Source code

Workflow tutorials

Please check the following GitHub link for full tutorials, including:

  1. Calculating overlapping DEGs from the same cell type across datasets
  2. Running ssREAD backend analysis workflow locally

https://github.com/OSU-BMBL/ssread-protocol

More methods details about the pipeline can be found in What is ssREAD’s scRNAseq pipeline? or What is ssREAD’s spatial transcriptomics pipeline?

scRNA-seq & snRNAseq and spatial datasets

IMPORTANT: If you are unable to download, please try to right click the ‘download’ link and select ‘save link as’.

#Handling .qsave Files:The datasets may include files with the .qsave extension. This is an object serialization format specific to the qs R package, optimized for performance when loading the Seurat object to replacing the h5seurat format. To load these files into your R environment, the qs package needs to be installed.

You can install and load the object in R with the following command:

#install.packages("qs")
data_obj <- qs::qread("path_to_your_qsave_file.qsave")

Note 1: The h5ad files are currently unavailable. We are actively working on making them available for download soon.
Note 2: Please be aware that access to the AD Knowledge Portal is subject to Controlled Use restrictions. For compliance with these restrictions, we have removed the counts’ matrix and log-normalized matrix from the Seurat object. The downloadable object now only contains scaled data and dimension reductions. If you require raw counts, please refer to the AD Knowledge Portal. For more information, please check the terms and conditions in AD Knowledge Portal Data Use Certificate (DUC). If you would like to use the following dataset, please include the following statement in your study:

