,
Agus Salim [ctb],
infinityFlow [ctb]| Title: | MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments |
|---|---|
| Description: | MAPFX is an end-to-end toolbox that pre-processes the raw data from MPC experiments (e.g., BioLegend's LEGENDScreen and BD Lyoplates assays), and further imputes the ‘missing’ infinity markers in the wells without those measurements. The pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Additionally, MAPFX can be used to normalise data from FFC assays as well. |
| Authors: | Hsiao-Chi Liao [aut, cre] ,
Agus Salim [ctb],
infinityFlow [ctb] |
| Maintainer: | Hsiao-Chi Liao <[email protected]> |
| License: | GPL-2 |
| Version: | 1.3.0 |
| Built: | 2024-12-07 06:34:19 UTC |
| Source: | https://github.com/bioc/MAPFX |
This function has been designed to do background correction for the backbone markers by using normal-exponential convolution model.
bkc_bkb( paths, bkb.v, MPC, bkb.upper.quantile = 0.9, bkb.lower.quantile = 0.1, bkb.min.quantile = 0.01, plots = TRUE )bkc_bkb( paths, bkb.v, MPC, bkb.upper.quantile = 0.9, bkb.lower.quantile = 0.1, bkb.min.quantile = 0.01, plots = TRUE )
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
bkb.v |
a vector of the names of the backbone markers (MUST match to the names in the FCS file). |
MPC |
if the data is from MPC experiments, set |
bkb.upper.quantile |
the cut-off (default = 0.9) for selecting cells used for estimating the parameter of signal. |
bkb.lower.quantile |
the cut-off (default = 0.1) for selecting cells used for estimating the parameters of noise. |
bkb.min.quantile |
the cut-off (default = 0.01) for omitting the cells with the smallest values to minimise the impact of outliers. |
plots |
logical; if TRUE (default), produce scatter plots for pre- and post- background adjusted backbone markers (calibrated values on y-axis and raw values on x-axis). |
Generating the calibrated measurements and save to medpara_bkc.bkb_no.bkcPhy_mt.rds file, and visualising the result with the scatter plots in the output directory.
Background noise corrected backbone markers and graphs if specified
Hsiao-Chi Liao and Agus Salim
This function has been designed to do background correction for the well-specific markers (PE) by using normal-exponential convolution model.
bkc_pe(paths, pe.lower.quantile = 0.1, pe.min.quantile = 0.01, plots = TRUE)bkc_pe(paths, pe.lower.quantile = 0.1, pe.min.quantile = 0.01, plots = TRUE)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
pe.lower.quantile |
the cut-off (default = 0.1) for selecting cells used for estimating the parameters of noise for infinity markers. |
pe.min.quantile |
the cut-off (default = 0.01) for omitting the cells with the smallest values to minimise the impact of outliers for infinity markers. |
plots |
logical; if TRUE (default), produce scatter plots for pre- and post- background adjusted infinity markers (calibrated values on y-axis and raw values on x-axis). |
Generating the calibrated measurements and save to bkc.pe_mt.rds file, and visualising the result with the scatter plots in the output directory.
Background noise corrected infinity markers and graphs if specified
Hsiao-Chi Liao and Agus Salim
This function has been designed to perform cluster analysis for the normalised backbone measurements and the complete dataset which includes the normalised backbone measurements and the imputed well-specific markers.
cluster.analysis(paths, bkb.v, yvar = "Legend", control.wells, plots = TRUE)cluster.analysis(paths, bkb.v, yvar = "Legend", control.wells, plots = TRUE)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
bkb.v |
a vector of the names of the backbone markers (MUST match to the names in the FCS file). |
yvar |
the name of the well-specific marker in the FCS files (e.g., "Legend"). |
control.wells |
the well label of the control wells, including the autofluorescence and the isotype controls (format: plate_well, e.g., P1_A01) |
plots |
logical; if TRUE (default), produce an UMAP embedding plot from the normalised backbone markers and the imputed infinity markers to visualise the structure of the biological clusters. |
Updating the metadata for cells in the fcs_metadata_df.rds file, adding the information of the biological clusters from the clean and complete dataset, and visualising the result with the scatter plots in the output directory.
