# <table border="0" cellspacing="0" cellpadding="0"> <tbody> <tr> <td rowspan="4"> <img src="cellsnake-logo-blue-small.png"> </td> <td> [](https://hub.docker.com/r/sinanugur/cellsnake) [](https://cellsnake.readthedocs.io/en/latest/?badge=latest) </td> </tr> <tr> <td> [](https://badge.fury.io/py/cellsnake) </td> </tr> <tr> <td> [](https://anaconda.org/bioconda/cellsnake) </td> </tr> <tr> <td> [](https://anaconda.org/bioconda/cellsnake) [](http://bioconda.github.io/recipes/cellsnake/README.html) </td> </tr></tbody></table>
A command line tool for easy and scalable single cell RNA sequencing analysis
Our main Snakemake workflow is here: https://github.com/sinanugur/scrna-workflow
Installation
------------
Please use Bioconda repo for installation. Mamba installation is also recommended. To create a clean environment and install __cellsnake__, type:
```
conda install mamba -c conda-forge
mamba create -n cellsnake -c bioconda -c conda-forge cellsnake
```
Apple Silicon computers have to force Osx64, you can install like this.
```
conda install mamba -c conda-forge
CONDA_SUBDIR=osx-64 mamba create -n cellsnake -c bioconda -c conda-forge cellsnake
```
Check if the installation works by calling the main script.
```
conda activate cellsnake
cellsnake --help
```
Then install the R packages by typing:
```
cellsnake --install-packages
```
You should see this message if all the packages are available:
```
cellsnake --install-packages
[1] "All packages were installed...OK"
```
__Cellsnake__ auto install most of the packages when necessary or during the creation of environment but it is good to check if they are installable.
You can then move the environment to an offline location as well if required. We recommend our Docker image and it is a better solution for installation problems. Podman also works fine with our Docker image.
See our Docker repo: https://hub.docker.com/r/sinanugur/cellsnake
Quick start examples
-------------------
Run `cellsnake` in a clean directory and `cellsnake` will create the required directories while running. You may download publicly available fetal brain dataset to test your `cellsnake` installation. The link is here.
https://www.dropbox.com/sh/1qn2odtnci0vvtr/AADPxHH-GR4h-OuQG0TLQyxWa?dl=0
After downloading the dataset, just point the data folder which contains the two datasets, this will trigger a standard cellsnake workflow:
```
cellsnake standard data
```
After the pipeline finishes, browse the output files. You can also integrate these two samples which makes sense.
```
cellsnake integrate data
```
That is it. Lets work on the integrated object from now on, we already processed the samples separately.
Let's do a minimal run, this will also generate a __clustree__ plot as well which can be used to investigate the optimal resolution.
```
cellsnake integrated minimal analyses_integrated/seurat/integrated.rds
```
You want a resolution of 0.1 after checking clustree plot, then you can trigger a run with this resolution.
```
cellsnake integrated standard analyses_integrated/seurat/integrated.rds --resolution 0.1
```
It is also possible to use automatic resolution selection, however this might be very slow in large datasets.
```
cellsnake integrated standard analyses_integrated/seurat/integrated.rds --resolution auto
```
See our documentation for detailed explanations and to read full features: https://cellsnake.readthedocs.io/
Options and Arguments
---------------------
```
Usage:
cellsnake <command> <INPUT> [options] [--unlock|--remove] [--dry]
cellsnake integrated <command> <INPUT> [options] [--unlock|--remove] [--dry]
cellsnake --generate-template
cellsnake --install-packages
cellsnake (-h | --help)
cellsnake --version
commands:
minimal Run cellsnake with minimal workflow.
standard Run cellsnake with standard workflow.
advanced Run cellsnake with advanced workflow.
clustree Run cellsnake with clustree workflow.
integrate Run cellsnake to integrate samples under analyses folder.
This option expects you have already finished processing multiple samples.
main arguments:
INPUT Input directory or a file to process (if a directory given, batch mode is ON).
--configfile <text> Config file name in YAML format, for example, "config.yaml". No default but can be created with --generate-template.
