Terry Speed
Affiliations: | Statistics | University of California, Berkeley, Berkeley, CA, United States |
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"Terry Speed"Children
Sign in to add trainee| Bin Yu | grad student | 1990 | UC Berkeley (E-Tree) |
| Gregory J. Hather | grad student | 2008 | UC Berkeley |
| Soyeon Ahn | grad student | 2009 | UC Berkeley |
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Publications
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| Lin Y, Cao Y, Kim HJ, et al. (2020) scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Molecular Systems Biology. 16: e9389 |
| Lin Y, Ghazanfar S, Strbenac D, et al. (2019) Evaluating stably expressed genes in single cells. Gigascience. 8 |
| Savas P, Virassamy B, Ye C, et al. (2019) Abstract PD5-03: Characterization of high TIL breast cancers reveals a prognostic and functionally distinct tissue-resident memory subpopulation Cancer Research. 79 |
| Zhang Y, Feng ZP, Naselli G, et al. (2016) Corrigendum to 'MicroRNAs in CD4(+) T cell subsets are markers of disease risk and T cell dysfunction in individuals at risk for type 1 diabetes' [J. Autoimmun. 68C (2016) 52-61]. Journal of Autoimmunity |
| Xin A, Masson F, Liao Y, et al. (2016) A molecular threshold for effector CD8(+) T cell differentiation controlled by transcription factors Blimp-1 and T-bet. Nature Immunology |
| Zhang Y, Feng ZP, Naselli G, et al. (2016) MicroRNAs in CD4(+) T cell subsets are markers of disease risk and T cell dysfunction in individuals at risk for type 1 diabetes. Journal of Autoimmunity |
| Quaglieri A, Speed T, Majewski I. (2016) Finding optimal coverage F1000research. 5 |
| Jacob L, Gagnon-Bartsch JA, Speed TP. (2016) Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed Biostatistics. 17: 16-28 |
| Huang KT, Mikeska T, Li J, et al. (2015) Assessment of DNA methylation profiling and copy number variation as indications of clonal relationship in ipsilateral and contralateral breast cancers to distinguish recurrent breast cancer from a second primary tumour. Bmc Cancer. 15: 669 |
| Peixoto L, Risso D, Poplawski SG, et al. (2015) How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets. Nucleic Acids Research. 43: 7664-74 |