Selected Publications
Ouyang, W., Winsnes, C. F., Hjelmare, M., Cesnik, A. J., Åkesson, L., Xu, H., Sullivan, D. P., Dai, S., Lan, J., Jinmo, P., Galib, S. M, Henkel, C., Hwang, K., Poplavskiy, D., Tunguz, B., Wolfinger, R., Gu, Y., Li, C., Xie, J., Buslov, D., Fironov, S., Kiselev, A., Panchenko, D., Cao, X., Wei, R., Wu, Y., Zhu, X., Tseng, K, Gao, Z., Ju, C., Yi, X., Zheng, H., Kappel, C., Lundberg, E. (2019). Analysis of the Human Protein Atlas Image Classification competition. Nature Methods, 16(12), 1254-1261.
Zhu, X., Yunits, B., Wolfgruber, T., Liu, Y., Huang, Q., Poirion, O., Arisdakessian, C., Zhao, T., Garmire, D., and Garmire, L. GranatumX: A Community Engaging and Flexible Software Environment for Single-Cell Analysis. Genome Medicine, in review.
Poirion, O., Zhu, X., Ching, T. and Garmire, L.X. (2018) Using Single Nucleotide Variations in Single-Cell Rna-Seq to Identify Subpopulations and Genotype-Phenotype Linkage. Nature Communications, 9, 4892.
Arisdakessian, C., Poirion, O., Yunits, B., Zhu, X., & Garmire, L. X. (2019). DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data. Genome biology, 20(1), 1-14.
Ching, T., Zhu, X. and Garmire, L.X. (2018) Cox-Nnet: An Artificial Neural Network Method for Prognosis Prediction of High-Throughput Omics Data. PLoS computational biology, 14, e1006076.
Zhu, X., Wolfgruber, T.K., Tasato, A., Arisdakessian, C., Garmire, D.G. and Garmire, L.X. (2017) Granatum: A Graphical Single-Cell Rna-Seq Analysis Pipeline for Genomics Scientists. Genome medicine, 9, 108.
Ortega, M.A., Poirion, O., Zhu, X., Huang, S., Wolfgruber, T.K., Sebra, R. and Garmire, L.X. (2017) Using Single-Cell Multiple Omics Approaches to Resolve Tumor Heterogeneity. Clinical and translational medicine, 6, 46.
Zhu, X., Ching, T., Pan, X., Weissman, S.M. and Garmire, L. (2017) Detecting Heterogeneity in Single-Cell Rna-Seq Data by Non-Negative Matrix Factorization. PeerJ, 5, e2888.
Yang, J., Tanaka, Y., Seay, M., Li, Z., Jin, J., Garmire, L.X., Zhu, X., Taylor, A., Li, W., Euskirchen, G. and others. (2016) Single Cell Transcriptomics Reveals Unanticipated Features of Early Hematopoietic Precursors. Nucleic acids research, 45, 1281–1296.
Lu, L., McCurdy, S., Huang, S., Zhu, X., Peplowska, K., Tiirikainen, M., Boisvert, W.A. and Garmire, L.X. (2016) Time Series miRNA-mRNA Integrated Analysis Reveals Critical miRNAs and Targets in Macrophage Polarization. Scientific reports, 6, 37446.
Ching, T., Zhu, X., & Garmire, L. X. (2018). Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS computational biology, 14(4), e1006076.
Ching, T., Peplowska, K., Huang, S., Zhu, X., Shen, Y., Molnar, J., Yu, H., Tiirikainen, M., Fogelgren, B., Fan, R. and others. (2016) Pan-Cancer Analyses Reveal Long Intergenic Non-Coding Rnas Relevant to Tumor Diagnosis, Subtyping and Prognosis. EBioMedicine, 7, 62–72.
Wei, R., De Vivo, I., Huang, S., Zhu, X., Risch, H., Moore, J.H., Yu, H. and Garmire, L.X. (2016) Meta-Dimensional Data Integration Identifies Critical Pathways for Susceptibility, Tumorigenesis and Progression of Endometrial Cancer. Oncotarget, 7, 55249.
Menor, M., Ching, T., Zhu, X., Garmire, D. and Garmire, L.X. (2014) MirMark: A Site-Level and Utr-Level Classifier for miRNA Target Prediction. Genome biology, 15, 500.
Last update: June 2020