Metadata for cells with group labels from the cluster analysis
Hsiao-Chi Liao
This function has been designed to perform cluster analysis for the normalised backbone measurements.
cluster.analysis.bkbOnly(paths, bkb.v, plots = TRUE)cluster.analysis.bkbOnly(paths, bkb.v, plots = TRUE)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
bkb.v |
a vector of the names of the backbone markers (MUST match to the names in the FCS file). |
plots |
logical; if TRUE (default), produce an UMAP embedding plot from the normalised backbone markers to visualise the structure of the biological clusters. |
Updating the metadata for cells in the fcs_metadata_df.rds file, adding the information of the biological clusters from the clean and complete dataset, and visualising the result with the scatter plots in the output directory.
Metadata for cells with group labels from the cluster analysis
Hsiao-Chi Liao
This function has been designed to convert the raw FCS files to data matrix and export to RDS files.
fcs_to_rds(paths, file_meta, yvar)fcs_to_rds(paths, file_meta, yvar)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
file_meta |
if the file names of the FCS files are in the specified format, set file_meta="auto"; otherwise set file_meta="usr" and provide "filename_meta.csv" in FCSpath. |
yvar |
the name of the well-specific marker in the FCS files (e.g., "Legend"). |
Generating fcs_metadata_df.rds and fcs_rawInten_mt.rds files in the output directory.
Raw protein intensities and the corresponding metadata from MPC experiments
Hsiao-Chi Liao
This function has been designed to convert the raw FCS files to data matrix and export to RDS files.
fcs_to_rds_bkb(paths, file_meta, MPC)fcs_to_rds_bkb(paths, file_meta, MPC)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
file_meta |
if the file names of the FCS files are in the specified format, set file_meta="auto"; otherwise set file_meta="usr" and provide "filename_meta.csv" in FCSpath. |
MPC |
if the data is from MPC experiments, set MPC = TRUE. Setting FALSE represents data from the fluorescence flow cytometry (FFC) assay. |
Generating fcs_metadata_df.rds and fcs_rawInten_mt.rds files in the output directory.
Raw protein intensities and the corresponding metadata from FFC experiments
Hsiao-Chi Liao
This function has been designed to impute/predict the unmeasured well-specific markers with regression models.
imputation_bkb.predictors( paths, chans, yvar = "Legend", cores = 4L, models.use, extra_args_regression_params, prediction_events_downsampling = NULL, impu.training, plots = TRUE )imputation_bkb.predictors( paths, chans, yvar = "Legend", cores = 4L, models.use, extra_args_regression_params, prediction_events_downsampling = NULL, impu.training, plots = TRUE )
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
chans |
a vector of the names of the backbone markers (MUST match to the names in the FCS file). |
yvar |
the name of the well-specific marker in the FCS files (has been changed to "Legend" in the first function). |
cores |
the number of cores used to perform parallel computation (default = 8L). |
models.use |
a vector of the names of the models used for imputation (an example: |
extra_args_regression_params |
a list of the lists of the parameters for running regression models. |
prediction_events_downsampling |
default = NULL (not doing subsampling). How many cells per well you want to have the imputation? (must be less than or equal to a half as we won't get the prediction for cells in the training set). |
impu.training |
logical; if FALSE (default), not impute the training set (the dataset used to train the imputation models). |
plots |
logical; if TRUE (default), visualise the distribution of R-sq from each infinity marker. |
This function returns the object of imputation of the unmeasured well-specific markers. In the output directory, the imputations are saved to predictions.Rds file. Visualisation of the imputation accuracy will be provided if specified.
A list of imputations
Hsiao-Chi Liao and InfinityFlow (Becht et. al, 2021)
Generating the M matrix for removing well effects.
initM(paths, assay, bkb.v, plots = TRUE)initM(paths, assay, bkb.v, plots = TRUE)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
assay |
the type of the input data - MPC or FFC. |
bkb.v |
a vector of the names of the backbone markers (MUST match to the names in the FCS file). |
plots |
logical; if TRUE (default), produce a UMAP embedding plot to visualise the structure of the biological clusters to form the initial M matrix for removal of well effect. |
This function has been designed to find initial biological clusters with centred transformed data.
Updating the metadata for cells in the fcs_metadata_df.rds file, adding the information of the initial biological clusters, and visualising the result with the scatter plots in the output directory.
Metadata for cells with the initial biological clusters labels added
Hsiao-Chi Liao
This function is used to normalise, including background correction and removal of batch effects, protein intensity data from FFC assays. The input data is in FCS format. The functions include data normalisation and cluster analysis.