--metadata <text> Metadata file name in CSV, TSV or Excel format, for example, "metadata.csv", header required, first column sample name. No default but can be created with --generate-template.
--metadata_column <text> Metadata column for differential expression analysis [default: condition].
other arguments:
--gene <gene or filename> Create publication ready plots for a gene or a list of genes from a text file.
main options:
--percent_mt <double> Maximum mitochondrial gene percentage cutoff,
for example, 5 or 10, write "auto" for auto detection [default: 10].
--resolution <double> Resolution for cluster detection, write "auto" for auto detection [default: 0.8].
other options:
--doublet_filter <bool> [default: True] #this may fail on some samples
--percent_rp <double> [default: 0] #Ribosomal genes minimum percentage (0-100), default no filtering
--min_cells <integer> [default: 3] #seurat default, recommended
--min_features <integer> [default: 200] #seurat default, recommended, nFeature_RNA
--max_features <integer> [default: Inf] #seurat default, nFeature_RNA, 5000 can be a good cutoff
--min_molecules <integer> [default: 0] #seurat default, nCount_RNA, min_features usually handles this so keep it 0
--max_molecules <integer> [default: Inf] #seurat default, nCount_RNA, to filter potential doublets, doublet filtering is already default, so keep this Inf
--highly_variable_features <integer> [default: 2000] #seurat defaults, recommended
--variable_selection_method <text> [default: vst] #seurat defaults, recommended
--normalization_method <text> [default: LogNormalize]
--scale_factor <integer> [default: 10000]
--logfc_threshold <double> [default: 0.25]
--test_use <text> [default: wilcox]
--mapping <text> [default: org.Hs.eg.db] #you may install others from Bioconductor, this is for human
--organism <text> [default: hsa] #alternatives https://www.genome.jp/kegg/catalog/org_list.html
--species <text> [default: human] for cellchat, #only human or mouse is accepted
plotting parameters:
--min_percentage_to_plot <double> [default: 2] #only show clusters more than % of cells on the legend
--show_labels <bool> [default: True] #
--marker_plots_per_cluster_n <integer> [default: 20] #plot summary marker plots for top markers
--umap_markers_plot <bool> [default: True]
--tsne_markers_plot <bool> [default: False]
annotation options:
--singler_ref <text> [default: BlueprintEncodeData] # https://bioconductor.org/packages/release/data/experiment/vignettes/celldex/inst/doc/userguide.html#1_Overview
--celltypist_model <text> [default: Immune_All_Low.pkl] #refer to Celltypist for another model
microbiome options:
--kraken_db_folder <text> No default, you need to provide a folder with kraken2 database
--taxa <text> [default: genus] # available options "domain", "kingdom", "phylum", "class", "order", "family", "genus", "species"
--microbiome_min_cells <integer> [default: 1]
--microbiome_min_features <integer> [default: 3]
--confidence <double> [default: 0.05] #see kraken2 manual
--min_hit_groups <integer> [default: 4] #see kraken2 manual
integration options:
--dims <integer> [default: 30] #refer to Seurat for more details
--reduction <text> [default: cca] #refer to Seurat for more details
others:
--generate-template Generate config file template and metadata template in the current directory.
--install-packages Install, reinstall or check required R packages.
-j <integer>, --jobs <integer> Total CPUs. [default: 2]
-u, --unlock Rescue stalled jobs (Try this if the previous job ended prematurely or currently failing).
-r, --remove Delete all output files (this won't affect input files).
-d, --dry Dry run, nothing will be generated.
-h, --help Show this screen.
--version Show version.
```
Output
------
The `cellsnake` main executable will generate two main folders: analyses and results. If an integrated dataset available, analyses_integrated and results_integrated will be created.
The main directory structure will look like this, __resolution__ and __percent_mt__ can be visible on directory names. These are the only parameters that will generate a separate folders.
