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A bioinformatic pipeline for the analysis of spatial transcriptomic data

Project description

Spatial transcriptomics sequencing

Structure of the pipeline

This repository collects all scripts and tools used for analyzing the sequencing side of the spatial transcriptomics datasets. The following steps are currently performed:

Demultiplex the data

This assumes that the sample sheet has been provided and that the raw data has been copied to the basecalls folder. The tool bcl2fastq is used to demultiplex the data.

Rename the fastq files

It is important to rename the .fastq files so that the namings are meaningful.

Reverse the fastq files

Read 1 needs to be reversed to match the barcodes of the optical side.

Run FastQC on the fastq files

Run it on all files. Do QC.

Run the sequencing analysis pipeline

After this the sequences are analyzed. It needs to be provided the (i) species to map onto and (ii) the filename of the sample.

Produce the QC sheet of the sequencing data

After everything is finished, a python script is being run to produce the QC sheet for the sample. There's the qc_sequencing_parameters.yaml file which contains metadata for the experiment/sample and currently needs to be created automatically. Could be automized, with taking info partially from the sample sheet.


The pipeline is implemented in snakemake. All metadata of the experiments (experiment_name, flowcell_id, species, etc) should be put in a config.yaml file. An example config.yaml file is in the root of this repo.

To run the snakemake script, the snakemake python library is required (installed with pip or conda). The script requires at least 6 threads to run, this is due to pipeing several commands one after the other to descrease runtime.

Example run:

snakemake --snakefile path_to_snakefile --configfile path_to_configfile.

This will create the output in the directory in which the command is run. Note, that all samplesheet-flowcell_id paris should be ideally in one configfile somewhere.

Produced directory structure

The following directory structure will be produced by the snakemake file

    |-- demultiplex_data                            # demultiplexed data folders, one per each samplesheet
    |   |-- 200110_STS_017_4-7-8STS_018_1           # directory names are identical to the samplesheet names
    |   |   |-- Stats
    |   |   |-- sts_017
    |   |   |-- sts_018
    |   |   |-- Undetermined_S0_R1_001.fastq.gz
    |   |   `-- Undetermined_S0_R2_001.fastq.gz
    |   `-- 20191206_spatseq_smples3-4
    |       |-- indicator.log
    |       |-- Reports
    |       |-- Stats
    |       |-- sts_0xxx
    |       |-- Undetermined_S0_R1_001.fastq.gz
    |       `-- Undetermined_S0_R2_001.fastq.gz
    |-- sts_017                                     # root output directory, one per project
    |   |-- data
    |   |   |-- sts_017_4                           # directory containing results of running the pipeline. one per sample 
    |   |   |-- sts_017_7
    |   |   `-- sts_017_8
    |   `-- reads                                   # reads directory, one per sample
    |       |-- fastqc
    |       |-- raw
    |       `-- reversed
    |-- sts_018
    |   |-- data
    |   |   `-- sts_018_1
    |   `-- reads
    |       |-- fastqc
    |       |-- raw
    |       `-- reversed
    `-- sts_0xxx
        |-- data
        |   |-- sts_01
        |   `-- sts_02
        `-- reads
            |-- fastqc
            |-- raw
            `-- reversed

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