Package 'flowGraph'

Description

Determines the layer on which the given phenotypes reside.

Usage

cell_type_layers(phen)

Arguments

Details

Given a vector of phenotypes, returns an equal length vector of the number of markers in each phenotype.

Value

A numeric vector with the same length as phen indicating which layer each phenotype resides on.

See Also

get_phen_list get_phen_meta

Examples

phen <- c('A+B+C-D++', 'A+B-', '', 'B++D-E+')
   cell_type_layers(phen)

Description

Extracts all unique markers from cell population phenotypes

Usage

extract_markers(phen)

Arguments

Value

A vector of unique markers

See Also

str_split

Description

Adds a feature created using feat_fun from fg OR m into a given flowGraph object. Only use this function if you cannot generate the desired features using the existing flowGraph functions starting with fg_feat_<feature name>.

Usage

fg_add_feature(
  fg,
  type = "node",
  feature,
  m = NULL,
  feat_fun = NULL,
  overwrite = FALSE,
  ...
)

Arguments

Details

fg_add_feature adds the given new feature matrix to the given flowGraph object fg updating slots feat and feat_desc. See flowGraph-class slot feat and feat_desc for what should be in these slots. We do not recommend users to directly use this method unless there is a clear understanding on how the row and column names should be specified. Instead, we recommend users to use the functions listed in the "See also" sections prefixed with "fg_feat_".

Value

flowGraph object.

See Also

flowGraph-class fg_feat_node_prop fg_feat_node_specenr fg_get_feature fg_rm_feature fg_get_feature_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 fg_get_feature_desc(fg)

 fg <- fg_add_feature(fg, type="node", feature="count_copy",
                      m=fg_data_pos30$count)
 fg_get_feature_desc(fg)

Description

Adds a feature summary into a given flowGraph object. Only use this function if your summary statistic cannot be calcuated using the fg_summary function.

Usage

fg_add_summary(
  fg,
  type = "node",
  summary_meta = NULL,
  p = NULL,
  summ_fun = NULL,
  overwrite = FALSE,
  ...
)

Arguments

Details

fg_add_summary adds the given feature summary list p or the output of the given function summ_fun to the given flowGraph object fg updating slots summary and summary_desc. See flowGraph-class slot summary and summary_desc for what should be in these slots. We do not recommend users directly use this function unless what is required is duly in the above slots is well understood — note these slots are used in plotting functions e.g. fg_plot. We instead recommend users to use the fg_summary function.

Value

flowGraph object.

See Also

flowGraph-class fg_summary fg_get_summary fg_rm_summary fg_get_summary_desc fg_add_feature

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 # get samples that we are going to compare
 m <- fg_get_feature(fg, type="node", feature="prop")
 m1_ <- m[fg_data_pos30$meta$class=="control",,drop=FALSE]
 m2_ <- m[fg_data_pos30$meta$class=="exp",,drop=FALSE]

 # define test or summary function to conduct comparison
 test_custom <- function(x,y)
     tryCatch(stats::t.test(x,y)$p.value, error=function(e) 1)
 values_p <- sapply(seq_len(ncol(m)), function(j)
     test_custom(m1_[,j], m2_[,j]) )
 values_p <- p.adjust(values_p , method="BY")

 # the user can choose to fill either parameter "p" or "summ_fun",
 # the latter of which must output a list with the same elements as "p".
 # see documentation for ?flowGraph-class, slot "summary" for
 # details on what should be in "p".
 p <- list(values=values_p, test_fun=test_custom, adjust_fun="BY")
 fg <- fg_add_summary(fg, type="node", summary_meta=list(
      feature="prop", test_name="wilcox_BY",
      class="class", label1="control", label2="exp"), p=p)

 fg_get_summary_desc(fg)

Description

Reformats cell population phenotypes into flowGraph format

Usage

fg_clean_phen(phen, markers = NULL)

Arguments

Value

Vector with the same length as phen containing reformatted and not necessarily changed cell population phenotype names.

See Also

str_extract,str_split

Examples

# fg_clean_phen(c("A+_B+","B+_notC","A-_C"))

Description

Returns a flowGraph object with only the count feature.

Usage

fg_clear_features(fg)

Arguments

Value

flowGraph object with only the count node feature.

See Also

flowGraph-class

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 fg <- fg_clear_features(fg)
 fg_get_summary_desc(fg)

Description

Removes all summary statistics in a flowGraph object; we recommend doing this to save space.

Usage

fg_clear_summary(fg)

Arguments

Value

flowGraph object with an empty summary slot.

See Also

flowGraph-class fg_summary

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores, node_features="count")
 fg_get_summary_desc(fg)

 fg <- fg_clear_summary(fg)
 fg_get_summary_desc(fg)

Description

fg_data_fca

Usage

fg_data_fca

Format

A list containing the following elements derived from the flowCAP-II AML data set for cell populations up to layer 3.

• count: A numeric sample x cell population node matrix with cell count values.

• meta: A data frame containing meta information on samples in count; it contains columns:

  • class: a string indicating whether a sample is from a "control" or "aml" subject.

  • id: a string containing sample id's.

  • train: a logical variable indicating whether a sample is from the train or test set.

  • subject: a numeric variable containing the id of the subject from whom the sample came from.

  • tube: the tube or panel number; all samples in this data set is analyzed under the 6th panel.

Source

Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH, Consortium F, Consortium DREAM, others (2013). “Critical assessment of automated flow cytometry data analysis techniques.” Nature methods, 10(3), 228–238.

Description

fg_data_pos2

Usage

fg_data_pos2

Format

A list containing the following elements for a positive control data set with markers A, B, C, D. This is a positive control data set where node A+B+C+ increased by 50

• count: A numeric sample x cell population node matrix with cell count values

• meta: A data frame containing meta information on samples in count; it contains columns:

  • id: a string containing sample id's.

  • class: a string indicating whether a sample is from a "control" or "exp" (experiment) subject.

Description

fg_data_pos30

Usage

fg_data_pos30

Format

A list containing the following elements for a positive control data set with markers A, B, C, D; note it was made with two and three thresholds for markers A and B to test functions with multiple thresholds (this is a positive control data set where nodes A+..B+..C+ increased by 50

• count: A numeric sample x cell population node matrix with cell count values

• meta: A data frame containing meta information on samples in count; it contains columns:

  • id: a string containing sample id's.

