├── Data Integration └── Readme.md ├── LICENSE ├── README.md ├── Spatial Transcriptomics └── Readme.md ├── install_R_packages.R ├── scATAC-seq └── Readme.md ├── scMultiomics └── Readme.md ├── scRNA └── Readme.md ├── server.R └── ui.R /Data Integration/Readme.md: -------------------------------------------------------------------------------- 1 | **Module 2 : Data Integration Module** 2 | 3 | ezSingleCell uses 4 different methods (namely Seurat, Harmony, scVI and fastMNN) to perform data integration. It accepts data in two different formats namely: 4 | 5 | 1. Gene counts (in text format), or 6 | 7 | 2. Cell Ranger output 8 | 9 | For gene counts, the test gene expression dataset comprising of 500 cells from each batch was downloaded from: 10 | 11 | 1. 10X website (https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k), or 12 | 13 | 2. Google drive link (https://drive.google.com/file/d/1SxhS1q2SgIDSwnu-4_9x4vTpriwI4Z6T/view?usp=share_link) 14 | 15 | The metadata can also be found at the google drive link (https://drive.google.com/file/d/1Cp9bJ0_DFqh7ePr7LruDeBj3P8ojpQ-1/view?usp=share_link) 16 | 17 | For Cellranger output, the data can be found at the google drive as below: 18 | 19 | 1. Batch 1 data (https://drive.google.com/file/d/11yc2MvzQAlSvk_BCSr9LqQjZ1lXuEwRX/view?usp=share_link) 20 | 21 | 2. Batch 2 data (https://drive.google.com/file/d/17ShgTTMx8k8IpENqKDOiPH6Fx-ns4JVX/view?usp=share_link) 22 | 23 | ![image](https://github.com/JinmiaoChenLab/ezSingleCell2/assets/8286779/74d27ad1-0d8a-4a46-9090-2843cc95f841) 24 | 25 | 26 | 27 | 28 | 29 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ezSingleCell 2 | 3 | To run ezsinglecell, follow the steps below: 4 | 5 | a) Open R (preferably R >= 4.0) 6 | 7 | b) Type the commands in your R console : 8 | 9 | library(shiny) 10 | 11 | runGist("https://gist.github.com/raman91/5171381ae540bc7f58f43f79af2c7d6d") 12 | 13 | Please note: The stand-alone version only supports analysis of user's own data. For running example files, please use the web server at 14 | 15 | ![Picture1](https://github.com/JinmiaoChenLab/ezSingleCell2/assets/8286779/874d1a54-4ba4-4e24-ba9e-ba5e9c98bed5) 16 | -------------------------------------------------------------------------------- /Spatial Transcriptomics/Readme.md: -------------------------------------------------------------------------------- 1 | **Module 3: Spatial Transcriptomics module** 2 | 3 | The Spatial Transcriptomics module offers analysis for 10X Visium and Xenium data. 4 | 5 | For 10X Visium data, ezSingleCell offers data analysis such as spatial clustering and deconvolution using 2 methods namely: 6 | 7 | 1. Seurat (https://satijalab.org/seurat/articles/spatial_vignette.html) 8 | 9 | 2. In-house developed algorithm named GraphST avaialable at https://github.com/JinmiaoChenLab/GraphST [Long Y et al, Nature Communications (2023)] 10 | 11 | The acceptable input format is in the form of 10X SpaceRanger output. The test dataset used is 'Human Breast Cancer' data comprising of 3798 spots downloaded from https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0. 12 | 13 | ezSingleCell also offers analysis support for sub-cellular resolution data such as data acquired using the **Xenium** platform. Users can perform clustering analysis and interactively visualize the expression patterns at sub-cellular level. In addition, users can zoom into a certain section to have a closer look at the cellular composition and inter-cellular interactions. Users can also view the expression profile of each gene on the tissue slice. 