Chapter 8 About CytoTree

Bug Reports

If there is any error in installing or librarying the CytoTree package, please contact us via e-mail .

Current version

Version: 1.0.3

Release Date: 2020-11-01


This work was supported by the National Key Research and Development Plan of China Grants [No. 2018YFA0107802], the National Natural Science Foundation of China (NSFC) General Program [No. 81570122, 81670094, 81770205, 81830007], the National Key Research and Development Program [No. 2016YFC0902800], and the Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support [No. 20161303], Shanghai Collaborative Innovation Program on Regenerative Medicine and Stem Cell Research [No. 2019CXJQ01], Mayo Clinic Center for Individualized Medicine.

We thank the Center for High Performance Computing of Shanghai Jiao Tong University for providing computing support.

We thank Prof. Guangchuang Yu at Southern Medical University for his kind comments and advice of CytoTree.

Session Information

# Show session information
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## other attached packages:
## [1] stringr_1.4.0   pheatmap_1.0.12 ggthemes_4.2.0  ggplot2_3.3.2   LSD_4.1-0       flowCore_2.2.0  CytoTree_1.0.3  igraph_1.2.6   
## loaded via a namespace (and not attached):
##   [1] reticulate_1.18             RUnit_0.4.32                tidyselect_1.1.0            RSQLite_2.2.1              
##   [5] AnnotationDbi_1.52.0        grid_4.0.2                  ranger_0.12.1               BiocParallel_1.24.0        
##   [9] Rtsne_0.15                  scatterpie_0.1.5            aws.signature_0.6.0         munsell_0.5.0              
##  [13] destiny_3.4.0               codetools_0.2-16            umap_0.2.7.0                withr_2.3.0                
##  [17] colorspace_1.4-1            Biobase_2.50.0              highr_0.8                   knitr_1.30                 
##  [21] rstudioapi_0.11             stats4_4.0.2                SingleCellExperiment_1.12.0 robustbase_0.93-6          
##  [25] vcd_1.4-8                   VIM_6.0.0                   TTR_0.24.2                  labeling_0.4.2             
##  [29] MatrixGenerics_1.2.0        GenomeInfoDbData_1.2.4      polyclip_1.10-0             bit64_4.0.5                
##  [33] farver_2.0.3                flowWorkspace_4.2.0         vctrs_0.3.4                 generics_0.1.0             
##  [37] xfun_0.19                   R6_2.5.0                    GenomeInfoDb_1.26.0         RcppEigen_0.        
##  [41] locfit_1.5-9.4              bitops_1.0-6                DelayedArray_0.16.0         scales_1.1.1               
##  [45] nnet_7.3-14                 gtable_0.3.0                sva_3.38.0                  RProtoBufLib_2.2.0         
##  [49] rlang_0.4.8                 genefilter_1.72.0           scatterplot3d_0.3-41        flowUtils_1.54.0           
##  [53] splines_4.0.2               hexbin_1.28.1               BiocManager_1.30.10         yaml_2.2.1                 
##  [57] abind_1.4-5                 RBGL_1.66.0                 tools_4.0.2                 bookdown_0.21              
##  [61] ellipsis_0.3.1              RColorBrewer_1.1-2          proxy_0.4-24                BiocGenerics_0.36.0        
##  [65] Rcpp_1.0.5                  plyr_1.8.6                  base64enc_0.1-3             zlibbioc_1.36.0            
##  [69] purrr_0.3.4                 RCurl_1.98-1.2              FlowSOM_1.22.0              openssl_1.4.3              
##  [73] S4Vectors_0.28.0            zoo_1.8-8                   SummarizedExperiment_1.20.0 haven_2.3.1                
##  [77] cluster_2.1.0               magrittr_1.5                ncdfFlow_2.36.0             data.table_1.13.2          
##  [81] RSpectra_0.16-0             openxlsx_4.2.3              gmodels_2.18.1              lmtest_0.9-38              
##  [85] RANN_2.6.1                  pcaMethods_1.82.0           matrixStats_0.57.0          hms_0.5.3                  
##  [89] evaluate_0.14               xtable_1.8-4                smoother_1.1                XML_3.99-0.5               
##  [93] rio_0.5.16                  jpeg_0.1-8.1                mclust_5.4.6                readxl_1.3.1               
##  [97] IRanges_2.24.0              gridExtra_2.3               ggcyto_1.18.0               compiler_4.0.2             
## [101] tibble_3.0.4                crayon_1.3.4                htmltools_0.5.0             mgcv_1.8-33                
## [105] corpcor_1.6.9               tidyr_1.1.2                 RcppParallel_5.0.2          aws.s3_0.3.21              
## [109] DBI_1.1.0                   tweenr_1.0.1                MASS_7.3-53                 boot_1.3-25                
## [113] Matrix_1.2-18               car_3.0-10                  gdata_2.18.0                parallel_4.0.2             
## [117] GenomicRanges_1.42.0        forcats_0.5.0               pkgconfig_2.0.3             rvcheck_0.1.8              
## [121] prettydoc_0.4.0             foreign_0.8-80              laeken_0.5.1                sp_1.4-4                   
## [125] xml2_1.3.2                  annotate_1.68.0             XVector_0.30.0              digest_0.6.27              
## [129] tsne_0.1-3                  ConsensusClusterPlus_1.54.0 graph_1.68.0                rmarkdown_2.5              
## [133] cellranger_1.1.0            edgeR_3.32.0                curl_4.3                    gtools_3.8.2               
## [137] ggplot.multistats_1.0.0     lifecycle_0.2.0             nlme_3.1-150                jsonlite_1.7.1             
## [141] carData_3.0-4               BiocNeighbors_1.8.0         askpass_1.1                 limma_3.46.0               
## [145] pillar_1.4.6                lattice_0.20-41             httr_1.4.2                  DEoptimR_1.0-8             
## [149] survival_3.2-7              glue_1.4.2                  xts_0.12.1                  zip_2.1.1                  
## [153] png_0.1-7                   bit_4.0.4                   Rgraphviz_2.34.0            ggforce_0.3.2              
## [157] class_7.3-17                stringi_1.5.3               blob_1.2.1                  RcppHNSW_0.3.0             
## [161] CytoML_2.2.1                latticeExtra_0.6-29         memoise_1.1.0               dplyr_1.0.2                
## [165] cytolib_2.2.0               knn.covertree_1.0           irlba_2.3.3                 e1071_1.7-4


