development-mouse

Analysis pipeline for the mouse developing nervous system project.

Publication: Braun et al: Comprehensive cell atlas of the first-trimester developing human brain

Download data:

• Complete dataset, 31053 genes x 292495 cells, anndata format: developing_mouse_nervous_system.h5ad
• One column per cluster, 31053 genes x 798 clusters, loom format: dev_all.agg.loom

Installation

1. Install luigi, cytograph and loompy

2. Clone the repository to your computer:

git clone https://github.com/linnarsson-lab/development-mouse.git

3. Install in development mode using pip:

cd development-mouse
pip install -e .

Setting up your environment

In order to run the pipeline, you will need to set up your samples folder:

• A folder containing the raw samples (loom files named like 10X23_1.loom)
• Inside it, a sub-folder named classified and containing pre-classified clusters (loom files named like L0_Cortex1.loom)
• Also inside it, a sub-folder named metadata and containing a file named metadata.xlsx with sample metadata

For example, you could have something like this:

loom_samples/
    10X23_1.loom
    10X23_2.loom
    10X53_1.loom
    ...
    classified/
        L0_Cortex1.loom
        L0_Hippocampus.loom
        ...
        classifier.pickle
    metadata/
        metadata.xlsx

Note: On monod, a samples folder is available at /data/proj/chromium/. Please use this directly instead of making a copy.

Note: Instead of sample files directly in the top-level folder, sample files can also be in subfolders named after the sample ID, or in a sub-folder of such a subfolder, named velocyto. Thus, these are all equivalent:

10X43_2.loom
10X43_2/10X43_2.loom
10X43_2/velocyto/10X43_2.loom

If it exists, the sample in velocyto will take precedence.

Furthermore, you need a file in the current directory (typically, development-mouse) named pooling_specification.tab, which gives a list of all the samples and their pool names. This file has four columns: SampleID (like 10X04_1), Pool (like Hippocampus), TimepointPool (always none), QC (OK or FAILED), and Project (Adolescent).

Samples with QC == FAILED will be ignored for all analyses.

Running the pipeline

1. Create a folder to hold the output of the build, e.g. mkdir /data/proj/development/build_20171107.

2. Run luigi. For example:

luigi --workers 15 --local-scheduler --module development_mouse TASKNAME --paths-samples /data/proj/chromium/loom_samples/ --paths-build /data/proj/development/build_20171107

The command line arguments are as follows:

Argument	Effect
--workers	Number of parallel process to use
--local-scheduler	run the pipeline directly, not in client/server mode.
--module development_mouse TASKNAME	run the TASKNAME task in the Python module development_mouse.
--paths-samples /data/proj/chromium/loom_samples/	a configuration of the paths object, setting the sample path
--paths-build /data/proj/development/build_20171107	a configuration of the paths object, setting the build path

Tasks Descriptions

Level1 tasks

[TODO] Add image of the tasks hierarchy

Level1Analysis

It is the main task to run. It triggers ExportL1 that in turn will trigger all level 1 set.

Velocity related

below an example how to run velocity on a single sample

Preliminary Level1 Analysis

If we want to make make sure that Level1 Analysis has been appropriatelly run, run:

luigi --local-scheduler --module development_mouse ExportL1 --tissue Forebrain_E9-11 --paths-samples /data/proj/chromium/loom_samples/ --paths-build /data/proj/development/build_20171115

EstimateVelocity

nohup luigi --local-scheduler --module development_mouse EstimateVelocity --tissue Forebrain_E9-11 --paths-samples /data/proj/chromium/ --paths-build /data/proj/development/build_20171115 > ~/luigi_EstimateVelocity_EstimateVelocity.log &