14 | 15 | The test dataset for Xenium can be downloaded using the link : 16 | 17 | https://cf.10xgenomics.com/samples/xenium/1.0.2/Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP/Xenium_V1_FF_Mouse_Brain_Coronal_Subset_CTX_HP_outs.zip 18 | 19 | ![image](https://github.com/JinmiaoChenLab/ezSingleCell2/assets/8286779/3878d12b-c808-4b0f-8c24-e46d36b39cba) 20 | 21 | -------------------------------------------------------------------------------- /install_R_packages.R: -------------------------------------------------------------------------------- 1 | options(repos = BiocManager::repositories()) 2 | if (!require("devtools")) install.packages("devtools", repos="http://cran.us.r-project.org") 3 | if (!require("shiny")) install.packages("shiny", repos="http://cran.us.r-project.org") 4 | if (!require("shinythemes")) install.packages("shinythemes", repos="http://cran.us.r-project.org") 5 | if (!require("tidyverse")) install.packages("tidyverse", repos="http://cran.us.r-project.org") 6 | if (!require("stringr")) install.packages("stringr", repos="http://cran.us.r-project.org") 7 | if (!require("servr")) install.packages("servr", repos="http://cran.us.r-project.org") 8 | if (!require("ggplot2")) install.packages("ggplot2", repos="http://cran.us.r-project.org") 9 | if (!require("pheatmap")) install.packages("pheatmap", repos="http://cran.us.r-project.org") 10 | if (!require("heatmaply")) install.packages("heatmaply", repos="http://cran.us.r-project.org") 11 | if (!require("plotly")) install.packages("plotly", repos="http://cran.us.r-project.org") 12 | if (!require("shinyjs")) install.packages("shinyjs", repos="http://cran.us.r-project.org") 13 | if (!require("shinyBS")) install.packages("shinyBS", repos="http://cran.us.r-project.org") 14 | if (!require("msigdbr")) install.packages("msigdbr", repos="http://cran.us.r-project.org") 15 | if (!require("fontawesome")) install.packages("fontawesome", repos="http://cran.us.r-project.org") 16 | if (!require("shinycssloaders")) install.packages("shinycssloaders", repos="http://cran.us.r-project.org") 17 | if (!require("DT")) install.packages("DT", repos="http://cran.us.r-project.org") 18 | if (!require("e1071")) install.packages("e1071", repos="http://cran.us.r-project.org") 19 | if (!require("dplyr")) install.packages("dplyr", repos="http://cran.us.r-project.org") 20 | if (!require("pbmcapply")) install.packages("pbmcapply", repos="http://cran.us.r-project.org") 21 | if (!require("reshape2")) install.packages("reshape2", repos="http://cran.us.r-project.org") 22 | if (!require("shinydashboard")) install.packages("shinydashboard", repos="http://cran.us.r-project.org") 23 | if (!require("shinyalert")) install.packages("shinyalert", repos="http://cran.us.r-project.org") 24 | if (!require("shinyFiles")) install.packages("shinyFiles", repos="http://cran.us.r-project.org") 25 | if (!require("igraph")) install.packages("igraph", repos="http://cran.us.r-project.org") 26 | if (!require("cluster")) install.packages("cluster", repos="http://cran.us.r-project.org") 27 | if (!require("RANN")) install.packages("RANN", repos="http://cran.us.r-project.org") 28 | if (!require("shinyWidgets")) install.packages("shinyWidgets", repos="http://cran.us.r-project.org") 29 | if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") 30 | if (!require("scales")) install.packages("scales", repos="http://cran.us.r-project.org") 31 | if (!require("dtwclust")) install.packages("dtwclust", repos="http://cran.us.r-project.org") 32 | if (!require("anndata")) install.packages("anndata", repos="http://cran.us.r-project.org") 33 | if (!require("reticulate")) install.packages("reticulate", repos="http://cran.us.r-project.org") 34 | if (!require("tibble")) install.packages("tibble", repos="http://cran.us.r-project.org") 35 | if (!require("clustree")) install.packages("clustree", repos="http://cran.us.r-project.org") 36 | if (!require("magrittr")) install.packages("magrittr", repos="http://cran.us.r-project.org") 37 | if (!require("cowplot")) install.packages("cowplot", repos="http://cran.us.r-project.org") 