  • class: a string indicating whether a sample is from a "control" or "exp" (experiment) subject.

Description

Extracts or removes a specified set of phenotypes from a flowGraph object.

Usage

fg_extract_phenotypes(fg, phenotypes)

Arguments

Details

The summary in fg will not be modified; we recommend users recalculate them.

Value

flowGraph object.

See Also

flowGraph-class fg_get_feature_desc fg_merge fg_extract_samples fg_merge_samples

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg0 <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 fg_get_feature_desc(fg0)

 fg <- fg_extract_phenotypes(fg0, fg_get_graph(fg0)$v$phenotype[1:10])
 fg_get_feature_desc(fg)

Description

Returns a flowGraph object with only the count feature and meta data. This function clears all other features and feature summaries to save space.

Usage

fg_extract_raw(fg)

Arguments

Value

flowGraph object with all summary statistics and feature values removed except for the node count feature.

See Also

flowGraph-class

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 fg <- fg_extract_raw(fg)
 show(fg)

Description

Extracts or removes a specified set of samples from a flowGraph object.

Usage

fg_extract_samples(fg, sample_ids, rm_summary = TRUE)

Arguments

Details

The summaries in fg will not be modified; we recommend the user recalculates them.

Value

flowGraph object.

See Also

flowGraph-class fg_get_feature_desc fg_merge fg_extract_phenotypes

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg0 <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 fg_get_feature_desc(fg0)

 fg <- fg_extract_samples(fg0, fg_get_meta(fg0)$id[1:5])
 fg_get_feature_desc(fg)

Description

Converts the cell counts in a flowGraph object into cumulated cell counts; this is optional and can be done only for there is more than one threshold for one or more markers. This should also only be ran when initializing a flowGraph object as converting back and forth is computationally expensive. If the user is interested in seeing non- and cumulated counts, we recommend keeping two flowGraph objects, one for each version. This function simply converts e.g. the count of A+ or A++ into the sum of count of A+, A++, and A+++ or A++, and A+++.

Usage

fg_feat_cumsum(fg, no_cores)

Arguments

Details

fg_feat_cumsum returns the given flowGraph object with an adjusted count feature. As in our example,

Value

flowGraph object with cumulated counts.

See Also

flowGraph-class Matrix

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- flowGraph:::fg_feat_cumsum(fg, no_cores=no_cores)

Description

Generates the proportion edge feature and returns it inside the flowGraph object.

Usage

fg_feat_edge_prop(fg, no_cores = 1, overwrite = FALSE)

Arguments

Details

Given a flowGraph object, fg_feat_edge_prop returns the same flowGraph object with an additional proportions prop edge feature and its meta data. The proportions feature is made using the node count feature and is the cell count of each cell population (e.g. A+B+) over the cell count of its parent (e.g. A+); each edge then corresponds with such a relationship. The edge feature matrix has column names <from>_<to> e.g. A+_A+B+.

Value

flowGraph object containing the proportion edge feature.

See Also

flowGraph-class fg_feat_node_prop fg_feat_node_specenr fg_add_feature fg_get_feature fg_rm_feature fg_get_feature_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_feat_edge_prop(fg)

Description

Generates the SpecEnr edge feature and returns it inside the flowGraph object.

Usage

fg_feat_edge_specenr(fg, no_cores = 1, overwrite = FALSE)

Arguments

Details

Given a flowGraph object, fg_feat_edge_SpecEnr returns the same flowGraph object with an additional SpecEnr and expected proportions expect_prop edge feature and its meta data. The expected proportions edge feature is calculated by taking the ratio of the child nodes' (e.g. A+B+) expected proportion value over its parent nodes' (e.g. A+) actual proportion value. The SpecEnr feature is the actual over expected proportion ratio, logged. The edge feature matrix has column names <from>_<to> e.g. A+_A+B+.

Value

flowGraph object containing the proportion edge feature.

See Also

flowGraph-class fg_feat_node_prop fg_feat_node_specenr fg_add_feature fg_get_feature fg_rm_feature fg_get_feature_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_feat_edge_specenr(fg)

Description

For each class label in column class of meta, fg_feat_mean_class takes the column mean of the rows in the given feature matrices (as specified in node_features and edge_features) associated with that class; it then takes the difference point by point between these means and the original rows for that class.

FUNCTION_DESCRIPTION

Usage

fg_feat_mean_class(
  fg,
  class,
  no_cores = 1,
  node_features = NULL,
  edge_features = NULL
)

Arguments

Details

For all features in the given flowGraph object and for each class label in column class of meta, fg_feat_mean_class. It takes the column mean of the rows in the given feature matrices (as specified in node_features and edge_features) associated with that class; it then takes the difference point by point between these means and the original rows for that class. fg_feat_mean_class

Value

A numeric matrix whose dimensions equate to that of the input and whose values are normalized per class.

flowGraph object with normalized features.

See Also

flowGraph-class

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_feat_mean_class(fg, class="class", node_features="count",
                        no_cores=no_cores)

Description

Generates the proportion node feature and returns it inside the returned flowGraph object.

Usage

fg_feat_node_prop(fg, overwrite = FALSE)

Arguments

Details

Given a flowGraph object, fg_feat_node_prop returns the same flowGraph object, inside of which is an additional proportions prop node feature and its meta data. The proportions feature is made using the node count feature and is the cell count of each cell population over the total cell count.

Value

flowGraph object containing the proportion node feature.

See Also

flowGraph-class fg_feat_node_specenr fg_add_feature fg_get_feature fg_rm_feature fg_get_feature_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_feat_node_prop(fg)

Description

Generates the SpecEnr node feature and returns it inside the returned flowGraph object.

Usage

fg_feat_node_specenr(fg, no_cores = 1, feature = "prop", overwrite = FALSE)

Arguments

Details

Given a flowGraph object, fg_feat_node_specenr returns the same flowGraph object with an additional SpecEnr and expect_prop node feature and its meta data. The expected proportions feature is made using the prop node and edge features; therefore, the returned flowGraph will also contain these two features. For details on how these feature is calculated.

Value

flowGraph object containing the SpecEnr node feature.