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About The Author

The author Yuting Dai is a Ph.D. student at Shanghai Jiao Tong University. In 2016, she graduated from Shanghai Jiao Tong University and got a Bachelor Degree in Bioinformatics. Then she continued to study for a Ph.D. at Shanghai Institute of Hematology, Shanghai Jiao Tong University. The main research directions of her are bioinformatic tools or workflows development on multi-omics data in cancer, and the details as follow:

  1. Sequencing data analysis in cancer, especially leukemia, e.g. whole-genome sequencing, whole-exome sequencing, RNA sequencing, single-cell RNA sequencing, ChIP-seq.

  2. Flow and mass cytometry data analysis and software development.

  3. Visualization of multidimensional data.

She has participated in several projects and here is the publication (# represents the co-first author, and * represents the corresponding author):

[1] Lu Jiang#, Xue-Ping Li#, Yu-Ting Dai#, Bing Chen, Xiang-Qin Weng, Shu-Min Xiong, Min Zhang, Jin-Yan Huang*, Zhu Chen*, Sai-Juan Chen*. Multidimensional study of the heterogeneity of leukemia cells in t(8;21) acute myelogenous leukemia identifies the subtype with poor outcome. PNAS, 2020, 117(33):20117-20126.

[2] Jie Xiong#, Bo-Wen Cui#, Nan Wang#, Yu-Ting Dai#, Hao Zhang#, Chao-Fu Wang#, Hui-Juan Zhong, Shu Cheng, Bin-Shen Ou-Yang, Yu Hu, Xi Zhang, Bin Xu, Wen-Bin Qian, Rong Tao, Feng Yan, Jian-Da Hu, Ming Hou, Xue-Jun Ma, Xin Wang, Yuan-Hua Liu, Zun-Min Zhu, Xiao-Bin Huang, Li Liu, Chong-Yang Wu, Li Huang, Yun-Feng Shen, Rui-Bin Huang, Jing-Yan Xu, Chun Wang, De-Pei Wu, Li Yu, Jian-Feng Li, Peng-Peng Xu, Li Wang, Jin-Yan Huang*, Sai-Juan Chen*, Wei-Li Zhao*. Genomic and Transcriptomic Characterization of Natural Killer T Cell Lymphoma. Cancer Cell, 2020, 37(3):403-419.

[3] Yi Zhou#, Xingli Zhu#, Yuting Dai#, Shumin Xiong, Chuijin Wei, Pei Yu, Yuewen Tang, Liang Wu, Jianfeng Li, Dan Liu, Yanlin Wang, Zhu Chen, Sai-Juan Chen*, Jinyan Huang*, Lin Cheng*. Chemical cocktail induces hematopoietic reprogramming and expands hematopoietic stem/progenitor cells. Advanced Science, 2019. 7(1):1901785

[4] Jian-Feng Li#, Yu-Ting Dai#, Henrik Lilljebjörn#, Shu-Hong Shen, Bo-Wen Cui, Ling Bai, Yuan-Fang Liu, Mao-Xiang Qian, Yasuo Kubota, Hitoshi Kiyoi, Itaru Matsumura, Yasushi Miyazaki, Linda Olsson, Ah Moy Tan, Hany Ariffin, Jing Chen, Junko Takita, Takahiko Yasuda, Hiroyuki Mano, Bertil Johansson, Jun J. Yang, Allen Yeoh Eng Juh, Fumihiko Hayakawa, Zhu Chen*, Ching-Hon Pui*, Thoas Fioretos*, Sai-Juan Chen*, Jin-Yan Huang*. The transcriptional landscape of B-cell precursor acute lymphoblastic leukemia based on an international study of 1,223 cases. PNAS, 2018. 115(50):E11711-E11720.