MAGeCK - Pathway

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How to run MAGeCK Pathway on Latch

1. Find MAGeCK Pathway in your Workspace
   1. Find MAGeCK Count in “All Workflows” and open the workflow
2. Enter the parameters for MAGeCK Count
   1. First add your Gene Ranking file, if you used MAGeCK test to generate your Gene Ranking file it will be *.gene_summary.txt file from the test outputs.
   2. Then add your Pathway file in GMT format (learn more about this file and format below).
   3. TThen fill out the Output Prefix and Output Location and click Launch Workflow.
3. Within no time your results will show up in the Data tab!

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FYI

• If you want to run multiple executions of this workflow click the large plus button at the bottom of the parameters to add an additional execution of the count workflow.
• We have hidden many of the optional parameters under Hidden Parameters, you can click that if you would like to fine tune your execution run or want to use any of the advanced parameters.

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Required Parameters

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Gene Ranking

• The gene ranking file generated by MAGeCK Test. This will be the *.gene_summary.txt from the outputs.

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Pathway File in GMT Format Used For Analysis

• The GMT file format stores the pathway information and is consistent with the GMT file in Gene Set Enrichment Analysis (GSEA). The details of the GMT format can be found at GSEA website.
• You can also download different pathway files directly from GSEA MSigDB database. They can be used directly by MAGeCK.

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Gene Summary is Single Ranking File

• Enabling this tells MAGeCK that the provided file is a (single) gene ranking file, either positive or negative selection and only one enrichment comparison will be performed.

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Pathway Summary Analysis Method

• The method used by MAGeCK for testing pathway enrichment. By default MAGeCK uses [GSEA]( (Gene Set Enrichment Analysis), but also is able to use RRA (Robust Rank Aggregation).

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Output Prefix

• The prefix appended to all of the outputted files.

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Output Location

• The directory where the files produced by this subcommand will be placed. A path can either be selected or if a new path is typed in field Latch will automatically create the folders in the data viewer.

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Hidden Parameters

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Analysis Settings

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Default Alpha Value For RRA Pathway Enrichment

• The default alpha value for RRA pathway enrichment. By default MAGeCK uses 0.25.

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GSEA Permutation

• The permutation for GSEA. By default MAGeCK uses 1000.

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Negative Selection Score Ranking Column Index (2 = 3rd Column)

• Column number index in the gene summary file for gene ranking. By default MAGeCK uses 2 (the 3rd column) which is the neg|score.

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Positive Selection Score Ranking Column Index (8 = 9th Column)

• Column number in the gene summary file for gene ranking. This value is used to determine the column for positive selections and is disabled if Single Ranking is specified. Default “8” (the 9th column).

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Output Settings

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Sort Criteria for Output Summaries

• Tells MAGeCK to sort summaries either by negative selection (neg) or positive selection (pos). By default MAGeCK will sort by negative selection.

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Keep Intermediate Files

• This will have MAGeCK keep the _.pathway.high.txt and _.pathway.low.txt files which are used in generating the pathway summary file and normally deleted at the end of the execution by MAGeCK.

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Outputs

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pathway_summary.txt

• The output of the pathway summary is similar to the gene summary. You can learn more about the format of it at the MAGeCK Wiki.

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Log File

• This file contains all of the logs of the execution. This file is mostly a bunch of techno gobbledygook but you can view it to view any errors the execution might have encountered.

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Temporary Files

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pathway.high.txt

• An intermediate file used in pathway analysis.

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pathway.low.txt

• An intermediate file used in pathway analysis.

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What is MAGeCK

Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK is a computational tool to identify important genes from the recent genome-scale CRISPR-Cas9 knockout screens (or GeCKO) technology. MAGeCK can be used for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK identifies both positively and negatively selected genes simultaneously and reports robust results across different experimental conditions. MAGeCK is developed and maintained by Wei Li and Han Xu from Prof. Xiaole Shirley Liu’s lab at the Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health. MAGeCK has been used to identify functional lncRNAs from screens with close to 100% validation rate.