References

Yue A, Chauve C, Libbrecht M, Brinkman R (2019). “Identifying differential cell populations in flow cytometry data accounting for marker frequency.” BioRxiv, 837765.

See Also

flowGraph-class fg_feat_node_prop fg_add_feature fg_get_feature fg_rm_feature fg_get_feature_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 # SpecEnr is by default calculated based on proportions
 fg <- fg_feat_node_specenr(fg, no_cores=no_cores)

 # SpecEnr can be calculated for other feature values too
 fg <- fg_feat_node_specenr(fg, feature="count")

 show(fg)

Description

Retrieves a feature matrix from a given flowGraph object, the feature type, and feature name.

Usage

fg_get_feature(fg, type = "node", feature = "count")

Arguments

Details

Returns NULL if the requested feature does not exist.

Value

A numeric matrix of the specified feature values.

See Also

flowGraph-class fg_get_feature_desc fg_add_feature fg_rm_feature fg_get_summary

Examples

data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=1)

 feature_matrix <- fg_get_feature(fg, type='node', feature='count')

Description

Retrieves and/or recalculates a feature description table for a given flowGraph object.

Usage

fg_get_feature_desc(fg, re_calc = FALSE)

Arguments

Value

A data frame where each row contains information on a feature from the given flowGraph object; its columns is as in the feat_desc slot of flowGraph-class.

See Also

flowGraph-class fg_get_feature fg_add_feature fg_rm_feature fg_get_summary_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 fg_get_feature_desc(fg, re_calc=TRUE)

Description

Retrieves a feature summary (e.g. colMeans) for samples specified by sample id's id OR class label label for class class given a feature specified by type and feat.

Usage

fg_get_feature_means(
  fg,
  type = c("node", "edge"),
  feature = "count",
  class = NULL,
  label = NULL,
  id = NULL,
  summary_fun = colMeans
)

Arguments

Value

A list containing two numeric vectors calculated using the summary_fun function on the subset of samples specified by sample id's id OR class label label for class class from a feature matrix specified by type and feat.

See Also

flowGraph-class fg_get_summary_desc fg_add_summary fg_rm_summary fg_get_summary

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)
 fg <- fg_summary(fg, no_cores=no_cores, class="class", label1="control",
                  overwrite=FALSE, test_name="t", diminish=FALSE)
 show(fg)
 feat_mean <- fg_get_feature_means(fg, type="node", feature="count",
                                   class="class", label="control")

Description

Retrieves a graph list from a given flowGraph object.

Usage

fg_get_graph(fg)

Arguments

Value

A list containing two data frames (v and ]codee) from the graph slot of the given flowGraph object containing information on the cell populations phenotype nodes and edges representing relation between cell populations.

See Also

flowGraph-class fg_plot ggdf plot_gr

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 gr <- fg_get_graph(fg)
 head(gr$v)
 head(gr$e)

Description

Retrieves the markers from a given flowGraph object.

Usage

fg_get_markers(fg)

Arguments

Value

A character vector containing the markers used in a flowGraph object.

See Also

flowGraph-class

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 fg_get_markers(fg)

Description

Retrieves sample meta from a given flowGraph object.

Usage

fg_get_meta(fg)

Arguments

Value

A data frame containing sample meta data.

See Also

flowGraph-class fg_replace_meta

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 head(fg_get_meta(fg))

Description

Retrieves a summary statistic from a given flowGraph object; while fg is required, the user can choose to input parameters summary_meta, index, or all of type, feat, test_name, class, label1, and label2. See fg_get_summary_desc for details.

Usage

fg_get_summary(
  fg,
  type = "node",
  index = NULL,
  summary_meta = NULL,
  adjust_custom = "byLayer",
  SpecEnr_filt = TRUE,
  summary_fun = colMeans,
  adjust0_lim = c(-0.1, 0.1),
  filter_adjust0 = 1,
  filter_es = 0,
  filter_btwn_tpthres = 0.05,
  filter_btwn_es = 0.5,
  default_p_thres = 1
)

Arguments

Value

A list containing elements on feature summary retrieved by the user as in the summary slot of flowGraph-class. If summary_fun is not NULL, this list also includes:

• m1: a numeric vector the same length as values; this is a summary of the samples compared e.g. mean.

• m2: a numeric vector the same length as values; this is a summary of the samples compared e.g. mean.

• cohensd: a numberic vector indicating cohen's d values considering effect size.

• cohensd_size: a factor vector interpreting cohen's d values.

• adjust0: a numeric vector indicating the percentage of samples that have a SpecEnr value in the range of adjust0_lim around 0; if there are two classes of samples being compared, we output the smaller percentage between the two classes.

• btwn: a data frame containing columns:

  • tpv1: unadjusted p-value calculated between the actual and expected raw feature values of class 1.

  • tpv2: unadjusted p-value calculated between the actual and expected raw feature values of class 2.

  • cd1: Cohen's D between the actual and expected raw feature values of class 1.

  • cd2: Cohen's D between the actual and expected raw feature values of class 2.

  • btp: unadjusted p-value calculated between the difference between actual and expected raw feature of the two classes.

  • bcd: Cohen's D calculated between the difference between actual and expected raw feature of the two classes.

  • btp_: unadjusted p-value calculated between the log ratio between actual and expected raw feature of the two classes.

  • bcd_: Cohen's D calculated between the log ratio between actual and expected raw feature of the two classes.

See Also

flowGraph-class fg_get_feature_means fg_get_summary_desc fg_add_summary fg_rm_summary fg_get_feature

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 # set features to NULL to apply summary statistic to all features.
 fg <- fg_summary(fg, no_cores=no_cores, class="class", label1="control",
                  overwrite=FALSE, test_name="t", diminish=FALSE,
                  node_features=NULL, edge_features=NULL)
 show(fg)

 feat_summ <- fg_get_summary(fg, type="node", summary_meta=list(
     feature="SpecEnr", test_name="t", class="class",
     label1="control", label2="exp"))

Description

Retrieves a feature summary description table for a given flowGraph object.

Usage

fg_get_summary_desc(fg)

Arguments

Value

A data frame where each row contains information on a feature summary from fg:

• type: feature type (i.e. 'node' or 'edge').

• feat: feature name.

• test_name: summary name.

• class: class or the column name of fg_get_meta(fg) whose values represent the class label of each sample on which the summary was created for.