MapfxFFC( runVignette = FALSE, runVignette_meta = NULL, runVignette_rawInten = NULL, FCSpath = NULL, Outpath = NULL, protein.v = NULL, protein.upper.quantile = 0.9, protein.lower.quantile = 0.1, protein.min.quantile = 0.01, plots.bkc.protein = TRUE, plots.initM = TRUE, plots.rmBatchEffect = TRUE, cluster.analysis.protein = TRUE, plots.cluster.analysis.protein = TRUE )MapfxFFC( runVignette = FALSE, runVignette_meta = NULL, runVignette_rawInten = NULL, FCSpath = NULL, Outpath = NULL, protein.v = NULL, protein.upper.quantile = 0.9, protein.lower.quantile = 0.1, protein.min.quantile = 0.01, plots.bkc.protein = TRUE, plots.initM = TRUE, plots.rmBatchEffect = TRUE, cluster.analysis.protein = TRUE, plots.cluster.analysis.protein = TRUE )
runVignette |
logical; if FALSE (default), specify a path to |
runVignette_meta |
the argument for the built-in metadata when running Vignette; NULL (default). |
runVignette_rawInten |
the argument for the built-in raw intensities when running Vignette; NULL (default). |
FCSpath |
path to the input directory where |
Outpath |
path to the output directory where intermediate results and final results will be stored. |
protein.v |
a vector of the names of the protein markers (MUST be the same as the names in the FCS files). For example, |
protein.upper.quantile |
the cut-off (default = 0.9) for selecting cells used for estimating the parameter of signal for protein markers. |
protein.lower.quantile |
the cut-off (default = 0.1) for selecting cells used for estimating the parameters of noise for protein markers. |
protein.min.quantile |
the cut-off (default = 0.01) for omitting the cells with the smallest values to minimise the impact of outliers during estimation. |
plots.bkc.protein |
logical; if TRUE (default), produce scatter plots for pre- and post- background adjusted protein markers (calibrated values on y-axis and raw values on x-axis). |
plots.initM |
logical; if TRUE (default), produce an UMAP embedding plot to visualise the structure of the biological clusters used to form the initial M matrix for removal of batch effects. |
plots.rmBatchEffect |
logical; if TRUE (default), produce heatmaps to visualise the unwanted (batch) effects and biological effects in the pre- and post- adjusted datasets. |
cluster.analysis.protein |
logical; if TRUE (default), perform cluster analysis using normalised protein markers. |
plots.cluster.analysis.protein |
logical; if TRUE (default), produce an UMAP embedding plot from the normalised protein markers to visualise the structure of the biological clusters. |
In the output directory, this function produces the normalised protein measurements, cell group labels from the cluster analysis using normalised proteins, and graphs will be provided if specified.
Normalised protein markers on log scale and metadata for cells
Hsiao-Chi Liao, Agus Salim
# import built-in data data(ord.fcs.raw.meta.df.out_ffc) data(ord.fcs.raw.mt_ffc) # create an Output directory for the MapfxFFC function dir.create(file.path(tempdir(), "FFCnorm_Output")) MapfxFFC_obj <- MapfxFFC( runVignette = TRUE, #set FALSE if not running this example runVignette_meta = ord.fcs.raw.meta.df.out_ffc, #set NULL if not running this example runVignette_rawInten = ord.fcs.raw.mt_ffc, #set NULL if not running this example FCSpath = NULL, # users specify their own input path if not running this example Outpath = file.path(tempdir(), "FFCnorm_Output"), protein.v = c("CD3","CD4","CD8","CD45"), protein.upper.quantile = 0.9, protein.lower.quantile = 0.1, protein.min.quantile = 0.01, plots.bkc.protein = TRUE, plots.initM = TRUE, plots.rmBatchEffect = TRUE, cluster.analysis.protein = TRUE, plots.cluster.analysis.protein = TRUE)# import built-in data data(ord.fcs.raw.meta.df.out_ffc) data(ord.fcs.raw.mt_ffc) # create an Output directory for the MapfxFFC function dir.create(file.path(tempdir(), "FFCnorm_Output")) MapfxFFC_obj <- MapfxFFC( runVignette = TRUE, #set FALSE if not running this example runVignette_meta = ord.fcs.raw.meta.df.out_ffc, #set NULL if not running this example runVignette_rawInten = ord.fcs.raw.mt_ffc, #set NULL if not running this example FCSpath = NULL, # users specify their own input path if not running this example Outpath = file.path(tempdir(), "FFCnorm_Output"), protein.v = c("CD3","CD4","CD8","CD45"), protein.upper.quantile = 0.9, protein.lower.quantile = 0.1, protein.min.quantile = 0.01, plots.bkc.protein = TRUE, plots.initM = TRUE, plots.rmBatchEffect = TRUE, cluster.analysis.protein = TRUE, plots.cluster.analysis.protein = TRUE)
This function is an end-to-end toolbox for analysing single-cell protein intensity data from the Massively-Parallel Cytometry (MPC) Experiments in FCS format. The functions include data normalisation, imputation (using backbone markers), and cluster analysis.