```
results/integrated/percent_mt~auto/resolution~0.8/ #for regular samples
results_integrated/integrated/percent_mt~auto/resolution~0.8/ #for integrated samples
```
Raw data
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"description": "# <table border=\"0\" cellspacing=\"0\" cellpadding=\"0\"> <tbody> <tr> <td rowspan=\"4\"> <img src=\"cellsnake-logo-blue-small.png\"> </td> <td> [](https://hub.docker.com/r/sinanugur/cellsnake) [](https://cellsnake.readthedocs.io/en/latest/?badge=latest) </td> </tr> <tr> <td> [](https://badge.fury.io/py/cellsnake) </td> </tr> <tr> <td> [](https://anaconda.org/bioconda/cellsnake) </td> </tr> <tr> <td> [](https://anaconda.org/bioconda/cellsnake) [](http://bioconda.github.io/recipes/cellsnake/README.html) </td> </tr></tbody></table> \n\n\nA command line tool for easy and scalable single cell RNA sequencing analysis \n\nOur main Snakemake workflow is here: https://github.com/sinanugur/scrna-workflow \n\nInstallation\n------------\n\nPlease use Bioconda repo for installation. Mamba installation is also recommended. To create a clean environment and install __cellsnake__, type:\n```\nconda install mamba -c conda-forge\n\nmamba create -n cellsnake -c bioconda -c conda-forge cellsnake\n\n\n```\n\nApple Silicon computers have to force Osx64, you can install like this.\n\n```\nconda install mamba -c conda-forge\n\nCONDA_SUBDIR=osx-64 mamba create -n cellsnake -c bioconda -c conda-forge cellsnake\n\n```\n\n\nCheck if the installation works by calling the main script. \n```\nconda activate cellsnake\ncellsnake --help\n```\n\nThen install the R packages by typing: \n```\ncellsnake --install-packages\n```\n\n\nYou should see this message if all the packages are available:\n```\ncellsnake --install-packages\n[1] \"All packages were installed...OK\"\n```\n\n__Cellsnake__ auto install most of the packages when necessary or during the creation of environment but it is good to check if they are installable. \nYou can then move the environment to an offline location as well if required. We recommend our Docker image and it is a better solution for installation problems. Podman also works fine with our Docker image.\n\nSee our Docker repo: https://hub.docker.com/r/sinanugur/cellsnake\n\nQuick start examples\n-------------------\nRun `cellsnake` in a clean directory and `cellsnake` will create the required directories while running. You may download publicly available fetal brain dataset to test your `cellsnake` installation. The link is here.\n\nhttps://www.dropbox.com/sh/1qn2odtnci0vvtr/AADPxHH-GR4h-OuQG0TLQyxWa?dl=0\n\nAfter downloading the dataset, just point the data folder which contains the two datasets, this will trigger a standard cellsnake workflow:\n```\ncellsnake standard data\n```\n\nAfter the pipeline finishes, browse the output files. You can also integrate these two samples which makes sense.\n```\ncellsnake integrate data\n```\n\nThat is it. Lets work on the integrated object from now on, we already processed the samples separately. \n\nLet's do a minimal run, this will also generate a __clustree__ plot as well which can be used to investigate the optimal resolution.\n```\ncellsnake integrated minimal analyses_integrated/seurat/integrated.rds\n```\n\nYou want a resolution of 0.1 after checking clustree plot, then you can trigger a run with this resolution.\n```\ncellsnake integrated standard analyses_integrated/seurat/integrated.rds --resolution 0.1\n```\n\nIt is also possible to use automatic resolution selection, however this might be very slow in large datasets.\n```\ncellsnake integrated standard analyses_integrated/seurat/integrated.rds --resolution auto\n```\n\nSee our documentation for detailed explanations and to read full features: https://cellsnake.readthedocs.io/\n\nOptions and Arguments\n---------------------\n```\nUsage:\n cellsnake <command> <INPUT> [options] [--unlock|--remove] [--dry]\n cellsnake integrated <command> <INPUT> [options] [--unlock|--remove] [--dry]\n cellsnake --generate-template\n cellsnake --install-packages\n cellsnake (-h | --help)\n cellsnake --version\n\ncommands:\n minimal Run cellsnake with minimal workflow.