• label1: A string from the class column of the meta slot indicating the label of samples compared.

• label2: A string from the class column of the meta slot indicating the label of samples compared.

See Also

flowGraph-class fg_get_summary fg_add_summary fg_rm_summary fg_get_feature_desc

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 fg_get_summary_desc(fg)

Description

Retrieves the index of the requested summary from a given flowGraph object.

Usage

fg_get_summary_index(fg, type = "node", index = NULL, summary_meta = NULL)

Arguments

Value

An integer analagous to index. If both index and summary_meta are NULL, returns 1.

See Also

flowGraph-class fg_get_summary_desc fg_add_summary fg_rm_summary fg_plot

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 # set features to NULL to apply summary statistic to all features.
 fg <- fg_summary(fg, no_cores=no_cores, class="class", label1="control",
                  overwrite=FALSE, test_name="t", diminish=FALSE,
                  node_features=NULL, edge_features=NULL)
 show(fg)

 index <- flowGraph:::fg_get_summary_index(
  fg, type="node", summary_meta=list(
    feature="SpecEnr", test_name="t", class="class",
    label1="control", label2="exp"))

Description

Retrieves a table containing all node or edge summary statistics given a flowGraph object.

Usage

fg_get_summary_tables(fg, type = "node")

Arguments

Value

A list; this output is the same as that of function fg_get_graph with additional columns. These columns contain summary statistics from the summary slot of the flowGraph object. These columns are named: <feature type: node/edge>.<feature>.<summary name>.<class>.<class labels>.

See Also

flowGraph-class fg_get_feature_means fg_get_summary_desc fg_add_summary fg_rm_summary fg_get_summary fg_get_feature

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)

 fg <- fg_summary(fg, no_cores=no_cores, class="class", label1="control",
                  overwrite=FALSE, test_name="t", diminish=FALSE)
 show(fg)

 feat_summ_table_node <- fg_get_summary_tables(fg, type="node")
 head(feat_summ_table_node)

Description

Replace sample id's in a flowGraph object.

Usage

fg_gsub_ids(fg, ids_new, ids_old = NULL)

Arguments

Value

flowGraph object with sample id's replaced.

See Also

flowGraph-class fg_get_feature_desc fg_gsub_markers

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_gsub_ids(fg, ids_new=paste0(fg_get_meta(fg)$id, "_new"))

Description

Replace marker names in a flowGraph object.

Usage

fg_gsub_markers(fg, markers_new, markers_old = NULL)

Arguments

Value

flowGraph object with marker names replaced.

See Also

flowGraph-class fg_gsub_ids

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_gsub_markers(fg, c("Anew", "Bnew", "Cnew", "Dnew"))
 fg_get_feature_desc(fg)

Description

Load a flowGraph object from a specified folder path.

Usage

fg_load(folder_path)

Arguments

Details

see function fg_save

Value

flowGraph object

See Also

fg_save

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 fg_save(fg, "tmp")
 fg <- fg_load("tmp")

Description

Merges two flowGraph objects together.

Usage

fg_merge(
  fg1,
  fg2,
  method_sample = c("union", "intersect", "setdiff", "none"),
  method_phenotype = c("intersect", "setdiff", "none")
)

Arguments

Details

fg_merge is a generic function that merges the samples and phenotypes of two flowGraph objects. Note that if method_sample="union" then method_phenotype must be set to "intersect".

Value

flowGraph object.

See Also

flowGraph-class fg_extract_samples fg_extract_phenotypes fg_merge_samples

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg0 <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg1 <- fg_extract_samples(fg0, fg_get_meta(fg0)$id[1:5])
 fg2 <- fg_extract_samples(fg0, fg_get_meta(fg0)$id[4:7])
 fg <- fg_merge(fg1, fg2, method_sample="intersect",
                          method_phenotype="intersect")
 fg_get_feature_desc(fg)

Description

Merges the samples from two flowGraph objects together; we recommend removing all summary statistics from the new flowGraph object as those won't be adjusted: fg_clear_summary.

Usage

fg_merge_samples(fg1, fg2)

Arguments

Details

Appends the samples from fg2 onto those in fg1. This function requires that the two flowGraph objects must have the same phenotypes. Therefore, we recommend users to use, instead, fg_merge.

Value

flowGraph object.

See Also

flowGraph-class fg_get_feature_desc fg_merge fg_extract_samples

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg0 <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg1 <- fg_extract_samples(fg0, fg_get_meta(fg0)$id[1:5])
 fg2 <- fg_extract_samples(fg0, fg_get_meta(fg0)$id[4:7])
 fg <- fg_merge_samples(fg1, fg2)
 fg_get_feature_desc(fg)

Description

Creates a cell hierarchy plot given a flowGraph object. If a path is not provided for fg_plot to save the plot, please use plot_gr to view plot given the output of fg_plot.

Usage

fg_plot(
  fg,
  type = "node",
  index = 1,
  summary_meta = NULL,
  adjust_custom = "byLayer",
  show_nodes_edges = NULL,
  label_max = 30,
  p_thres = 0.05,
  filter_adjust0 = 1,
  filter_es = 0,
  filter_btwn_tpthres = 1,
  filter_btwn_es = 0,
  node_labels = c("prop", "expect_prop"),
  summary_fun = colMeans,
  layout_fun = NULL,
  show_bgedges = TRUE,
  main = NULL,
  interactive = FALSE,
  visNet_plot = TRUE,
  path = NULL,
  width = 9,
  height = 9
)

Arguments

Details

fg_plot takes a flowGraph object as input and returns the graph slot of the given object with additional columns to serve as input into plot_gr for plotting using functions in the ggplot2 package. Users can choose to save a PNG version of the plot by filling out the path parameter with a full path to the PNG plot. In addition to specifying columns added from ggdf, fg_plot also adds label column(s) whose values serve as labels in the interactive version of the plot.

Value

A list of nodes and edges for plotting with the plot_gr function. Other elements in this list include show_bgedges, which has the same value as parameter show_bgedges, and main, the title of the plot.

See Also

flowGraph-class get_phen_meta ggdf plot_gr fg_get_feature fg_get_summary

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 gr <- fg_plot(fg, type="node", index=1, label_max=30,
   show_nodes_edges=NULL, p_thres=.01, node_labels=c("prop", "expect_prop"),
   path=NULL) # set path to a full path to save plot as a PNG
 # plot_gr(gr)

Description

Creates a boxplot comparing the features of samples belonging to different classes corresponding to an existing summary statistic using ggplot2.

