This function is mainly used for compatibility with matrix-based clustering algorithms, such as depeche in the DepecheR package.
The flowSet or flowFrame to be converted to a dataframe.
A list of any number of characteristics that can be derived from the file names. For each entry, a gsub specification of where to find the information in the file name should be added, such as id=""..._|..."".
A long data frame with one column per PMT/APD (or fluorochrome, depending on the state of the imported files), one for the acquisition date (for fcs files) and one colum for each specified slot above. If no gsub-pattern is provided, only a single column with the full file name will be used to separate the observations from each file.
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#' # Load uncompensated data data(fullPanel) # Load the spectral unmixing matrix generated with controls from the same # experiment. These can be generated using the specMatCalc function. data(specMat) # Now unmix fullPanelUnmix <- specUnmix(fullPanel, specMat) # Transform all fluorescent channels fullPanelTrans <- arcTrans(fullPanelUnmix, transNames = colnames(fullPanelUnmix)[6:18]) # This function is primarily meant to be used with flowSets. # If we had only one flowFrame, we could just extract the data by # the use of the flowCore function exprs(), so we will convert the data to a # flowSet now. library(flowCore) fullPanelFs <- flowSet(fullPanelTrans) # Before converting to a dataframe it is important to get an idea of the # structure of the names, to be able to extract meaningful parts of the name. # Here, we have an exceptional case again, as the flowSet has just been # created, so there is actually no meaningful name of the flowFrame inside # it. So for example reasons, we will give it one now: sampleNames(fullPanelFs) <- "PBMC_full_panel_d1.fcs" # And now, we generate the dataframe: fullPanelDf <- flowSet2LongDf(fullPanelFs, idInfo = list("Tissue" = "|_full_panel_..\\.fcs", "Donor" = "...._full_panel_|\\.fcs")) # This is the result str(fullPanelDf)
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