|
look Konig paper/code for explanation about this method
● Data Source:
BioConductor
● Keywords:
● Alias: OPIrsa
●
0 images
|
|
look Konig paper in order to have information about this function
● Data Source:
BioConductor
● Keywords:
● Alias: OPIrsaScore
●
0 images
|
|
This method takes in input a dataframe containing the results of an siRNA screen. Then it adds a set of column that are useful for sorting to the dataframe. At the moment the following sorting columns are provided: - column with the median value of the siRNA score for each gene - columns that comes out from the execution of the RSA sorting method (Renate Konig et al.)
● Data Source:
BioConductor
● Keywords:
● Alias: add_rank_col
●
0 images
|
|
This method takes in input a dataframe containing the results of an siRNA screen. This screen must contain the siRNA sequences in a dedicated column (the sequences have to be provided in the guide/antisense orientation). Then it adds a column with the seed of the siRNA sequences.
● Data Source:
BioConductor
● Keywords:
● Alias: add_seed
●
0 images
|
|
This method can be used to benchmark sorted gene vectors (A) that comes out from a siRNA screen. The benchmark is done against other sorted gene vectors (B) that we know to contain high density of real hits (e.g. the results of a second siRNA screen performed with a different library). The benchmark is performed simply comparing the top n hits of the two lists. If the two lists contain many shared best hits than we have a strong statistical signal. Then we display the number of shared best hits for different n, in a graph (if visualize_pval variable is set to true the pvalue of the t-test is plotted instead of the number of shared hits).
● Data Source:
BioConductor
● Keywords:
● Alias: benchmark_shared_hits
●
0 images
|
|
apply a function to a group of rows in the input data frame (similar to the sql group by statememnt).
● Data Source:
BioConductor
● Keywords:
● Alias: bydf, bydfa
●
0 images
|
|
This method takes an siRNA screen as input and check its consistency (i.e. check that the format of the data is suitable for the usage with our scsR package). The method prints meaningful warnings for every inconsistency that can be detected
● Data Source:
BioConductor
● Keywords:
● Alias: check_consistency
●
0 images
|
|
This method can be used to compare the performances of two different sorted gene vectors (A1 and A2) relative to a reference vector (B). To perform the comparison we use n best hits from genesetA1 and genesetA2. n is defined as the number of elements of the smallest of the two vectors(after intersecting it with the background). For the comparison see also the enrichment_geneSet method.
● Data Source:
BioConductor
● Keywords:
● Alias: compare_sorted_geneSets
●
0 images
|
|
We observed that the standard deviation of the oligos that share the same seed do change relative to their average score. In principle we could plot this information on a graph (x-axis = average of the oligos that share the same seed, y-axes = standard deviation of the oligos). We do provide this utility method to condense this information in a matrix (that reports the quantiles of the standard deviation for every score interval).
● Data Source:
BioConductor
● Keywords:
● Alias: create_sd_matrix
●
0 images
|
|
Delete a specific column in the data frame.
● Data Source:
BioConductor
● Keywords:
● Alias: delColDf
●
0 images
|
providing 
.