Usage

fg_plot_box(
  fg,
  type = "node",
  index = 1,
  summary_meta = NULL,
  node_edge = 1,
  adjust_custom = "byLayer",
  p_thres = 0.05,
  filter_adjust0 = 0.5,
  filter_es = 0.5,
  filter_btwn_tpthres = 0.05,
  filter_btwn_es = 0.5,
  paired = FALSE,
  dotplot = TRUE,
  outlier = TRUE,
  all_labels = FALSE,
  show_mean = TRUE,
  main = NULL,
  path = NULL
)

Arguments

Details

The plot is made using the ggplot2 package. The interactive version is the same as the static version, it is only here to support the shiny app.

Value

A static boxplot.

See Also

flowGraph-class fg_plot plot_gr fg_get_feature fg_get_summary fg_plot_qq

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 fg_plot_box(fg, type="node", summary_meta=NULL, adjust_custom="byLayer", index=1, node_edge=10)

Description

Creates a p value vs feature difference plot where the difference is that of the features of samples belonging to different classes corresponding to an existing summary statistic.

Usage

fg_plot_pVSdiff(
  fg,
  type = "node",
  index = 1,
  summary_meta = NULL,
  adjust_custom = "byLayer",
  logged = TRUE,
  label_max = 5,
  p_thres = 0.05,
  filter_adjust0 = 1,
  filter_es = 0,
  filter_btwn_tpthres = 1,
  filter_btwn_es = 0,
  shiny_plot = FALSE,
  nodes_max = 30,
  main = NULL,
  interactive = FALSE,
  path = NULL
)

Arguments

Details

The interactive plot is made using the ggiraph package.

Value

A static or interactive p value vs difference plot.

See Also

flowGraph-class fg_plot plot_gr fg_get_feature fg_get_summary fg_plot_qq

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 gp <- fg_plot_pVSdiff(fg, type="node", summary_meta=NULL,
                       adjust_custom="byLayer", index=1, label_max=10)

Description

Creates a QQ plot of a summary statistic.

Usage

fg_plot_qq(
  fg,
  type = "node",
  index = 1,
  summary_meta = NULL,
  adjust_custom = "byLayer",
  logged = TRUE,
  p_thres = 0.05,
  filter_adjust0 = 1,
  filter_es = 0,
  filter_btwn_tpthres = 1,
  filter_btwn_es = 0,
  shiny_plot = FALSE,
  main = NULL,
  interactive = FALSE,
  path = NULL
)

Arguments

Details

The interactive plot is made using the ggiraph package.

Value

A static or interactive qq plot.

See Also

flowGraph-class fg_plot plot_gr fg_get_feature fg_get_summary

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 fg_plot_qq(fg, type="node", summary_meta=NULL, adjust_custom="byLayer", index=1,
         interactive=TRUE, logged=FALSE)

 fg_plot_qq(fg, type="node", summary_meta=NULL, adjust_custom="byLayer", index=1,
         interactive=FALSE, logged=FALSE)

Description

Replaces sample meta in a given flowGraph object.

Usage

fg_replace_meta(fg, meta)

Arguments

Value

A flowGraph object with an updated sample meta.

See Also

flowGraph-class fg_get_meta

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 head(fg_get_meta(fg))

 new_df <- fg_data_pos30$meta
 new_df$id[1] <- "newID"

 fg <- fg_replace_meta(fg, new_df)
 head(fg_get_meta(fg))

Description

Removes a feature from a given flowGraph object.

Usage

fg_rm_feature(fg, type = "node", feature = NULL)

Arguments

Details

fg_rm_feature removes a specified feature matrix from the given flowGraph object fg updating slots feat and feat_desc. See flowGraph-class slot feat and feat_desc for what should be in these slots.

Value

flowGraph object with specified feature removed.

See Also

flowGraph-class fg_add_feature fg_get_feature fg_get_feature_desc fg_rm_summary

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 no_cores=no_cores)
 fg_get_feature_desc(fg)

 fg <- fg_rm_feature(fg, type="node", feature="prop")
 fg_get_feature_desc(fg)

Description

Removes a feature summary from a given flowGraph object; while fg is required, the user can choose to input parameters summary_meta, index, or all of type, feat, test_name, class, label1, and label2. See fg_get_summary_desc for details.

Usage

fg_rm_summary(fg, type = "node", index = NULL, summary_meta = NULL)

Arguments

Value

flowGraph object.

See Also

flowGraph-class fg_get_summary fg_add_summary fg_get_summary_desc fg_rm_feature

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 fg <- fg_summary(fg, no_cores=no_cores, class="class", label1="control",
                  overwrite=FALSE, test_name="wilcox_byLayer", diminish=FALSE,
                  node_features=NULL, edge_features=NULL)
 fg_get_summary_desc(fg)

 fg <- fg_rm_summary(fg, summary_meta=c(
     feature="count",test_name="wilcox_byLayer",
     class="class", label1="control", label2="exp"))
 fg_get_summary_desc(fg)

Description

Saves flowGraph object to a specified path.

Usage

fg_save(fg, folder_path = NULL, save_plots = TRUE, paired = FALSE, ...)

Arguments

Details

See generated README.md file.

Value

TRUE if flowGraph object successfully saved.

See Also

length,c("nrow", "nrow"),NULL map

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 fg_save(fg, "tmp")

Description

Saves numerous plots for all summary statistics in a given flowGraph object to a user specified folder.

Usage

fg_save_plots(
  fg,
  plot_path,
  plot_types = "node",
  interactive = FALSE,
  adjust_custom = "byLayer",
  label_max = 10,
  box_no = 20,
  paired = FALSE,
  logged = FALSE,
  filter_adjust0 = 1,
  filter_es = 0,
  filter_btwn_tpthres = 1,
  filter_btwn_es = 0,
  overwrite = TRUE,
  node_labels = "NONE",
  ...
)

Arguments

Details

The interactive plots are made using the ggiraph package.

Value

No return; plots are saved to file.

