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We are now ready to measure gene expression in our dataset. To do this, we will use the mRNA quantification task in the Analyze Known Genes section of the RNA-Seq workflow. mRNA quantification creates quantification creates spreadsheets showing expression at exon, transcript, and gene levels and reports raw and normalized reads for each sample.

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Your choice here depends on the method used for sample preparation. A directional mRNA-seq sample preparation protocol only synthesizes the first strand of cDNA whereas other methods reverse transcribe the mRNA into double-stranded cDNA. If double-stranded cDNA has been synthesized, the sequencer reads sequences from both the forward and reverse strands but does not discriminate between them, eliminating strand information. When strand information is preserved, it is possible for paired-end sequences to come from a combination of the forward and reverse strands. If in doubt, select Auto-detect form from the drop-down list. The data for this tutorial did not preserve strand information so we selected No.

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The _reads and _rpkm spreadsheets can be used for data analysis. Sample grouping can be visualized using PCA. Select View > Scatter Plot from the toolbar or press  on the quick action bar to create a PCA plot from the selected spreadsheet. See Exploratory Gene Expression Data Analysis for an example of using PCA plots for data analysis or consult Chapter 7 of the Partek User's Manual for a detailed introduction to PCA. With replicates in a sample group, you would also be able to use the _rpkm perform Differential  spreadsheet to perform differential expression analysis using ANOVA. 

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The contents of this spreadsheet are explained in more detail in a later section of the tutorial - Detecting Unexplained Regions. 

References

Xing Y, Yu T, Wu YN, Roy M, Kim J, Lee C: An expectation-maximization algorithm for probalisitic reconstructions of full-length isoforms from splice graphs. Nucleic Acids Res 2006, 34: 3150-3160. 

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