Description Usage Arguments Value Examples
This function computes Cohen's f, f2 and w, adjusted p-value from GLM quasi-Poisson, negative binomial and Normal distribution.
1 | get_statistics_from_file(file_expr = "", file_group = "", padj = "fdr")
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file_expr |
a CSV type file, comma (,) seperated file format, that has unique "ID" at the first column and expression data for the corresponding ID. Here is an short example.
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file_group |
a CSV type file, comma (,) seperated file format, that consists of "Col_Name", column names of "file_expr" parameter, and "Group" information of the corresponding column name. The order of "Col_Name" column have to be same to order of columns in "file_expr". Here is an example. See also the example above.
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padj |
Choose one of these c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none").
"fdr" is default option. The option is same to |
A list that consists of the following items:
$data_table | A data frame that have statistics for each IDs |
$min_rep | Common number of replicates in your group information. |
$max_rep | Maximum number of replicates in your group information. |
$nt | The number of total experiments in your expression profile. |
$ng | The number of groups in your group information. |
$method_pvalue_adjustment | The selected method for p-value adjustment |
data_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 | library(selfea)
## 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
## Write Gregori data to use 'get_statistics_from_file' function
write.csv(df_contrast,"expression.csv",row.names=FALSE)
write.csv(df_group,"group.csv",row.names=FALSE)
## Get statistics
list_result <- get_statistics_from_file("expression.csv","group.csv","fdr")
## 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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