See Also

flowGraph-class fg_plot plot_gr fg_get_feature fg_get_summary fg_plot_qq fg_plot_pVSdiff fg_plot_box

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count,
                 class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 fg_save_plots(fg, "temp")

Description

Determines cell hierarchy layout and returns the X, Y coordinate of each cell population. This function is a wrapper for set_layout_graph.

Usage

fg_set_layout(fg, layout_fun = "layout.reingold.tilford")

Arguments

Details

Given a flowGraph object, modifies the graph slot such that it contains X, Y axes for each node in accordance to a user specified layout.

Value

flowGraph object with coordinate meta data on cell populations and edges for plotting use.

Examples

no_cores <- 1
   data(fg_data_pos30)
   fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                                   prop=FALSE, specenr=FALSE,
                                   no_cores=no_cores)

   fg <- fg_set_layout(fg)
   head(fg_get_graph(fg)$v)

Description

Calculates feature summary statistics for flowGraph features; users can choose from a list of statistical significance tests/adjustments or define custom summary functions. For special cases, see example in function fg_add_summary on how to manually calculate summary statistics without using this function.

Usage

fg_summary(
  fg,
  no_cores = 1,
  class = "class",
  label1 = NULL,
  label2 = NULL,
  class_labels = NULL,
  node_features = "SpecEnr",
  edge_features = "NONE",
  test_name = "t_diminish",
  diminish = TRUE,
  p_thres = 0.05,
  p_rate = 2,
  test_custom = "t",
  effect_size = TRUE,
  adjust0 = TRUE,
  adjust0_lim = c(-0.1, 0.1),
  btwn = TRUE,
  btwn_test_custom = "t",
  save_functions = FALSE,
  overwrite = FALSE
)

Arguments

Details

fg_summary calculates a summary statistic as specified by the user in parameters test_name, diminish (p_thres, p_rate), and test_custom. The test is done for a node or edge feature of interest within a given flowGraph object as specified by parameters node_features, edge_features. It then returns information on the summary statistic inside the same flowGraph object and returns it to the user. See flowGraph-class slot summary for details on the contents.

Value

flowGraph object containing claculated summary statistics.

See Also

flowGraph-class fg_clear_summary

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)
 fg_get_summary_desc(fg)

 fg <- fg_summary(fg, no_cores=no_cores, class="class", label1="control",
                  overwrite=FALSE, test_name="t", diminish=FALSE,
                  node_features="count", edge_features="NONE")
 fg_get_summary_desc(fg)

Description

Initializes a flowGraph object given the cell counts for one or more flow cytometry sample(s). The flowGraph object returned holds meta data for each sample, each cell population node, edges representing how each cell population node relate to one another, and features for these nodes and edges.

Usage

flowGraph(
  input_,
  meta = NULL,
  class = "class",
  no_cores = 1,
  markers = NULL,
  layout_fun = "layout.reingold.tilford",
  max_layer = NULL,
  cumsumpos = FALSE,
  prop = TRUE,
  specenr = TRUE,
  path = NULL,
  calculate_summary = TRUE,
  node_features = "SpecEnr",
  edge_features = "NONE",
  test_name = "t_diminish",
  test_custom = "t",
  diminish = TRUE,
  label1 = NULL,
  label2 = NULL,
  save_plots = FALSE
)

Arguments

Details

flowGraph is the constructor for the flowGraph object. The user can choose to input as input_ a vector, a Phenotypes object (meaning there is only one sample), a matrix, or a Phenotypes object list. If the user is also inputting a sample meta data frame, it must contain a id column corresponding to sample names.

Value

flowGraph object

See Also

flowGraph-class fg_get_feature fg_get_feature_desc fg_get_summary fg_get_summary_desc fg_add_feature fg_rm_feature fg_add_summary fg_rm_summary fg_gsub_markers fg_gsub_ids fg_merge_samples fg_extract_samples fg_extract_phenotypes fg_merge registerDoParallel Matrix

Examples

no_cores <- 1
samplen <- 10
meta_file <- data.frame(
    id=1:samplen,
    class=append(rep("control", samplen/2), rep("exp", samplen/2)),
    stringsAsFactors=FALSE
)


## using the constructor -----------------------

data(fg_data_pos30)

# input: vector of load-able Phenotypes paths
fg <- flowGraph(fg_data_pos30$count[1,], no_cores=no_cores)

# input: matrix + vector of class corresponding to samples
fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                no_cores=no_cores)
# - save to file directly
# fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
#                no_cores=no_cores, path="path_to_folder)

# input: matrix + meta data frame
# fg <- flowGraph(fg_data_pos30$count, meta=fg_data_pos30$meta,
#                 no_cores=no_cores)

Description

'flowGraph': A class for storing cell count feature values for the Phenotype class.

Usage

## S4 method for signature 'flowGraph'
show(object)

Arguments

Value

a flowGraph object.

Methods (by generic)

• show: show method

Slots

feat

A list containing elements node and edge, each containing a list with feature values; each element in this list is named by the feature name and contains a numeric matrix with the sample id's as row names and cell populations phenotype labels or edge labels as column names. Column names for edge features are labelled as <from>_<to> e.g. A+_A+B+.

feat_desc

A list containing elements node and edge, each containing a data frame describing the features in the feat slot with columns:

• feat: feature name.

• nrow: number of samples.

• ncol: number of nodes or edges.

• inf: number of infinite values in the matrix.

• neginf: number of negative infinite values in the matrix.

• na: number of NA values in the matrix.

• nan: number of NaN values in the matrix.

• neg: number of negative values in the matrix.

• pos: number of positive values in the matrix.

• zero: number of 0's in the matrix.

• max: The maximum value in the matrix.

• min: The minimum value in the matrix.

summary

A list containing elements node and edge, each containing a list with a feature summary list; each feature summary in this list contains elements:

• values: a numeric vector the same length as the number of nodes or edges.

• test_custom: a function or a string name of the summary test method used.

summary_desc

A list containing elements node and edge, each containing a data frame describing the features in feat with columns:

• feat: A string indicating feature name the summary was created for.

• test_name: A string containing the name of the summary.

• class: A string corresponding to the column name of the meta slot whose values represent the class label of each sample on which the summary was created to compare or analyze.

• label1: A string from the class column of the meta slot indicating one of the labels compared to create the summary statistic.

