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IMPC Statistical Pipeline

This is the main R source code repository for IMPC statistical pipeline.

The IMPC statistical pipeline requires 4 steps to complete:

  1. Pre-processing the data and run the analysis.
  2. Run the annotation and transfer pipeline.
  3. Run the report generating pipeline.
  4. Run the extraction of risky genes pipeline.
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flowchart TB
    subgraph container[ ]
    style container fill:#ffffff
    direction TB
        subgraph stats_pipeline ["Step 1. Analysis ±2 weeks"]
            style stats_pipeline fill:#E6E6FA
            style main_ageing fill:#E6FAE6,stroke:#6FC56D
            style main_ageing_phase3 fill:#E6FAE6,stroke:#6FC56D
            direction LR
            subgraph phase1 ["Phase I. Preparing parquet files ±36 min"]
                direction TB
                inputStatsPipeline[StatsPipeline]-->|DRversion=20.2| step1[far:fa-file Step1MakePar2RdataJobs.R]
                step1 --> |Generate file with a list of jobs| step2_parquet2rdata{{jobs_step2_Parquet2Rdata.bch}}
                step2_parquet2rdata --> step2[far:fa-file Step2Parquet2Rdata.R] 
                step2_parquet2rdata --> |Run all jobs in .bch and \nwait until it's finished| step3[far:fa-file Step3MergeRdataFilesJobs.R]
                step2 --> step3
                step3 --> |Generate file with a list of jobs| step4_merge_rdatas{{jobs_step4_MergeRdatas.bch}}
                step4_merge_rdatas --> step4[far:fa-file Step4MergingRdataFiles.R] 
                step4_merge_rdatas --> |Run all jobs in .bch and \nwait until it's finished| compress_cleaning[Compress log files and clean up]
                step4 --> compress_cleaning
                compress_cleaning --> |zip -rm| parquet_to_rdata_jobs{{far:fa-folder Parquet2RdataJobs.zip}}
                compress_cleaning --> |zip -rm| parquet_to_rdata_logs{{far:fa-folder Parquet2RdataLogs.zip}}
                compress_cleaning --> |rm -rf| procedure_scatter_data{{far:fa-folder ProcedureScatterRdata}}
            end
            subgraph phase2 ["Phase II. Reprocessing the data ±5 days 14 hours"]
                direction TB
                job_creator[jobCreator from\nsideFunctions.R] --> |Generate file with jobs| data_generation_job_list{{DataGenerationJobList.bch}}
                data_generation_job_list --> input_data_generator[far:fa-file InputDataGenerator.R]
                data_generation_job_list --> |Run all jobs in .bch and \nwait until it's finished| compress_logs[Compress logs]
                input_data_generator --> generate_data[GenerateData from\nInputDataGenerator.R] 
                generate_data --> |GenerateData run\nmainAgeing function| main_ageing[mainAgeing from\nDRrequiredAgeing]
                main_ageing --> |BatchProducer = TRUE| compress_logs
                compress_logs --> remove_logs[Remove logs]
            end
            subgraph phase3 ["Phase III. Initialising the statistical analysis... ±6 days 22 hours"]
                direction TB
                update_impress[updateImpress from\nsideFunctions.R] --> windowing_pipeline{Is\nwindowingPipeline\nTrue?}
                windowing_pipeline --> |"True — default"| window_true[Copy function_windowed.R\n and rename to function.R]
                windowing_pipeline --> |Else| window_else[Copy function.R]
                window_true --> replace_word[ReplaceWordInFile from\nsideFunctions.R]
                window_else --> replace_word
                replace_word --> |ReplaceWordInFile use function.R| main_ageing_phase3[mainAgeing from\nDRrequiredAgeing]
                main_ageing_phase3 --> |BatchProducer = FALSE\nWait until completion| package_backup[packageBackup from\nsideFunctions.R]
            end
        end
        subgraph further_steps[ ]
            direction LR
            annotation["Step 2.Annotation\nand transfer pipeline\n±1 Day"] --> report["Step 3. Report\ngenerating pipeline\n±½ day"]
            report --> risky["Step 4. Extraction\nof risky genes pipeline\n±30 minutes"]
        end
        input[/ETL Parquet Files\] --> stats_pipeline --> further_steps
        mp_chooser[/mp_chooser\] --> stats_pipeline
        phase1 --> phase2
        phase2 --> phase3
    end

    classDef title font-size:30px
    class stats_pipeline title
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How to Run IMPC Statistical and Annotation Pipeline

These instructions are tailored for Release 21.0. To know more about input files for statistical pipeline refer to the Observations Output Schema. In the current dataset, some fields that should be arrays are presented as comma-separated lists.

