Atul J Butte

Dr. Atul J Butte

University of California, San Francisco, San Francisco, California, USA
orcid

Research Focus & Metrics

Academic Career Length

28Years (1999-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
14 January 2019 in Nature Immunology
Verified
Journal Article
Closed access
PDF file
DADvir Aran
Dvir Aran
ALAgnieszka P. Looney
Agnieszka P. Looney
LLLeqian Liu
Leqian Liu
EWEsther Wu
Esther Wu
VFValerie Fong
Valerie Fong
MBMallar Bhattacharya
Mallar Bhattacharya

15 November 2017 in Genome Biology
Verified
Journal Article
Open access
PDF file
DADvir Aran
Dvir Aran
ZHZicheng Hu
Zicheng Hu
ABAtul J Butte
Atul J Butte
Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/ .

23 February 2012 in PLOS Computational Biology
Verified
Journal Article
Open access
PDF file
PKPurvesh Khatri
Purvesh Khatri
MSMarina Sirota
Marina Sirota
ABAtul J Butte
Atul J Butte
Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.

4 December 2015 in Nature Communications
Verified
Journal Article
Open access
PDF file
DADvir Aran
Dvir Aran
MSMarina Sirota
Marina Sirota
ABAtul J Butte
Atul J Butte
The tumour microenvironment is the non-cancerous cells present in and around a tumour, including mainly immune cells, but also fibroblasts and cells that comprise supporting blood vessels. These non-cancerous components of the tumour may play an important role in cancer biology. They also have a strong influence on the genomic analysis of tumour samples, and may alter the biological interpretation of results. Here we present a systematic analysis using different measurement modalities of tumour purity in >10,000 samples across 21 cancer types from the Cancer Genome Atlas. Patients are stratified according to clinical features in an attempt to detect clinical differences driven by purity levels. We demonstrate the confounding effect of tumour purity on correlating and clustering tumours with transcriptomics data. Finally, using a differential expression method that accounts for tumour purity, we find an immunotherapy gene signature in several cancer types that is not detected by traditional differential expression analyses.

3 May 2014 in Immunologic Research
Verified
Journal Article
Closed access
PDF file
SBSanchita Bhattacharya
Sanchita Bhattacharya
SASandra Andorf
Sandra Andorf
LGLinda Gomes
Linda Gomes
PDPatrick Dunn
Patrick Dunn
HSHenry Schaefer
Henry Schaefer
ABAtul J Butte
Atul J Butte

27 February 2018 in Scientific Data
Verified
Journal Article
Open access
PDF file
SBSanchita Bhattacharya
Sanchita Bhattacharya
PDPatrick Dunn
Patrick Dunn
CTCristel G. Thomas
Cristel G. Thomas
BSBarry Smith
Barry Smith
HSHenry Schaefer
Henry Schaefer
ABAtul J Butte
Atul J Butte
Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the Shared Data portal (www.immport.org/immport-open), which allows research data to be repurposed to accelerate the translation of new insights into discoveries.