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Usage instructions:

1. scRNA-seq pipeline

A. Run the complete scRNA-seq pipeline automatically

library(HemaScopeR)
scRNASeq_10x_pipeline(
                     # input and output
                     input.data.dirs = c('/input/path/dataset1',
                                         '/input/path/dataset2'),
                     project.names = c('project1',
                                       'project2'), 
                     output.dir = '/output/path/',
                     pythonPath = '/python/path/',
                     databasePath = '/database/path/',
                     # quality control and preprocessing
                     gene.column = 2,
                     min.cells = 10,
                     min.feature = 200,
                     mt.pattern = '^MT-',
                     nFeature_RNA.limit = 200,
                     percent.mt.limit = 20,
                     scale.factor = 10000,
                     nfeatures = 3000,
                     ndims = 50,
                     vars.to.regress = NULL,
                     PCs = 1:35,
                     resolution = 0.4,
                     n.neighbors = 50,
                     # remove doublets
                     doublet.percentage = 0.04,
                     doublerFinderwraper.PCs = 1:20,
                     doublerFinderwraper.pN = 0.25,
                     doublerFinderwraper.pK = 0.1,
                     # phateR
                     phate.knn = 50,
                     phate.npca = 20,
                     phate.t = 10,
                     phate.ndim = 2,
                     min.pct = 0.25,
                     logfc.threshold = 0.25,
                     # visualization
                     ViolinPlot.cellTypeOrders = as.character(1:22),
                     ViolinPlot.cellTypeColors = NULL,
                     Org = 'hsa',
                     loom.files.path = c( '/input/velocyto/dataset1.loom',
                                          '/input/velocyto/dataset2.loom'),
                     # cell cycle
                     cellcycleCutoff = NULL,
                     # cell chat
                     sorting = FALSE,
                     ncores = 10,
                     # Verbose = FALSE,
                     # activeEachStep
                     Whether_load_previous_results = FALSE,
                     Step1_Input_Data = TRUE,
                     Step1_Input_Data.type = 'cellranger-count',
                     Step2_Quality_Control = TRUE,
                     Step2_Quality_Control.RemoveBatches = FALSE,
                     Step2_Quality_Control.RemoveDoublets = TRUE,
                     Step3_Clustering = TRUE,
                     Step4_Identify_Cell_Types = TRUE,
                     Step4_Use_Which_Labels = 'clustering',
                     Step4_Cluster_Labels = NULL,
                     Step4_Changed_Labels = NULL,
                     Step4_run_sc_CNV = FALSE,
                     Step5_Visualization = TRUE,
                     Step6_Find_DEGs = TRUE,
                     Step7_Assign_Cell_Cycle = TRUE,
                     Step8_Calculate_Heterogeneity = TRUE,
                     Step9_Violin_Plot_for_Marker_Genes = TRUE,
                     Step10_Calculate_Lineage_Scores = TRUE,
                     Step11_GSVA = TRUE,
                     Step11_GSVA.identify.cellType.features=TRUE,
                     Step11_GSVA.identify.diff.features=FALSE,
                     Step11_GSVA.comparison.design=NULL,
                     Step12_Construct_Trajectories = TRUE,
                     Step12_Construct_Trajectories.clusters = c('cluster1', 'cluster2', 'cluster3'), 
                     Step12_Construct_Trajectories.monocle = TRUE,
                     Step12_Construct_Trajectories.slingshot = TRUE,
                     Step12_Construct_Trajectories.scVelo = TRUE,
                     Step13_TF_Analysis = TRUE,
                     Step14_Cell_Cell_Interaction = TRUE,
                     Step15_Generate_the_Report = TRUE
        )

B. Run the scRNA-seq pipeline step by step

Follow the documents of each function in HemaScopeR.

2. st-seq pipeline

A. Run the complete st-seq pipeline automatically

library(HemaScopeR)
st_10x_visium_pipeline(
    input.data.dir = '/Path/to/data',
    output.dir = '/Path/to/save',
    sampleName = 'Hema',
    Step2_QC = T,
    Step3_Clustering = T,
    Step4_Find_DEGs = T,
    Step5_SVFs = T,
    Step6_Interaction = T,
    Step7_CNV = T,
    Step8_Deconvolution = T,
    Step9_Cellcycle = T,
    Step10_Nich = T,
    
    # settings
    verbose = FALSE,
    species = 'mouse', # human or mosue
    genReport = TRUE
)

B. Run the st-seq pipeline step by step

Follow the documents of each function in HemaScopeR.

3. Graphical user interface

A. Start the shiny GUI

library(HemaScopeR)
shinyApp(ui = ui, 
         server = server, 
         options = list(host = your_host, port = your_port))

B. Follow the instructions on the GUI

This is the homepage of the GUI:

homepage

Click the button 'Start scRNA-seq pipeline' to enter the following page:

sc1

After data loading, click the button 'Step 2. Quanlity Control' to enter the following page:

sc2

Proceed with the remaining steps by following the instructions on the GUI to execute the pipeline step by step.

