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| SubtitleText | Comparison of expected number of molecules of ERCC Mix A and the estimated number of transcript molecules (log2 of RPKM values), obtained by Partek’s modification of expectation-maximization algorithm. Each dot is an ERCC control. r^2 = 0.97, regression y = 0.98 * x - 74.41 |
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| SubtitleText | Comparison of expected number of molecules of ERCC Mix B and the estimated number of transcript molecules (log2 of RPKM values), obtained by Partek’s modification of expectation-maximization algorithm. Each dot is an ERCC control. r^2 = 0.97, regression y = 0.98 * x - 74.06 |
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| SubtitleText | Comparison of expected fold change (log2) vs. observed fold change (log2) in ERCC Mix A vs. Mix B, for the four groups of transcripts with the known fold change values. Each dot is an ERCC control. r^2 = 0.87, regression y = 0.96 * x + 0.09 |
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To test for possible biases in abundance estimates, we combined the approaches of Li et al. (9) and Jiang et al. (12). That amounted to regressing the estimated abundance not only on the expected abundance, but also on the transcript length, GC content, and the expected fold change, subjecting all the variables to log2 transformation.
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| Prefix | Table |
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| SubtitleText | Regression of the observed transcript abundance on the expected transcript abundance, transcript length, GC content, and the expected fold change, subjecting all the variables to log2 transformation. We started with the full model containing four covariates and performed model selection based on two criteria: adjusted r^2 (computed for all possible models) and stepwise regression (with a cutoff p-value of 0.15). The assessment was performed on the Mix A of ERCC, using Partek’s modified expectation-maximization (EM) algorithm for transcript quantification. |
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| AnchorName | table1 |
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| Prefix | Table |
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| SubtitleText | Regression of the observed transcript abundance on the expected transcript abundance and transcript length (the best model). We started with the full model containing four covariates and performed model selection based on two criteria: adjusted r^2 (computed for all possible models) and stepwise regression (with a cutoff p-value of 0.15). The assessment was performed on the Mix A of ERCC, using Partek’s modified expectation-maximization (EM) algorithm for transcript quantification. |
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| Prefix | Table |
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| SubtitleText | Regression of the observed transcript abundance on the expected transcript abundance (the benchmark model). We started with the full model containing four covariates and performed model selection based on two criteria: adjusted r^2 (computed for all possible models) and stepwise regression (with a cutoff p-value of 0.15). The assessment was performed on the Mix A of ERCC, using Partek’s modified expectation-maximization (EM) algorithm for transcript quantification. |
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| AnchorName | table3 |
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The full and the best models for the Mix B are shown in Tables 4 and 5 (respectively). Apparently, the regression failed to find evidence of any kind of bias.
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| Prefix | Table |
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| SubtitleText | Regression of the observed transcript abundance on the expected transcript abundance, transcript length, GC content, and the expected fold change, subjecting all the variables to log2 transformation. We started with the full model containing four covariates and performed model selection based on two criteria: adjusted r^2 (computed for all possible models) and stepwise regression (with a cutoff p-value of 0.15). The assessment was performed on the Mix B of ERCC, using Partek’s modified expectation-maximization (EM) algorithm for transcript quantification. |
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| AnchorName | table4 |
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| Prefix | Table |
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| SubtitleText | Regression of the observed transcript abundance on the expected transcript abundance (the best model). We started with the full model containing four covariates and performed model selection based on two criteria: adjusted R2 (computed for all possible models) and stepwise regression (with a cutoff p-value of 0.15). The assessment was performed on the Mix A of ERCC, using Partek’s modified expectation-maximization (EM) algorithm for transcript quantification. |
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| AnchorName | table5 |
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Conclusions
Although we did not report the results obtained by Cufflinks quantification algorithm, we believe that no extra analysis is necessary. First, the nature of ERCC approach does not make it a tool sensitive enough to detect the subtle effects that are estimated by Cufflinks quantification algorithm. Second, we showed that, even if those effects were taken into account, there would be very little room for improvement that could be detected by ERCC tools.
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