![]() ![]() the study ID variable will always be included to adjust for batch effects. In this case, Gender is included to control for gender differences. The "otherVariables" argument specifies other variables to be included in the regression. The function will only report the effect size for the "variableOfInterst", which is Subject Age in this case. The "variableOfInterest" argument specifies the variable that a researcher is interested in. The following code performs a regression model: fraction ~ Subject Age + Gender + study_id. To see how the fraction of cell subsets within the blood chance with age, we can use the glmAnalysis function. # join the cluster summary statistics with sample informationĪll_data = inner_join(fcs_stats,sample_info,by = "fcs_files") Sample_info $fcs_files = system.file( "extdata",sample_info $fcs_files,package = "MetaCyto") You won't need this line when running your actual meta-analysis Sample_info = read.csv(fn,stringsAsFactors = FALSE,check.names = FALSE) ![]() # Get sample information generated in step 1įn = system.file( "extdata", "sample_info_vignette.csv",package = "MetaCyto") # Collect Summary statistics generated in step 3įiles = list.files( "Example_Result/search_output",pattern = "cluster_stats_in_each_sample",recursive = TRUE,full.names = TRUE)įcs_stats = collectData(files,longform = TRUE) The first file (fcs_info.csv) contains the path (relative to working direction) of each fcs files and the study each fcs file belongs to. Step 1: data collectionįor your local datasets, the data collection has to be done manually (If you are analyzing data from ImmPort database, this step is automated, see example 2). We will go through the 4 steps: data collection, data pre-processing, identifying common cell subsets across studies and statistical analysis, to carry out the meta-analysis. One study uses flow cytometry and another study uses CyTOF. In this example, we perform meta-analysis on cytometry data from 2 studies. The rest of steps in performing meta-analysis on ImmPort data is exactly the same as Example 1. This example shows how to automate the data collection step for cytometry data downloaded from ImmPort. This example shows how to perform meta-analysis using MetaCyto on your local datasets.Įxample 2: ImmPort Cytometry Datasets. More self-contained examples, including data and code, are available on GitHub (/hzc363/MetaCyto_Examples).Įxample 1: Local Cytometry Datasets. In the second example, we provide an example to show how to use functions in MetaCyto to automate the data collection step for datasets from ImmPort. The example will walk you through all 4 steps of meta-analysis. We created 2 examples to demonstrate the use of MetaCyto in this Vignette.In the first example, we provided an example to show how MetaCyto can be used to analyze your local cytometry datasets. ![]() MetaCyto carries out the meta-analysis in 4 steps: data collection, data pre-processing, identifying common cell subsets across studies and statistical analysis. It is able to jointly analyze cytometry data from different studies with diverse sets of markers. MetaCyto is an R package that performs meta-analysis of both flow cytometry and mass cytometry (CyTOF) data. In MetaCyto: MetaCyto: A package for meta-analysis of cytometry data set2Frame: Combine cells in a flow set into a flow frame.searchCluster.batch: Search for clusters using pre-defined labels in cytometry.searchCluster: Search for clusters using pre-defined labels.sampleInfoParser: Collect sample information for fcs files in a study from.preprocessing.batch: Preprocessing fcs files from different studies in batch.preprocessing: Preprocess fcs files from a single experiment.plotGA: Plot the result from the glmAnalysis function.panelSummary: Summarize markers in panels.nameUpdator: Used to update marker names.markerFinder: Find markers in a flow frame object.labelSummary: Make a summary for the labels (cell populations) identified.labelCluster: Label each cluster as "+" or "-" or neutral for each marker.glmAnalysis: Perform generalized linear model analysis to estimate effect.flowSOM.MC: Cluster cytometry data using FlowSOM.flowHC: Cluster cytometry data using hierarchical clustering.findCutoff: Find cutoff for a 1D distribution.fcsInfoParser: Organize fcs files in a study from ImmPort into panels.densityPlot: Draw density plot for each cell cluster.collectData: Collect and combine data from multiple csv files of the same.clusterStats: Derive summary statistics for clusters.autoCluster.batch: Cluster the preprocessed fcs files from different studies in. ![]()
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