Last updated: 2021-07-27
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Knit directory: MAESTRO_documentation/
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In this example, we will be analyzing a sci-ATAC-seq dataset from public GEO data with accession SRR1947692 .
Please download the raw data from GEO.
$ mkdir sci-ATAC
$ cd sci-ATAC
$ fastq-dump --split-files --origfmt --defline-seq '@rd.$si:$sg:$sn' SRR1947692Before running MAESTRO, users need to activate the MAESTRO environment.
$ conda activate MAESTROInitialize the MAESTRO scATAC-seq workflow using MAESTRO scATAC-init command. This will install a Snakefile and a config file in this directory. Here we take the 10X PBMC data as an example. Considering MAESTRO provides built-in immune cell markers, it’s recommended to choose the RP-based cell-type annotation strategy. If the data is not immune-related, users can choose to provide their own marker gene list, or choose to annotate cell types through the peak-based method (see the following argument description for more details), or they can directly skip the annotation step by not setting --annotation.
$ MAESTRO scatac-init --input_path sci-ATAC \
--gzip --species GRCh38 --platform sci-ATAC-seq --format fastq --mapping minimap2 \
--deduplication cell-level \
--giggleannotation annotations/giggle.all \
--fasta references/Refdata_scATAC_MAESTRO_GRCh38_1.1.0/GRCh38_genome.fa \
--cores 16 --directory sci-scATAC-seq \
--annotation --method RP-based --signature human.immune.CIBERSORT \
--peak_cutoff 5 --count_cutoff 10 --frip_cutoff 0.05 --cell_cutoff 5
$ cd sci-scATAC-seq/
$ MAESTRO samples-init --assay_type scatac --platform sci-ATAC-seq --data_type fastq --data_dir sci-ATAC/Before running the workflow, please check the config.yaml and see if it is configured correctly. Once configured, users can use snakemake to run the workflow.
$ snakemake -np
$ nohup snakemake -j 16 >run.out &$ ls Result
Analysis Benchmark Log Mapping QC Report
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 knitr_1.33 magrittr_2.0.1 R6_2.5.0
[5] rlang_0.4.11 fansi_0.5.0 stringr_1.4.0 tools_4.1.0
[9] xfun_0.24 utf8_1.2.1 git2r_0.28.0 jquerylib_0.1.4
[13] htmltools_0.5.1.1 ellipsis_0.3.2 rprojroot_2.0.2 yaml_2.2.1
[17] digest_0.6.27 tibble_3.1.2 lifecycle_1.0.0 crayon_1.4.1
[21] later_1.2.0 sass_0.4.0 vctrs_0.3.8 promises_1.2.0.1
[25] fs_1.5.0 glue_1.4.2 evaluate_0.14 rmarkdown_2.9
[29] stringi_1.6.2 bslib_0.2.5.1 compiler_4.1.0 pillar_1.6.1
[33] jsonlite_1.7.2 httpuv_1.6.1 pkgconfig_2.0.3