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Beginning of making filtering steps all work together #41
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#TRUE means it should be filtered, FALSE means it shouldn't be filtered | ||
combined_filter <- rowSums(reduce(possible_filters, cbind)) >= min_n_filters | ||
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gimap_dataset$filtered <- NULL #TODO: Filtered version of the data can be stored here |
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Add here a nice readable version of combined_filter to gimap_dataset
and then the filtered version of the data to filtered_data
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This is in a later PR so I think this means this is set to go!
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MERGE IT
@@ -18,18 +23,57 @@ | |||
#' | |||
#' # To see filtered data | |||
#' gimap_dataset$filtered_data | |||
#' | |||
#' # If you want to only use a single filter or some subset, specify which using the filter_type parameter | |||
#' gimap_dataset <- gimap_filter(gimap_dataset, filter_type = "zero_count_only") |
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YAY DOCUMENTATION!
#TRUE means it should be filtered, FALSE means it shouldn't be filtered | ||
combined_filter <- rowSums(reduce(possible_filters, cbind)) >= min_n_filters | ||
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||
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gimap_dataset$filtered <- NULL #TODO: Filtered version of the data can be stored here |
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This is in a later PR so I think this means this is set to go!
Sorry, this PR is the first step of filtering where the
filter_ki
branch builds on thefilter_qc_ki5
branch. PR #40 is trying to mergefilter_ki2
intofilter_ki
. I submitted those PRs out of order since this one will be PR #41.This PR makes a lot of documentation changes, explaining parameters and how to use the
gimap_filter()
function.It also builds the groundwork for the filters working together.
reduce()
function tocbind
these filters together. This approach ignores NULLs and returns a df with column(s) of TRUEs and FALSEs.min_n_filters
parameter is used together withrowSums
to find out which pgRNA constructs are flagged by at least that minimum number of filters that would result in the pgRNA constructs being removed from the dataset -- creating a consensus or combined filter.