cytoqc-package: cytoqc: Quality control and standardization of cytometry data

Description Details


Provides quality control, quality assessment, and data standardization tools for cytometry.


The cytoqc package is used to perform QC and data standardization for flow and mass cytometry data stored in GatingSet objects or FCS files.

It simplifies the tasks of standardizing channel, markers, keywords, gating schemes, gate names, and so forth, necessary to run cytometry data through automated analysis pipelines.

The process for running cytoqc from FCS files consists of several steps.

First, one loads the data from FCS files using cqc_load_fcs(). The data is loaded into a cqc_cf_list object.

Next a QC evaluation of "channel", "marker" or "panel" is performed using cqc_check(). This returns a cqc_check family object, specific to either the "channel", "marker" or "panel" qc. This object can be viewed using summary. A nice print out of the cqc_check for reports can be generated with knit_print.

Next a reference group is chosen using cqc_match() and passing in a vector of channels or markers to use as the referece. Alterately one can pass in the group_id of the group to use as a reference. This returns a cqc_match_result.

Next we resolve discrepancies using cqc_recommend on the cqc_match_result. This will propose several ways to resolve discrepancies in the markers, channels, or panel. Again, a pretty print out can be generated using knit_print.

Finally the proppsed solution can either be edited (by writing it out to a csv file and editing it), or by applying cqc_fix() to the cqc_solution.

The QC can be updated after applying the fix using cqc_check again.

Any groups that cannot be standardized can be dropped using cqc_drop_groups.

The tidied data can be coerced to a cytoset via a call to the cytoset() constructor.

To summarize: Read -> group -> set reference -> propose a solution to qc issues -> apply the solution -> store cleaned data.

See the different vignettes for additional details.

RGLab/cytoqc documentation built on Nov. 25, 2020, 11:27 p.m.