Step 1. Data Preprocessing and Analysis

0. Switch to the mi_stats virtual user

become mi_stats

1. Set necessary variables

export VERSION="21.0"
export REMOTE="mpi2"
export BRANCH="master"
export KOMP_PATH="<absolute_path_to_directory>"

2. Download script run_pipeline.sh that run both statistical and annotation pipeline on SLURM and add execute permission to a file

wget https://raw.githubusercontent.com/${REMOTE}/impc_stats_pipeline/${BRANCH}/run_pipeline.sh
chmod +x run_pipeline.sh

3. Execute run_pipeline.sh script

./run_pipeline.sh ${VERSION} ${REMOTE} ${BRANCH} ${KOMP_PATH} ${KOMP_PATH}/data-releases/latest-input/dr${VERSION}/output/flatten_observations_parquet/ ${KOMP_PATH}/data-releases/latest-input/dr${VERSION}/output/mp_chooser_json/

After executing the script make sure that you wrote down job ID for both statistical and annotation pipeline. Note: Be cautious, the location of the input files may vary. To execute run_pipeline.sh we need to pass six parameters:

  1. Version of the data release.
  2. Remote name.
  3. Branch name.
  4. Path to the initial directory.
  5. Path to the input parquet files.
  6. Path to the MP chooser file.

Monitor progress using the following commands

  • Use squeue to check list of running jobs.
  • Use jobinfo -v <job_id> to check the job status.
  • Review the log files:
less ${KOMP_PATH}/impc_statistical_pipeline/IMPC_DRs/stats_pipeline_logs/stats_pipeline_${VERSION}.log
less ${KOMP_PATH}/impc_statistical_pipeline/IMPC_DRs/stats_pipeline_logs/stats_pipeline_${VERSION}.err

Outdated Steps

Step 2. Run the Extraction of Risky Genes Pipeline

This process generates a list of risky genes to check manually.

  1. Allocate a machine on codon cluster: bsub –M 8000 –Is /bin/bash
  2. Open an R session: R
  3. Run the following command in the console: DRrequiredAgeing:::extractRiskyGenesFromDRs('path to the gzip report from the NEW release','path to the new report on the OLD release')
  • You may need to transfer the old reports to a path to make it accessible for the pipeline.
  • The output of this process is a file RiskyGenesToCheck_[DATE].txt in the current directory with each line a gene that should be manually checked.

FAQ

  • When will the pipeline be completed?
    When there are no jobs running under the mi_stats user and all jobs are successfully completed.

  • Do you expect any errors from the stats pipeline?
    Yes, having a few errors is normal. If you observe more than a few errors, you may want to run the GapFilling pipeline. Refer to the Step_1.2_RunGapFillingPipeline.mp4 video. Make sure to log in to Confluence first.

Statistical Pipeline FAQ

  • How can you determine on step 1 if the pipeline is still running?
    The simplest method is to execute the squeue command. During the first 4 days of running the pipeline, there should be less than 20 jobs running. Otherwise, there should be 5000+ jobs running on the codon cluster.

  • How to determine if step 1 is finished?
    When there are no jobs running in the cluster, it indicates that the pipeline has been completed.

  • How to retrieve logs from the pipeline step 1?
    • The short answer: The simplest method is to check the <stats pipeline directory>/SP/logs directory after the pipeline completes.
    • Long answer (applicable if the pipeline fails during execution): Log files are distributed for individual jobs and are not located in a single directory. To consolidate the log files into a destination directory, you can use the following commands in bash."
cd <stats pipeline directory>/SP
find ./*/*_RawData/ClusterOut/ -name *ClusterOut -type f | xargs cp --backup=numbered -t <path to a log directory>
find ./*/*_RawData/ClusterErr/ -name *ClusterErr -type f | xargs cp --backup=numbered -t <path to a log directory>
  • When should you run the gap filling pipeline after completing step 1?
    In very rare cases, the stats pipeline may fail for unknown reasons.To resume the pipeline from the point of failure, you can use the GapFilling Pipeline. This is equivalent to running the pipeline by navigating to <stats pipeline directory>/SP/jobs and executing AllJobs.bch. Before doing so, make sure to edit function.R and set the parameter onlyFillNonExistingResults to TRUE. After making this change, run the pipeline by executing ./AllJobs.bch and wait for the pipeline to fill in the missing analyses. Please note that this process may take up to 2 days.

Annotation Pipeline FAQ

The IMPC_HadoopLoad command uses the power of cluster to assign the annotations to the StatPackets and transfers the files to the Hadoop cluster (transfer=TRUE). The files will be transferred to Hadoop:/hadoop/user/mi_stats/impc/statpackets/DRXX. Note: we run annotation pipeline with transfer=FALSE, so we don't transfer it now.

  • How to determine if the annotation pipeline has finished?
    Verify that there are no running jobs on the cluster.

  • Where are files located on the Hadoop cluster? (Provide the path to Federico)
    They are in directory YYY [a date in dmyyyy format]
    YYY: Hadoop:/hadoop/user/mi_stats/impc/statpackets/DRXX.YY/

  • How can one determine if a file has not been successfully transferred to the Hadoop cluster?
    If a file is located in the DDD directory and is in a gzipped format, it can be considered as successfully transferred.
    DDD: Codon:${KOMP_PATH}/impc_statistical_pipeline/IMPC_DRs/stats_pipeline_input_drXX.YY/SP/jobs/Results_IMPC_SP_Windowed/AnnotationExtractorAndHadoopLoader/tmp