Get IDs that pass two filters, p-value and effect-size. This top_table will make a significant list that is less than p-value and greater than effect-size. Effect-size are calculated by obtained power level. This function requires four parameters. ex) top_table(input_data,pvalue=0.05,power_desired=0.90,method='QPF')

1 2 | ```
top_table(input_list, pvalue = 0.05, power_desired = 0.9, method = "QPF",
FC_threshold = 2)
``` |

`input_list` |
The list should be produced by 'get_statistics_from_file' or 'get_statistics_from_dataFrame' function.
See
| |||||||||||||||||

`pvalue` |
p-value should be ranged between 0 to 1. default is 0.05. | |||||||||||||||||

`power_desired` |
Give the statistical power you desired for output significant list | |||||||||||||||||

`method` |
Choose statistics method you want to use for making significant list
| |||||||||||||||||

`FC_threshold` |
Fold change you want to use. Default is 2. |

A list containing the follow items and a scatter plot that x-axis is effect size and y-axis is probability. Vertical line the plot is minimum effect size and horizontal line is maximum probability threshold. Red dots means insignificant, while blue dots are significant.

top_table | a data frame that have calculated statistics for top table IDs |

minimum_effect_size | Minimum effect size threshold |

selected_effect_size_filter | The selected effect size filter |

minimum_power | Minimum statistical power in the top_table |

selected_model | The selected probability model for calculating p-value |

alpha | Maximum adjusted p-value |

method_pvalue_adjustment | The selected method for p-value adjustment |

num_group | The number of groups used for generating the top_table |

common_replicates | The number of common replicates. |

num_columns | The number of columns (samples or experiments) |

top_table's elements | |

Cohens_W | Cohen's w |

Cohens_F | Cohen's f |

Cohens_F2 | Cohen's f2 |

Max_FC | Maximum fold change among all the possible group pairs |

QP_Pval_adjusted | Adjusted p-value from GLM quasi-Poisson |

NB_Pval_adjusted | Adjusted p-value from GLM negative binomial |

Normal_Pval_adjusted | Adjusted p-value from Normal ANOVA |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
library(selfea)
## Test selfea for single protein expression
values <- c(6,8,10,29,26,22)
groups <- c("U200","U200","U200","U600","U600","U600")
experiments <- c("exp1","exp2","exp3","exp4","exp5","exp6")
df_expr <- data.frame(ID="Protein_1",exp1=6,exp2=8,exp3=10,exp4=29,exp5=26,exp6=22)
df_group <- data.frame(Col_Name=experiments,Group=groups)
list_result <- get_statistics_from_dataFrame(df_expr,df_group)
top_table(list_result)
## For this example we will import Gregori data
## Josep Gregori, Laura Villareal, Alex Sanchez, Jose Baselga, Josep Villanueva (2013).
## An Effect Size Filter Improves the Reproducibility
## in Spectral Counting-based Comparative Proteomics.
## Journal of Proteomics, DOI http://dx.doi.org/10.1016/j.jprot.2013.05.030')
## Description:
## Each sample consists in 500ng of standard yeast lisate spiked with
## 100, 200, 400 and 600fm of a mix of 48 equimolar human proteins (UPS1, Sigma-Aldrich).
## The dataset contains a different number of technical replimessagees of each sample
## import Gregori data
data(example_data1)
df_contrast <- example_data
df_group <- example_group
## Get statistics through 'get_statistics_from_dataFrame' function
list_result <- get_statistics_from_dataFrame(df_contrast,df_group)
## Get significant features (alpha >= 0.05 and power >= 0.90)
significant_qpf <- top_table(list_result,pvalue=0.05,power_desired=0.90,method='QPF')
``` |

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