MapfxMPC( runVignette = FALSE, runVignette_meta = NULL, runVignette_rawInten = NULL, FCSpath = NULL, Outpath = NULL, file_meta = "auto", bkb.v = NULL, yvar = "Legend", control.wells = NULL, bkb.upper.quantile = 0.9, bkb.lower.quantile = 0.1, bkb.min.quantile = 0.01, inf.lower.quantile = 0.1, inf.min.quantile = 0.01, plots.bkc.bkb = TRUE, plots.bkc.inf = TRUE, plots.initM = TRUE, plots.rmWellEffect = TRUE, impute = TRUE, models.use = c("XGBoost"), extra_args_regression_params = list(list(nrounds = 1500, eta = 0.03)), prediction_events_downsampling = NULL, impu.training = FALSE, plots.imputation = TRUE, cluster.analysis.bkb = TRUE, plots.cluster.analysis.bkb = TRUE, cluster.analysis.all = TRUE, plots.cluster.analysis.all = TRUE, cores = 4L )MapfxMPC( runVignette = FALSE, runVignette_meta = NULL, runVignette_rawInten = NULL, FCSpath = NULL, Outpath = NULL, file_meta = "auto", bkb.v = NULL, yvar = "Legend", control.wells = NULL, bkb.upper.quantile = 0.9, bkb.lower.quantile = 0.1, bkb.min.quantile = 0.01, inf.lower.quantile = 0.1, inf.min.quantile = 0.01, plots.bkc.bkb = TRUE, plots.bkc.inf = TRUE, plots.initM = TRUE, plots.rmWellEffect = TRUE, impute = TRUE, models.use = c("XGBoost"), extra_args_regression_params = list(list(nrounds = 1500, eta = 0.03)), prediction_events_downsampling = NULL, impu.training = FALSE, plots.imputation = TRUE, cluster.analysis.bkb = TRUE, plots.cluster.analysis.bkb = TRUE, cluster.analysis.all = TRUE, plots.cluster.analysis.all = TRUE, cores = 4L )
runVignette |
logical; if FALSE (default), specify a path to |
runVignette_meta |
the argument for the built-in metadata when running Vignette; NULL (default). |
runVignette_rawInten |
the argument for the built-in raw intensities when running Vignette; NULL (default). |
FCSpath |
path to the input directory where |
Outpath |
path to the output directory where intermediate results and final results will be stored. |
file_meta |
if the file names of the FCS files are in the specified format, set |
bkb.v |
a vector of the names of the backbone markers (MUST be the same as the names in the FCS files). For example, |
yvar |
the name of the well-specific exploratory marker in the FCS files (e.g., "Legend"). |
control.wells |
the well label of the control wells, including the autofluorescence and the isotype controls (format: plate_well, e.g., P1_A01). Users need to provide this information when |
bkb.upper.quantile |
the cut-off (default = 0.9) for selecting cells used for estimating the parameter of signal for backbone markers. |
bkb.lower.quantile |
the cut-off (default = 0.1) for selecting cells used for estimating the parameters of noise for backbone markers. |
bkb.min.quantile |
the cut-off (default = 0.01) for omitting the cells with the smallest values to minimise the impact of outliers during estimation (backbone). |
inf.lower.quantile |
the cut-off (default = 0.1) for selecting cells used for estimating the parameters of noise for infinity markers. |
inf.min.quantile |
the cut-off (default = 0.01) for omitting the cells with the smallest values to minimise the impact of outliers during estimation (infinity). |
plots.bkc.bkb |
logical; if TRUE (default), produce scatter plots for pre- and post- background adjusted backbone markers (calibrated values on y-axis and raw values on x-axis). |
plots.bkc.inf |
logical; if TRUE (default), produce scatter plots for pre- and post- background adjusted infinity markers (calibrated values on y-axis and raw values on x-axis). |
plots.initM |
logical; if TRUE (default), produce an UMAP embedding plot to visualise the structure of the biological clusters used to form the initial M matrix for removal of well effects. |
plots.rmWellEffect |