4. Docker

A. Pull the 'hemascoper' docker image

docker pull l1hj/hemascoper

B. Start a docker container

docker run -it --security-opt seccomp=unconfined hemascoper /bin/bash

Jupyter notebook is also available, which can be started as follow (e.g. set the port as 8888):

docker run -i -t -p 8888:8888 continuumio/miniconda3 /bin/bash -c "\
    mkdir -p /opt/notebooks && \
    jupyter notebook \
    --notebook-dir=/opt/notebooks --ip='*' --port=8888 \
    --no-browser --allow-root"

5. Demo datasets

The demo datasets are available in our Cloud Drive via this link https://cloud.tsinghua.edu.cn/d/3d363e32665249409571/.

A. scRNA-seq demo

We use the SRR7881414 dataset from demo datasets to demonstrate the scRNA-seq pipeline in HemaScopeR.

library(HemaScopeR)
scRNASeq_10x_pipeline(
                     # input and output
                     input.data.dirs = c('./testData/SRR7881414_filtered_feature_bc_matrix'),
                     project.names = c('SRR7881414'), 
                     output.dir = './testData/SRR7881414_result',
                     pythonPath = '/python/path',
                     databasePath = '/database/path/',
                     # quality control and preprocessing
                     gene.column = 2,
                     min.cells = 10,
                     min.feature = 200,
                     mt.pattern = '^MT-',
                     nFeature_RNA.limit = 200,
                     percent.mt.limit = 20,
                     scale.factor = 10000,
                     nfeatures = 3000,
                     ndims = 50,
                     vars.to.regress = NULL,
                     PCs = 1:35,
                     resolution = 0.4,
                     n.neighbors = 50,
                     # remove doublets
                     doublet.percentage = 0.04,
                     doublerFinderwraper.PCs = 1:20,
                     doublerFinderwraper.pN = 0.25,
                     doublerFinderwraper.pK = 0.1,
                     # phateR
                     phate.knn = 50,
                     phate.npca = 20,
                     phate.t = 10,
                     phate.ndim = 2,
                     min.pct = 0.25,
                     logfc.threshold = 0.25,
                     # visualization
                     ViolinPlot.cellTypeOrders = NULL,
                     ViolinPlot.cellTypeColors = NULL,
                     Org = 'hsa',
                     loom.files.path = c('./testData/SRR7881414.loom'),
                     # cell cycle
                     cellcycleCutoff = NULL,
                     # cell chat
                     sorting = FALSE,
                     ncores = 10,
                     # Verbose = FALSE,
                     # activeEachStep
                     Whether_load_previous_results = FALSE,
                     Step1_Input_Data = TRUE,
                     Step1_Input_Data.type = 'cellranger-count',
                     Step2_Quality_Control = TRUE,
                     Step2_Quality_Control.RemoveBatches = FALSE,
                     Step2_Quality_Control.RemoveDoublets = TRUE,
                     Step3_Clustering = TRUE,
                     Step4_Identify_Cell_Types = TRUE,
                     Step4_Use_Which_Labels = 'clustering',
                     Step4_Cluster_Labels = NULL,
                     Step4_Changed_Labels = NULL,
                     Step4_run_sc_CNV = TRUE,
                     Step5_Visualization = TRUE,
                     Step6_Find_DEGs = TRUE,
                     Step7_Assign_Cell_Cycle = TRUE,
                     Step8_Calculate_Heterogeneity = TRUE,
                     Step9_Violin_Plot_for_Marker_Genes = TRUE,
                     Step10_Calculate_Lineage_Scores = TRUE,
                     Step11_GSVA = TRUE,
                     Step11_GSVA.identify.cellType.features=TRUE,
                     Step11_GSVA.identify.diff.features=FALSE,
                     Step11_GSVA.comparison.design=NULL,
                     Step12_Construct_Trajectories = TRUE,
                     Step12_Construct_Trajectories.clusters = NULL, 
                     Step12_Construct_Trajectories.monocle = TRUE,
                     Step12_Construct_Trajectories.slingshot = TRUE,
                     Step12_Construct_Trajectories.scVelo = TRUE,
                     Step13_TF_Analysis = TRUE,
                     Step14_Cell_Cell_Interaction = TRUE,
                     Step15_Generate_the_Report = TRUE
        )

B. st-seq demo

We use one sample from the GSE230207 dataset in the demo datasets to showcase the st-seq pipeline in HemaScopeR. The other st-seq datasets within the demo can be run using the same code. And you just need to adjust specific paths and parameters.

library(HemaScopeR)
st_10x_visium_pipeline(
    input.data.dir = './testData/GSE230207_RAW/hot',
    output.dir = './testData/GSE230207_hot_result',
    sampleName = 'GSE230207_hot',
    Step2_QC = TRUE,
    Step3_Clustering = TRUE,
    Step4_Find_DEGs = TRUE,
    Step5_SVFs = TRUE,
    Step6_Interaction = TRUE,
    Step7_CNV = TRUE,
    Step8_Deconvolution = TRUE,
    Step9_Cellcycle = TRUE,
    Step10_Nich = TRUE,
    
    # settings
    verbose = FALSE,
    species = 'human', # human or mosue
    genReport = TRUE
)

C. Examples of HTML-formatted analysis reports

The HTML-formatted demo analysis reports can be accessed via these links https://github.com/ZhenyiWangTHU/HemaScopeR/blob/main/sc_demo_report.rar and https://github.com/ZhenyiWangTHU/HemaScopeR/blob/main/st_demo_report.rar, at the same time, we are continually updating and optimizing the format and content of these reports.