• label2: A string from the class column of the meta slot indicating one of the labels compared to create the summary statistic.

meta

A data frame containing the column(s) id (sample id's corresponding to row names of features in the feat slot) and any other meta data pertaining to samples being analyzed.

markers

A character vector containing markers used.

edge_list

A list containing elements child and parent. These elements contain an edge list from child to parent and vice versa.

graph

A list containing data frames v and e with information on cell population nodes and edges. v contains columns:

• phenotype: The cell population node label names e.g. A+B+C+.

• phenocode: A string of "0", "1", "2", ... indicating the whether each marker is expressed on a cell population.

• phenolayer: The layer on which a cell population resides i.e. the numer of markers in its phenotype label.

• phenogroup: The markers used the make up the phenotype.

plot_layout

A string indicating the name of the igraph layout function used to layout the cell population nodes for plotting.

etc

A list containing other information (see fg_get_summary for other things stored in this slot):

• cumsumpos: A logical indicating whether cell counts in flowGraph object contains cumulated cell counts; this is optional and can be done only for there is more than one threshold for one or more markers. This should also only be ran when initializing a flowGraph object as converting back and forth is computationally expensive. If the user is interested in seeing non- and cumulated counts, we recommend keeping two flowGraph objects, one for each version. This function simply converts e.g. the count of A+ or A++ into the sum of count of A+, A++, and A+++ or A++, and A+++.

• class_mean_normalized: A logical indicating whether the features in the flowGraph object has been normalized according to some sample meta e.g. subject.

• save: A list containing a string indicating the save ID of the object and a string indicating path where the object is saved – used in function save_fg to identify whether or not to save to the same folder.

Creating Objects

Objects can be created using new("flowFrame") or the constructor flowGraph, with mandatory argument input_. Creating objects using new is discouraged.

Methods

'object' represents a flowGraph object.

• show(fg): Shows a description of the flowGraph object.

• fg_get_meta: Retrieves the sample meta data from a given flowGraph object. See fg_get_meta.

• fg_get_graph: Retrieves the cell population (v) and edge (e) meta data from a given flowGraph object. See fg_get_graph.

• fg_get_feature: Retrieves the numeric feature matrix requested by the user from a given flowGraph object. See fg_get_feature.

• fg_get_summary: Retrieves the feature summary list requested by the user from a given flowGraph object. See fg_get_summary.

• fg_get_feature_desc: Retrieves the data frame from the feat_desc slot of a given flowGraph object. See fg_get_feature_desc.

• fg_get_summary_desc: Retrieves the data frame from the summary_desc slot of a given flowGraph object. See fg_get_summary_desc.

• fg_add_feature: Adds a feature to a given flowGraph object; we do not recommend users directly use this method, instead please use wrapper functions e.g. fg_feat_node_prop, fg_feat_node_specenr, See fg_add_feature.

• fg_rm_feature: Removes a user specified feature from a given flowGraph object. See fg_rm_feature.

• fg_add_summary: Adds a feature to a given flowGraph object; we do not recommend users directly use this method, instead please use wrapper function fg_summary.

• fg_clear_summary: Removes all feature summaries from a given flowGraph object. See fg_clear_summary.

• fg_rm_summary: Removes a user specified feature summaries from a given flowGraph object. See fg_rm_summary.

• fg_gsub_markers: Substitutes marker names in a given flowGraph object. See fg_gsub_markers.

• fg_gsub_ids: substitutes sample id's in a flowGraph object See fg_gsub_ids.

• fg_merge_samples: Merges the samples of two flowGraph objects; we recomment users use the wrapper function fg_merge instead. See fg_merge_samples.

• fg_extract_samples: Extract data for specific samples from a flowGraph object. See fg_extract_samples.

• fg_extract_phenotypes: Extract data for specific cell population nodes from a flowGraph object. See fg_extract_phenotypes.

• fg_merge: Merges two given flowGraph objects. See fg_merge.

• fg_set_layout: Sets layout for cell population nodes for the purpose of plotting. See fg_set_layout.

• fg_plot: Plots cell hierarchies in the flowGraph object. See fg_plot.

Examples

showClass("flowGraph")

Description

Initializes a flowGraph object given the cell counts for one or more flow cytometry sample(s). The flowGraph object returned holds meta data for each sample, each cell population node, edges representing how each cell population node relate to one another, and features for these nodes and edges.

Usage

flowGraphSubset(
  input_,
  meta = NULL,
  class = "class",
  no_cores = 1,
  markers = NULL,
  layout_fun = "layout.reingold.tilford",
  max_layer = NULL,
  cumsumpos = FALSE,
  path = NULL,
  summary_pars = flowGraphSubset_summary_pars(),
  summary_adjust = flowGraphSubset_summary_adjust(),
  save_plots = TRUE
)

Arguments

Details

All node and edge features are trimmed such that only the significant phenotypes are left; the original input is stored in the slot etc$original_count of the returned flowGraph object.

Value

flowGraph object

Examples

## Not run: 
if(interactive()){
  data(fg_data_pos2)
  fg <- flowGraph(fg_data_pos2$count, meta=fg_data_pos2$meta, no_cores=1)
 }

## End(Not run)

Description

Default input for flowGraphSubset's summary_adjust parameter. ONLY USE THIS OVER flowGraph IF: 1) your data set has more than 10,000 cell populations and you want to speed up your calculation time AND 2) you only have one set of classes you want to test on the SAME SET OF SAMPLES (e.g. control vs experiment). As flowGraphSubset does not calculate the SpecEnr for all cell populations, so if you want to test other sets of classes on the same samples, you will not be able to test all possible cell populations on the new set of classes.

Usage

flowGraphSubset_summary_adjust()

Value

Default list parameter flowGraphSubset's summary_adjust parameter.

Examples

flowGraphSubset_summary_adjust()

Description

Default input for flowGraphSubset's summary_pars parameter.

Usage

flowGraphSubset_summary_pars()

Value

Default list parameter flowGraphSubset's summary_pars parameter.

Examples

flowGraphSubset_summary_pars()

Description

Wrapper for purrr::map and furrr::future_map to handle parallel-ization

Usage

fpurrr_map(x, f, no_cores = 1, prll = TRUE, ...)

Arguments

Details

Wrapper for purrr::map and furrr::future_map to handle parallel-ization easily; note that future must have been ran already outside of the function and outputs will always be a list.

