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adding files to replicate Figure 2A results in paper
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| # TFA-evaluation inputs from Ma&Brent 2020 | ||
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| These files are adapted from the Supplemental Files of Ma&Brent 2020 to show replicate results shown in Fig2A | ||
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| Example run: | ||
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| python evaluateTFA.py -a Paper_Inputs/S5_unlabeled_50-TF_TFA_values_optimized_with_ChIP-CC-TFKO_on_ZEV.csv -m Paper_Inputs/S5_TF_mRNA_in_ZEV.csv -t Paper_Inputs/S5_TF_labels.csv -s Paper_Inputs/sample_labels.csv -p 2 | ||
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| Expected output: | ||
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| TFA values will be evaluated according to file of expected activity pattern | ||
| Median rank percentile of perturbed TFs: | ||
| 86.0 p-value: 1.55e-09 | ||
| Percent of TFA changes in expected direction: | ||
| 0.66 p-value: 1.64e-02 | ||
| Percent positive correlation between TFA and mRNA: | ||
| 0.74 p-value: 4.68e-05 | ||
| Percent positive correlation between TFA and mRNA (bootstrapped median): | ||
| 0.72 | ||
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| -a | ||
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| a csv file of TF activity values, arranged as a TF x samples matrix | ||
| these TFA values were inferred by using the ChIP-CC network, sign constrained sign constrained and optimized with TFKO dataset | ||
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| -m | ||
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| a csv file of TF gene expression values from the ZEV dataset that matches the arrangement of the TFA file | ||
| this file is used to calculate the positive correlation metric | ||
| the percent of TFs with a positive correlation is calculated | ||
| p-value is calculated as probability to get that percentage with # coin flips = # TFs | ||
| the median percentage of TFs with a positive correlation across 1000 bootstrap samples is calculated | ||
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| -t | ||
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| a file that labels the rows of the activity matrix with TF names | ||
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| -s | ||
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| a file that labels the columns of the activity matrix | ||
| if a sample has an expected change in TFA, it should be named with that TF to match the TF file | ||
| wherever the sample and tf labels match, the TFA value will be used for calculating the correct direction and median rank metrics | ||
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| -p | ||
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| the direction of TFA perturbation | ||
| 0 indicates low activity | ||
| 2 indicates high activity | ||
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| YPL038W | ||
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| YPL177C | ||
| YPL248C | ||
| YPR065W |
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Paper_Inputs/S11_unlabeled_50-TF_TFA_values_optimized_with_ChIP-PC-TFKO_on_ZEV.csv
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Paper_Inputs/S12_unlabeled_50-TF_TFA_values_optimized_with_ChIP-PC-ZEV_on_TFKO.csv
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Paper_Inputs/S17_unlabeled_50-TF_TFA_values_optimized_with_DE-PC-TFKO_on_ZEV.csv
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Paper_Inputs/S18_unlabeled_50-TF_TFA_values_optimized_with_DE-PC-ZEV_on_TFKO.csv
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Paper_Inputs/S23_unlabeled_50-TF_TFA_values_optimized_with_PWM-PC-TFKO_on_ZEV.csv
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| YBL021C | ||
| YDL106C | ||
| YDL170W | ||
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| YDR216W | ||
| YDR310C | ||
| YDR421W | ||
| YER028C | ||
| YER045C | ||
| YFL031W | ||
| YGL013C | ||
| YGL035C | ||
| YGL162W | ||
| YGL166W | ||
| YGL209W | ||
| YHR006W | ||
| YHR124W | ||
| YHR206W | ||
| YIL101C | ||
| YIR013C | ||
| YIR017C | ||
| YIR018W | ||
| YIR023W | ||
| YJL056C | ||
| YJL089W | ||
| YKL185W | ||
| YLL054C | ||
| YLR014C | ||
| YLR098C | ||
| YLR176C | ||
| YLR228C | ||
| YLR256W | ||
| YLR403W | ||
| YLR451W | ||
| YML007W | ||
| YMR042W | ||
| YMR182C | ||
| YNL314W | ||
| YOL067C | ||
| YOL108C | ||
| YOR028C | ||
| YOR032C | ||
| YOR113W | ||
| YOR140W | ||
| YOR162C | ||
| YOR172W | ||
| YOR358W | ||
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Paper_Inputs/S24_unlabeled_50-TF_TFA_values_optimized_with_PWM-PC-ZEV_on_TFKO.csv
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