  • AD001 (syn18485175): The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). Samples for this study were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152, the Illinois Department of Public Health, and the Translational Genomics Research Institute.
  • AD014 (syn21438358): Study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161 (ROS), R01AG15819 (ROSMAP; genomics and RNAseq), R01AG17917 (MAP), R01AG30146, R01AG36042 (5hC methylation, ATACseq), RC2AG036547 (H3K9Ac), R01AG36836 (RNAseq), R01AG48015 (monocyte RNAseq) RF1AG57473 (single nucleus RNAseq), U01AG32984 (genomic and whole exome sequencing), U01AG46152 (ROSMAP AMP-AD, targeted proteomics), U01AG46161(TMT proteomics), U01AG61356 (whole genome sequencing, targeted proteomics, ROSMAP AMP-AD), the Illinois Department of Public Health (ROSMAP), and the Translational Genomics Research Institute (genomic). Additional phenotypic data can be requested at www.radc.rush.edu.
  • AD025 (syn23763409): The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). This study was funded through Alzheimer’s Association (2018-AARF-589154), National Institutes of Health (RF1AG055104, RF1AG051496), the Diana Davis Spencer Foundation and the following contributors: Hongtian Stanley Yang, Kristen D. Onos, Kwangbom Choi, Kelly J. Keezer, Daniel A. Skelly, Gregory W. Carter, and Gareth R. Howell.
  • AD035 (syn26223298): The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org/). Study data were generated from postmortem brain tissue obtained from the University of Washington BioRepository and Integrated Neuropathology (BRaIN) laboratory and Precision Neuropathology Core, which is supported by the NIH grants for the UW Alzheimer’s Disease Research Center (P50AG005136 and P30AG066509) and the Adult Changes in Thought Study (U01AG006781 and U19AG066567). This study is supported by NIA grant U19AG060909.
  • AD041 (syn22079621): Although this is a non-AD Portal study with no apparent Acknowledgement Statements listed, please check the original data source for citation: https://www.synapse.org/Synapse:syn22079621
  • AD047 (syn16780177): “Study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by NIA grants P30AG10161 (ROS), R01AG15819 (ROSMAP; genomics and RNAseq), R01AG17917 (MAP), R01AG30146, R01AG36042 (5hC methylation, ATACseq), RC2AG036547 (H3K9Ac), R01AG36836 (RNAseq), R01AG48015 (monocyte RNAseq) RF1AG57473 (single nucleus RNAseq), U01AG32984 (genomic and whole exome sequencing), U01AG46152 (ROSMAP AMP-AD, targeted proteomics), U01AG46161(TMT proteomics), U01AG61356 (whole genome sequencing, targeted proteomics, ROSMAP AMP-AD), the Illinois Department of Public Health (ROSMAP), and the Translational Genomics Research Institute (genomic). Additional phenotypic data can be requested at www.radc.rush.edu.
    [For snRNAseq Data:]
    Study data were generated from postmortem brain tissue provided by the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) cohort at Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. This work was funded by NIH grants U01AG061356 (De Jager/Bennett), RF1AG057473 (De Jager/Bennett), and U01AG046152 (De Jager/Bennett) as part of the AMP-AD consortium, as well as NIH grants R01AG066831 (Menon) and U01AG072572 (De Jager/St George-Hyslop).”
  • AD048 and AD051 (syn52293417): The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org/). Study data were generated from postmortem brain tissue provided by the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) cohort at Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. This work was supported in part by the Cure Alzheimer’s Fund, NIH grants AG058002, AG062377, NS110453, NS115064, AG062335, AG074003, NS127187, MH119509, HG008155 (M.K.), RF1AG062377, RF1 AG054321, RO1 AG054012 (L.-H.T.) and the NIH training grant GM087237 (to C.A.B.). ROSMAP is supported by P30AG10161, P30AG72975, R01AG15819, R01AG17917. U01AG46152, U01AG61356.
AD00101 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00101.qsave
AD00102 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00102.qsave
AD00103 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00103.qsave
AD00104 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00104.qsave
AD00105 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00105.qsave
AD00106 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00106.qsave
AD00107 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00107.qsave
AD00108 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00108.qsave
AD00109 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00109.qsave
AD00110 AD001 According to note 2, we have removed the counts and normalized matrices for the download dataset AD00110.qsave
AD00201 AD002 682.45 MB AD00201.qsave
AD00202 AD002 17.63 MB AD00202.qsave
AD00203 AD002 67.42 MB AD00203.qsave
AD00204 AD002 49.72 MB AD00204.qsave
AD00205 AD002 480.23 MB AD00205.qsave
AD00206 AD002 767.75 MB AD00206.qsave
AD00301 AD003 1137.86 MB AD00301.qsave
AD00302 AD003 190.1 MB AD00302.qsave
AD00303 AD003 33.62 MB AD00303.qsave
AD00304 AD003 1268.28 MB AD00304.qsave
AD00305 AD003 910.3 MB AD00305.qsave
AD00306 AD003 1147.32 MB AD00306.qsave
AD00307 AD003 139.73 MB AD00307.qsave
AD00308 AD003 98.47 MB AD00308.qsave
AD00309 AD003 70.09 MB AD00309.qsave
AD00401 AD004 378.31 MB AD00401.qsave
AD00402 AD004 249.24 MB AD00402.qsave
AD00403 AD004 333.73 MB AD00403.qsave
AD00404 AD004 117.46 MB AD00404.qsave
AD00405 AD004 53.22 MB AD00405.qsave
AD00501 AD005 8.32 MB AD00501.qsave
AD00502 AD005 21.92 MB AD00502.qsave
AD00601 AD006 14.91 MB AD00601.qsave
AD00602 AD006 15 MB AD00602.qsave
AD00603 AD006 16.31 MB AD00603.qsave
AD00604 AD006 29.14 MB AD00604.qsave
AD00702 AD007 35.77 MB AD00702.qsave
AD00703 AD007 65.47 MB AD00703.qsave
AD00704 AD007 48.29 MB AD00704.qsave
AD00705 AD007 61.93 MB AD00705.qsave
AD00708 AD007 90.13 MB AD00708.qsave
AD00709 AD007 93.26 MB AD00709.qsave
AD00710 AD007 32.61 MB AD00710.qsave
AD00711 AD007 43.12 MB AD00711.qsave
AD00712 AD007 45.98 MB AD00712.qsave
AD00713 AD007 49.3 MB AD00713.qsave
AD00714 AD007 47.41 MB AD00714.qsave
AD00715 AD007 59.74 MB AD00715.qsave
AD00716 AD007 54.74 MB AD00716.qsave
AD00717 AD007 46.64 MB AD00717.qsave
ssREAD is developed by BMBL, it is free and open to all users and there is no login requirement. | 2024