\n standard Run cellsnake with standard workflow.\n advanced Run cellsnake with advanced workflow.\n clustree Run cellsnake with clustree workflow.\n integrate Run cellsnake to integrate samples under analyses folder.\n This option expects you have already finished processing multiple samples.\n\nmain arguments:\n INPUT Input directory or a file to process (if a directory given, batch mode is ON).\n --configfile <text> Config file name in YAML format, for example, \"config.yaml\". No default but can be created with --generate-template.\n --metadata <text> Metadata file name in CSV, TSV or Excel format, for example, \"metadata.csv\", header required, first column sample name. No default but can be created with --generate-template.\n --metadata_column <text> Metadata column for differential expression analysis [default: condition].\n\nother arguments:\n --gene <gene or filename> Create publication ready plots for a gene or a list of genes from a text file.\n\nmain options:\n --percent_mt <double> Maximum mitochondrial gene percentage cutoff,\n for example, 5 or 10, write \"auto\" for auto detection [default: 10].\n --resolution <double> Resolution for cluster detection, write \"auto\" for auto detection [default: 0.8].\n\nother options:\n --doublet_filter <bool> [default: True] #this may fail on some samples\n --percent_rp <double> [default: 0] #Ribosomal genes minimum percentage (0-100), default no filtering\n --min_cells <integer> [default: 3] #seurat default, recommended\n --min_features <integer> [default: 200] #seurat default, recommended, nFeature_RNA\n --max_features <integer> [default: Inf] #seurat default, nFeature_RNA, 5000 can be a good cutoff\n --min_molecules <integer> [default: 0] #seurat default, nCount_RNA, min_features usually handles this so keep it 0\n --max_molecules <integer> [default: Inf] #seurat default, nCount_RNA, to filter potential doublets, doublet filtering is already default, so keep this Inf\n --highly_variable_features <integer> [default: 2000] #seurat defaults, recommended\n --variable_selection_method <text> [default: vst] #seurat defaults, recommended\n\n --normalization_method <text> [default: LogNormalize]\n --scale_factor <integer> [default: 10000]\n --logfc_threshold <double> [default: 0.25]\n --test_use <text> [default: wilcox]\n\n\n --mapping <text> [default: org.Hs.eg.db] #you may install others from Bioconductor, this is for human\n --organism <text> [default: hsa] #alternatives https://www.genome.jp/kegg/catalog/org_list.html\n --species <text> [default: human] for cellchat, #only human or mouse is accepted\n\nplotting parameters:\n --min_percentage_to_plot <double> [default: 2] #only show clusters more than % of cells on the legend\n --show_labels <bool> [default: True] #\n --marker_plots_per_cluster_n <integer> [default: 20] #plot summary marker plots for top markers\n --umap_markers_plot <bool> [default: True]\n --tsne_markers_plot <bool> [default: False]\n\nannotation options:\n --singler_ref <text> [default: BlueprintEncodeData] # https://bioconductor.org/packages/release/data/experiment/vignettes/celldex/inst/doc/userguide.html#1_Overview\n --celltypist_model <text> [default: Immune_All_Low.pkl] #refer to Celltypist for another model\n\nmicrobiome options:\n --kraken_db_folder <text> No default, you need to provide a folder with kraken2 database\n --taxa <text> [default: genus] # available options \"domain\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"\n --microbiome_min_cells <integer> [default: 1]\n --microbiome_min_features <integer> [default: 3]\n --confidence <double> [default: 0.05] #see kraken2 manual\n --min_hit_groups <integer> [default: 4] #see kraken2 manual\n\nintegration options:\n --dims <integer> [default: 30] #refer to Seurat for more details\n --reduction <text> [default: cca] #refer to Seurat for more details\n\nothers:\n --generate-template Generate config file template and metadata template in the current directory.\n --install-packages Install, reinstall or check required R packages.\n -j <integer>, --jobs <integer> Total CPUs. 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