Partek Flow Documentation

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  • Click the Filtered counts data node 
  • Expand the Pre-analysis tools section of the task menu
  • Click Pool cells (Figure 1)
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SubtitleTextSelecting the Pool cells task
AnchorNamePool cells

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The pool cells dialog offers the option to pool cells by different cell-level attributes by taking the sum, maximum, mean, or median of the expression values of individual cells. Mean is selected by default (Figure 2).

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SubtitleTextOutput of the Pools cells task
AnchorNamePools cells output

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The Glioma data node is equivalent to a bulk RNA-Seq gene counts data node and the same analysis steps can be performed on it including PCA and differential expression analysis.

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  • Click Glioma (multi-sample) to return to the Analyses tab
  • Click the Glioma data node 
  • Expand Differential analysis Statistics in the task menu
  • Click GSA  Differential analysis in the task menu menu
  • Select GSA 
  • Check the Subtype attribute (Figure 5)
  • Click Next 

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SubtitleTextSelecting factors to include in the statistical test
AnchorNameIncluding factors

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Next, we can set up a comparison between astrocytoma and oligodendroglioma subtypes. 

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  • Click Astrocytoma in the top panel
  • Click Oligodendroglioma in the bottom panel
  • Click Add comparison (Figure 6)
  • Filtering is not necessary so click None



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SubtitleTextAdding a comparison of astrocytoma and oligodendroglioma subtypes
AnchorNameAdding comparison

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Adding Astrocytoma vs. Oligodendroglioma will give fold-change and p-value for the comparison. Fold-change will be calculated as astrocytoma (numerator) over oligodendroglioma (denominator).

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SubtitleTextGSA task produces a Feature list data node
AnchorNameGSA results

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  • Double click the GSA data node

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SubtitleTextViewing the GSA report table
AnchorNameGSA report

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Genes are listed by ascending P-value, so the most significant genes are at the top of the list. Results for all genes can be visualized using a volcano plot.

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SubtitleTextApplying a filter
AnchorNameFilter settings

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You will be returned to the Analyses tab and a new Differential analysis filter task will be added. This will produce a new Filtered feature list data node.

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SubtitleTextOpen the hierarchical clustering heat map by double clicking the task node
AnchorNameOpen the heat map

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The hierarchical clustering heatmap displays samples on rows and genes on columns (Figure 13). The colors are scaled (normalized) expression values represented by the Z-score (standardized) for a heatmap by default.

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SubtitleTextHeatmap
AnchorNameHierarchical clustering heatmap

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The plot is interactive and configurable.

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SubtitleTextConfiguring the heatmap
AnchorNameModified heatmap

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The heatmap can be saved as a publication-quality image by clicking the save image icon  or sent to a page in the Notebook tab by clicking . For more information about customizing and interacting with the heatmap, please see the Hierarchical Clustering section of the user's manual. 

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  • Click the Filtered feature list data node
  • Expand Biological interpretation in the task menu
  • Click Gene set enrichment analysis
  • Select Gene set database
  • Select Homo sapiens (human) - hg38 from the Assembly drop-down menu
  • Select a gene set database from the drop-down menu. If one isn't available, choose Add gene set database and download one

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SubtitleTextGene set enrichment task node
AnchorNameEnrichment output

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The GO enrichment task report lists every analyzed gene set, ranked by Enrichment score, with P-value, number of genes in list, and number of genes not in list given for each (Figure 16). Using the default GO database, each gene set name is a link to the geneontology.org web-page for that GO term. Hovering over  shows the numbers of genes in the set in the list, in the set not in the list, in the list not in the set, and not in the list not in the set. You can view the genes in and not in the gene set by selecting . For more information about enrichment analysis and using custom gene sets, please see the Gene Set Enrichment section of the user's manual. 

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SubtitleTextViewing results of Enrichment analysis. Note that this GO enrichment test was performed using the hg38 - 2019_11_01 GO database. Your results may vary of you have used different version
AnchorNameGO enrichment report

Pathway enrichment is functionally identical to Gene set enrichment analysis, but utilizes the can also be performed using the the KEGG pathway database as the source for its gene sets. Note, to perform these next steps, you need to have the Pathway toolkit enabled. 

  • Click Glioma (multi-sample) to return to the Analyses tab
  • Click the Filtered feature list data node
  • Expand Biological interpretation in the task menu
  • Click Pathway enrichment (Figure 17)
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SubtitleTextInvoking the Pathway enrichment analysis
AnchorNameInvoking Pathway enrichment

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  • Gene set enrichment
  • Select KEGG database
  • Verify that the species in the drop-down menu is Homo sapiens 

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SubtitleTextKEGG map for Spliceosome pathway. Colored by fold change for astrocytoma vs. oligodendroglioma.
AnchorNameSpliceosome KEGG map

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Here, we can see that several components of the spliceosome pathway are upregulated in astrocytoma vs. oligodendroglioma.

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