View source: R/fct_02_pre_process.R
pre_process | R Documentation |
This function takes in user defined values to process the data for the EDA. Processing steps depend on data format, but generally includes missing value imputation, data filtering, and data transformations.
pre_process(
data,
missing_value = c("geneMedian", "treatAsZero", "geneMedianInGroup"),
data_file_format = c(1, 2, 3),
low_filter_fpkm,
n_min_samples_fpkm,
log_transform_fpkm,
log_start_fpkm,
min_counts,
n_min_samples_count,
counts_transform,
counts_log_start,
no_fdr
)
data |
Matrix of data that has already gone through
|
missing_value |
String indicating method to deal with missing data. This should be one of "geneMedian", "treatAsZero", or "geneMedianInGroup" |
data_file_format |
Integer indicating the data format. This should be one of 1 for read counts data, 2 for normalized expression, or 3 for fold changes and adjusted P-values |
low_filter_fpkm |
Integer for low count filter if
|
n_min_samples_fpkm |
Integer for minimum samples if
|
log_transform_fpkm |
TRUE/FALSE if a log transformation should be applied to normalized expression data |
log_start_fpkm |
Integer added to log transformation if
|
min_counts |
Numeric value for minimum count if
|
n_min_samples_count |
Integer for minimum libraries with
|
counts_transform |
Integer to indicate which transformation to make if
|
counts_log_start |
Integer added to log if |
no_fdr |
TRUE/FALSE to indicate fold-changes-only data with no p values
if |
A list containing the transformed data, the mean kurtosis, the raw counts, a data type warning, the size of the original data, and p-values.
cpm()
for information on calculating counts per
million
vst()
for information on variance
stabilizing transformation
rlog()
for
information on the regularized log transformation
Other preprocess functions:
chr_counts_ggplot()
,
chr_normalized_ggplot()
,
eda_boxplot()
,
eda_density()
,
eda_scatter()
,
gene_counts_ggplot()
,
individual_plots()
,
mean_sd_plot()
,
rRNA_counts_ggplot()
,
total_counts_ggplot()
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