38 | if (!require("rhandsontable")) install.packages("rhandsontable", repos="http://cran.us.r-project.org") 39 | if (!require("sctransform")) install.packages("sctransform", repos="http://cran.us.r-project.org") 40 | if (!require("data.table")) install.packages("data.table", repos="http://cran.us.r-project.org") 41 | if (!require("purrr")) install.packages("purrr", repos="http://cran.us.r-project.org") 42 | if (!require("scater")) BiocManager::install("scater", update = FALSE) 43 | if (!require("fgsea")) BiocManager::install("fgsea", update = FALSE) 44 | if (!require("MOFA2")) BiocManager::install("MOFA2", update = FALSE) 45 | if (!require("EnhancedVolcano")) BiocManager::install("EnhancedVolcano", update = FALSE) 46 | if (!require("liana")) remotes::install_github('saezlab/liana') 47 | if (!require("rGREAT")) install_github("jokergoo/rGREAT") 48 | if (!require("biovizBase")) BiocManager::install("biovizBase", update = FALSE) 49 | if (!require("GenomeInfoDb")) BiocManager::install("GenomeInfoDb", update = FALSE) 50 | if (!require("BSgenome.Hsapiens.UCSC.hg19")) BiocManager::install("BSgenome.Hsapiens.UCSC.hg19", update = FALSE) 51 | #if (!require("cytomapper")) BiocManager::install("cytomapper", update = FALSE) 52 | #if (!require("FlowSOM")) BiocManager::install("FlowSOM", update = FALSE) 53 | #if (!require("cytofkit2")) devtools::install_github("JinmiaoChenLab/cytofkit2", dependencies=TRUE) 54 | if (!require("lisi")) devtools::install_github("immunogenomics/lisi", dependencies=TRUE) 55 | if (!require("ktplots")) devtools::install_github('zktuong/ktplots', dependencies = TRUE) 56 | if (!require("EnsDb.Hsapiens.v86")) BiocManager::install("EnsDb.Hsapiens.v86", update = FALSE) 57 | #if (!require("flowCore")) BiocManager::install("flowCore", update = FALSE) 58 | if (!require("harmony")) BiocManager::install("harmony") 59 | #if (!require("rliger")) install.packages('rliger') 60 | if (!require("cowplot")) install.packages('cowplot') 61 | #if (!require("Rphenograph")) devtools::install_github("JinmiaoChenLab/Rphenograph", upgrade=TRUE) 62 | #if (!require("CellChat")) devtools::install_github("sqjin/CellChat", upgrade=TRUE) 63 | if (!require("sceasy")) devtools::install_github("cellgeni/sceasy", upgrade=TRUE) 64 | if (!require("leidenbase")) devtools::install_github('cole-trapnell-lab/leidenbase') 65 | #if (!require("monocle3")) devtools::install_github('cole-trapnell-lab/monocle3') 66 | #if (!require("SPOTlight")) devtools::install_github("https://github.com/MarcElosua/SPOTlight", upgrade=TRUE) 67 | if (!require("FastIntegration")) devtools::install_github("git@github.com:JinmiaoChenLab/FastIntegrate.git", upgrade=TRUE) 68 | if (!require("hdf5r")) install.packages("hdf5r", repos="http://cran.us.r-project.org") 69 | if (!require("Seurat")) install.packages("Seurat", repos="http://cran.us.r-project.org") 70 | if (!require("bslib")) install.packages("bslib", repos="http://cran.us.r-project.org") 71 | if (!require("shinyWidgets")) install.packages("shinyWidgets") 72 | if (!require("SeuratWrappers")) remotes::install_github('satijalab/seurat-wrappers') 73 | if (!require("Signac")) install.packages("Signac", repos="http://cran.us.r-project.org") 74 | -------------------------------------------------------------------------------- /scATAC-seq/Readme.md: -------------------------------------------------------------------------------- 1 | **Module5: scATACseq module** 2 | 3 | The single cell ATAC-seq module accepts input in 10X CellRanger-atac format that comprises of : 4 | 5 | 1. Peak/Cell matrix (filtered_peak_bc_matrix.h5): This is analogous to the gene expression count matrix used to analyze single-cell RNA-seq. However, instead of genes, each row of the matrix represents a region of the genome (a peak), that is predicted to represent a region of open chromatin. Each value in the matrix represents the number of Tn5 integration sites for each single barcode (i.e. a cell) that map within each peak. 6 | 7 | 2. Metadata 8 | 9 | 3. Fragments file: This represents a full list of all unique fragments across all single cells. This file contains all fragments associated with each single cell, as opposed to only fragments that map to peaks. 