logical; if TRUE (default), produce heatmaps to visualise the unwanted (well) effects and biological effects in the pre- and post- adjusted datasets. |
impute |
logical; if TRUE (default), impute the missing infinity markers. |
models.use |
a vector of the names of the models used for imputation. For example, |
extra_args_regression_params |
a list of the lists of the parameters for running regression models. The order should be the same as the models specified in |
prediction_events_downsampling |
integer (default = NULL); the number of samples used for the downsampling for the prediction. |
impu.training |
logical; if FALSE (default), not impute the training set (the dataset used to train the imputation models). |
plots.imputation |
logical; if TRUE (default), visualise the distribution of R-sq values of infinity markers. |
cluster.analysis.bkb |
logical; if TRUE (default), perform cluster analysis using normalised backbone markers for all cells. |
plots.cluster.analysis.bkb |
logical; if TRUE (default), produce an UMAP embedding plot from the normalised backbone markers to visualise the structure of the biological clusters for all cells. |
cluster.analysis.all |
logical; must set |
plots.cluster.analysis.all |
logical; must set |
cores |
the number of cores used to perform parallel computation during the imputation process (default = 4L). |
In the output directory, this function produces the normalised backbone measurements, the background corrected infinity measurements, and imputed infinity markers (if set impute = TRUE), cell group labels from the cluster analysis using both normalised backbones and the completed dataset (if impute = TRUE), and graphs will be provided if specified.
Normalised backbone markers on log scale, background noise corrected infinity markers, imputations, and metadata for cells
Hsiao-Chi Liao, Agus Salim, and InfinityFlow (Becht et. al, 2021)
# import built-in data data(ord.fcs.raw.meta.df.out_mpc) data(ord.fcs.raw.mt_mpc) # create an Output directory for the MapfxMPC function dir.create(file.path(tempdir(), "MPC_impu_Output")) # When `impute = TRUE`, randomly selecting 50% of the cells in each well for model training set.seed(123) MapfxMPC_impu_obj <- MapfxMPC( runVignette = TRUE, #set FALSE if not running this example runVignette_meta = ord.fcs.raw.meta.df.out_mpc, #set NULL if not running this example runVignette_rawInten = ord.fcs.raw.mt_mpc, #set NULL if not running this example FCSpath = NULL, # users specify their own input path if not running this example Outpath = file.path(tempdir(), "MPC_impu_Output"), file_meta = "auto", bkb.v = c( "FSC-H", "FSC-W", "SSC-H", "SSC-W", "CD69-CD301b", "MHCII", "CD4", "CD44", "CD8", "CD11c", "CD11b", "F480", "Ly6C", "Lineage", "CD45a488", "CD24", "CD103"), yvar = "Legend", control.wells = c( "P1_A01", "P2_A01", "P3_A01", "P3_F04", "P3_F05", "P3_F06", "P3_F07", "P3_F08", "P3_F09", "P3_F10", "P3_F11", "P3_F12", "P3_G01", "P3_G02"), bkb.upper.quantile = 0.9, bkb.lower.quantile = 0.1, bkb.min.quantile = 0.01, inf.lower.quantile = 0.1, inf.min.quantile = 0.01, plots.bkc.bkb = TRUE, plots.bkc.inf = TRUE, plots.initM = TRUE, plots.rmWellEffect = TRUE, impute = TRUE, models.use = c("XGBoost"), extra_args_regression_params = list(list(nrounds = 1500, eta = 0.03)), prediction_events_downsampling = NULL, impu.training = FALSE, plots.imputation = TRUE, cluster.analysis.bkb = TRUE, plots.cluster.analysis.bkb = TRUE, cluster.analysis.all = TRUE, plots.cluster.analysis.all = TRUE, cores = 2L)# import built-in data data(ord.fcs.raw.meta.df.out_mpc) data(ord.fcs.raw.mt_mpc) # create an Output directory for the MapfxMPC function dir.create(file.path(tempdir(), "MPC_impu_Output")) # When `impute = TRUE`, randomly selecting 50% of the cells in each well for model training set.seed(123) MapfxMPC_impu_obj <- MapfxMPC( runVignette = TRUE, #set FALSE if not running this example runVignette_meta = ord.fcs.raw.meta.df.out_mpc, #set NULL if not running this example runVignette_rawInten = ord.fcs.raw.mt_mpc, #set NULL if not running this example FCSpath = NULL, # users specify their own input path if not running this example Outpath = file.path(tempdir(), "MPC_impu_Output"), file_meta = "auto", bkb.v = c( "FSC-H", "FSC-W", "SSC-H", "SSC-W", "CD69-CD301b", "MHCII", "CD4", "CD44", "CD8", "CD11c", "CD11b", "F480", "Ly6C", "Lineage", "CD45a488", "CD24", "CD103"), yvar = "Legend", control.wells = c( "P1_A01", "P2_A01", "P3_A01", "P3_F04", "P3_F05", "P3_F06", "P3_F07", "P3_F08", "P3_F09", "P3_F10", "P3_F11", "P3_F12", "P3_G01", "P3_G02"), bkb.upper.quantile = 0.9, bkb.lower.quantile = 0.1, bkb.min.quantile = 0.01, inf.lower.quantile = 0.1, inf.min.quantile = 0.01, plots.bkc.bkb = TRUE, plots.bkc.inf = TRUE, plots.initM = TRUE, plots.rmWellEffect = TRUE, impute = TRUE, models.use = c("XGBoost"), extra_args_regression_params = list(list(nrounds = 1500, eta = 0.03)), prediction_events_downsampling = NULL, impu.training = FALSE, plots.imputation = TRUE, cluster.analysis.bkb = TRUE, plots.cluster.analysis.bkb = TRUE, cluster.analysis.all = TRUE, plots.cluster.analysis.all = TRUE, cores = 2L)
This function has been designed to combine the normalised backbone measurements and the normalised PE markers for later imputation.
mkImputeMT(paths)mkImputeMT(paths)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
Generating the combined data and saving to impu.input_log.mt.rds (on log scale) in the output directory.
Combined normalised backbone and infinity markers for imputation
Hsiao-Chi Liao
Metadata of the example MPC data
data(ord.fcs.raw.meta.df.out_ffc)data(ord.fcs.raw.meta.df.out_ffc)
a data.frame
Metadata of the example FFC data
data(ord.fcs.raw.meta.df.out_mpc)data(ord.fcs.raw.meta.df.out_mpc)
a data.frame
Subset of the single-cell murine lung data at steady state from an MPC experiment
data(ord.fcs.raw.mt_ffc)data(ord.fcs.raw.mt_ffc)
a matrix containing cells from 266 wells (50 cells/well)
https://flowrepository.org/id/FR-FCM-Z2LP
Subset of the sorted CD4+ and CD8+ T cells from mice splenocytes from an FFC experiment
data(ord.fcs.raw.mt_mpc)data(ord.fcs.raw.mt_mpc)
a matrix containing cells from 5 batches (50 cells/batch)
http://flowrepository.org/id/FR-FCM-Z6UG
This function has been designed to remove the unwanted effects (batch effects) from the background corrected measurements.
rmBatchEffect(paths, plots = TRUE)rmBatchEffect(paths, plots = TRUE)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
plots |
logical; if TRUE (default), produce heatmaps to visualise the unwanted (batch) effects and biological effects in the pre- and post- adjusted datasets. |
Generating the calibrated measurements and saving to bkc.adj.bkb_logScale_mt.rds (on log scale) and bkc.adj.bkb_linearScale_mt.rds (on linear scale), and visualising the result with the heatmaps in the output directory.
Normalised markers on log scale
Hsiao-Chi Liao
This function has been designed to remove the unwanted effects (well effects) from the background corrected measurements.
rmWellEffect(paths, plots = TRUE)rmWellEffect(paths, plots = TRUE)
paths |
a vector of characters of paths to store intput, intermediary results, outputs... |
plots |
logical; if TRUE (default), produce heatmaps to visualise the unwanted (well) effects and biological effects in the pre- and post- adjusted datasets. |
Generating the calibrated measurements and saving to bkc.adj.bkb_logScale_mt.rds (on log scale) and bkc.adj.bkb_linearScale_mt.rds (on linear scale), and visualising the result with the heatmaps in the output directory.
Normalised backbone markers on log scale
Hsiao-Chi Liao