitutions (1)
01 Sep 2016-Nature Protocols
TL;DR: This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts.

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Abstract: High-throughput sequencing of mRNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate and flexible software to reduce the raw read data to comprehensible results. HISAT (hierarchical indexing for spliced alignment of transcripts), StringTie and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol's execution time depends on the computing resources, but it typically takes under 45 min of computer time. HISAT, StringTie and Ballgown are available from http://ccb.jhu.edu/software.shtml.

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2,234 citations


Journal ArticleDOI
TL;DR: Comparing the performance of UMAP with five other tools, it is found that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters.

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Abstract: Advances in single-cell technologies have enabled high-resolution dissection of tissue composition. Several tools for dimensionality reduction are available to analyze the large number of parameters generated in single-cell studies. Recently, a nonlinear dimensionality-reduction technique, uniform manifold approximation and projection (UMAP), was developed for the analysis of any type of high-dimensional data. Here we apply it to biological data, using three well-characterized mass cytometry and single-cell RNA sequencing datasets. Comparing the performance of UMAP with five other tools, we find that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters. The work highlights the use of UMAP for improved visualization and interpretation of single-cell data.

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1,628 citations


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Performance
Metrics

Author's H-index: 23

No. of papers from the Author in previous years
YearPapers
20196
20186
20172
20163
20156
20142

Top Attributes

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Author's top 5 most impactful journals

Blood

5 papers, 222 citations

Nature Communications

3 papers, 63 citations

PLOS Genetics

2 papers, 55 citations

PLOS Biology

2 papers, 35 citations

Journal of Immunology

2 papers, 271 citations