10 | 11 | ezSingleCell employs the Signac package (https://stuartlab.org/signac/) and includes variety of analysis tasks such as Cell Clustering, Target Enrichment analysis (Link Peak to Genes), Gene Set Enrichment Analysis of scATAC-seq data using tools such as rGREAT and fgsea, Differential peak analysis. In addition, scATAC-seq supports interaction with other modules in ezSingleCell such as scRNA-seq or Data Integration module to annotate the cell types in scATAC-seq data. 12 | 13 | ![image](https://github.com/JinmiaoChenLab/ezSingleCell2/assets/8286779/d292f2e4-e004-46c0-aa52-eeba52025f56) 14 | -------------------------------------------------------------------------------- /scMultiomics/Readme.md: -------------------------------------------------------------------------------- 1 | **Module 4: scMultiomics module** 2 | 3 | The single cell Multiomics module can perform CITE-seq (RNA + Protein) and 10X Multiome (RNA + ATAC) analysis. It accepts input in 10X cellranger format. 4 | 5 | For CITE-seq data analysis, ezSingleCell supports data generated from TotalSeq A,B and C while for 10X Multiome analysis, the accepted formats is the output from cellranger-arc. 6 | 7 | ezSingleCell accepts input in the form of counts table of both RNA and Protein modalities (in .h5 format) in case of CITE-Seq data and counts table of both RNA and ATAC modalities as well as fragments file in case of 10X Multiome data. 8 | 9 | The data for scMultiomics can be downloaded from: 10 | 11 | a) CITE-seq : https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_protein_v3 12 | 13 | b) 10X Multiome: https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets/1.0.0/pbmc_unsorted_3k? 14 | 15 | Currently, we employ 2 methods to perform single cell Multiomics analysis: 16 | 17 | a) Seurat (https://satijalab.org/seurat/articles/multimodal_vignette), and 18 | 19 | b) MOFA2 (https://biofam.github.io/MOFA2/) 20 | 21 | **For CITE-Seq:** 22 | 23 | ![image](https://github.com/JinmiaoChenLab/ezSingleCell2/assets/8286779/1ce3fac0-b0b1-47b3-970d-f962d43c00e7) 24 | -------------------------------------------------------------------------------- /scRNA/Readme.md: -------------------------------------------------------------------------------- 1 | **Module 1: scRNA-seq module** 2 | 3 | The single cell RNA-seq module accepts 3 data formats namely: 4 | 5 | a) Gene counts (in text format), 6 | 7 | b) 10X cellranger output, and 8 | 9 | c) Seurat object 10 | 11 | **For gene counts**, the test dataset of 2,700 Human peripheral blood mononuclear cells (from Seurat guided clustering tutorial dataset) was downloaded either from: 12 | 13 | 1. 10X website (https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz) or 14 | 15 | 2. Google drive link (https://drive.google.com/file/d/1zoCaPKI3mV_BOJM4RPpiO3zW949Y2azq/view?usp=share_link) 16 | 17 | **For 10X cellranger output**, the test dataset can be downloaded from google drive link (https://drive.google.com/file/d/156o3WvxJi6XZkoiOgqTScOA4QlU_cjFZ/view?usp=share_link) 18 | 19 | **For seurat object**, the test dataset can be downlaoded from google drive link (https://drive.google.com/file/d/1F5p_0S8LwpS_F5yRJSxzQ5eTU7F23o8r/view?usp=share_link) 20 | 21 | ![scRNA-seq](https://user-images.githubusercontent.com/8286779/229020079-c6a0a199-1dec-4f80-b1c3-7ff08305035f.png) 22 | 23 | --------------------------------------------------------------------------------