Value

Unnested named list.

See Also

map future_map

Description

Gets the child populations of a vector of given cell populations parens and updates pchild the edge list if edge list doesn't contain the requested information.

Usage

get_child(parens, pchild, pc_i, ac__, meta_cell__)

Arguments

Value

A list containing child populations of parens; also globally updates pchild.

See Also

map,keep

Description

Gets the edge proportions of the edges in edge matrix edf_ and updates ep edge proportion matrix if it didn't contain the requested information.

Usage

get_eprop(edf_, ep, mp_, no_cores = 1)

Arguments

Value

ep with only the specific columns (edges) requested; also updates ep globally.

Description

Gets the parent populations of a vector of given cell childs and updates pparen the edge list if edge list doesn't contain the requested information.

Usage

get_paren(childs, pparen, pc__i, ac_, meta_cell_)

Arguments

Value

A list containing parent populations of childs; also globally updates pparen.

Description

Creates edge lists indicating relationships between cell populations given meta data on these cell populations produced by the get_phen_meta function.

Usage

get_phen_list(meta_cell = NULL, phen = NULL, no_cores = 1)

Arguments

Value

A list containing 'pchild', an edge list indicating where edges point to, 'pparen', an edge list indicating where edges point from, and 'edf', a data frame where each row contains the nodes an edge points 'from' and 'to'.

See Also

get_phen_meta cell_type_layers

Examples

phen <- c('A+B-C+', 'A+B-', 'A+')
   get_phen_list(phen=phen)

Description

Generates phenotype meta data given a vector of phenotypes and optionally phenocodes.

Usage

get_phen_meta(phen, phenocode = NULL)

Arguments

Value

A data frame with columns containing meta data on cell poulation nodes with columns:

• phenotype: cell population node label e.g. "A+B+".

• phenocode: a string penocode containing a numeric corresponding to the phenotype column e.g. "2200".

• phenolayer: a numeric layer on which a cell population resides in e.g. 2.

See Also

get_phen_list cell_type_layers

Examples

phen <- c('A+B+C-D++', 'A+B-', '', 'B++D-E+')
   phenc <- c('22130','21000','00000','03012')
   get_phen_meta(phen, phenc)

Description

Prepares a given node and edge graph list for plotting by function plot_gr; do not use this function on its own.

Usage

ggdf(gr0)

Arguments

Details

codeggdf adds to the data frames v and e in slot graph from a flowGraph object specifying plotting options as required by plot_gr:

• v

  • size: a numeric indicating node size.

  • colour: a numeric or string indicating node colour.

  • label: a string indicating the label of a node.

  • label_long: a string indicating teh long label of a node; used in interactive plots in plot_gr.

  • label_ind: a vector of logical variables indicating which nodes to add a label to in a static plot.

  • v_ind: a vector of logical variables indicating which nodes to plot.

• e

  • colour: a numeric or string indicating edge colour.

  • e_ind: a vector of logical variables indicating which edges to plot.

Value

A list containing data frames e and v, each with additional meta data column.

See Also

flowGraph-class get_phen_meta plot_gr

Examples

no_cores <- 1
 data(fg_data_pos30)
 fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                 prop=FALSE, specenr=FALSE,
                 no_cores=no_cores)

 gr_ <- ggdf(fg_get_graph(fg))
 head(gr_$v)
 head(gr_$e)

Description

loop_ind_f is a helper function that splits a vector of loop indices into a list of multiple loop indices for use in parallel processes within the flowGraph package.

Usage

loop_ind_f(x, n)

Arguments

Value

list of n vectors with elements from x.

Examples

old_loop_inds <- 1:10
 no_cores <- 5

 new_loop_inds <- flowGraph:::loop_ind_f(old_loop_inds, no_cores)
 # future::plan(future::multisession)
 # example_indices <- furrr::future_map(new_loop_inds, function(ii) {
 #     purrr::map(ii, function(i) i )
 # s})

Description

Used only in the fg_feat_mean_class function; for each class in the classes vector, meandiff takes the column mean of the rows in the given matrix associated with that class; it then takes the difference point by point between these means and the original rows for that class.

Usage

mean_diff(m0, classes)

Arguments

Value

A numeric matrix whose dimensions equate to that of the input and whose values are normalized per class.

See Also

fg_feat_mean_class

Examples

classes <- append(rep('apples',4), rep('oranges',3))
 m0 <- matrix(rnorm(35), nrow=7)
 m <- flowGraph:::mean_diff(m0, classes)

Description

FUNCTION_DESCRIPTION

Usage

ms_create(mp_, me_)

Arguments

Value

Numerical sample x cell population matrix w/ SpecEnr.

Description

Determines which phenotypes are statistically significant based on SpecEnr.

Usage

ms_psig(
  ms_,
  summary_pars,
  summary_adjust,
  test_cust,
  test_custom,
  lyrno,
  mp_,
  me_
)

Arguments

Value

A logical vector the same length as the number of columns in ms_ indicating whether or not each phenotype is significant; used only for the fast version of flowGraph to determine whether or not to keep testing the phenotypes' children.

Description

Plots a cell hierarchy given the output from fg_plot, a list of nodes and edges.

Usage

plot_gr(
  gr,
  main = NULL,
  show_bgedges = TRUE,
  colour_palette = NULL,
  label_coloured = TRUE,
  shiny_plot = FALSE,
  interactive = FALSE,
  visNet_plot = TRUE,
  colour_edges = FALSE,
  ...
)

Arguments

Value

A ggplot object if interactive is set to FALSE; a ggiraph object if interactive is set to TRUE.

See Also

flowGraph-class fg_plot get_phen_meta ggdf fg_get_feature fg_get_summary

Examples

no_cores <- 1
 data(fg_data_pos2)
 fg <- flowGraph(fg_data_pos2$count, class=fg_data_pos2$meta$class,
                 no_cores=no_cores)

 # fg <- fg_summary(fg, no_cores=no_cores, class="class", control="control",
 #                  overwrite=FALSE, test_name="t_byLayer", diminish=FALSE)

 gr_summary <- fg_plot(
   fg, type="node", p_thres=.05, show_bgedges=TRUE,
   path=NULL) # set path to a full path to save plot as a PNG

 plot_gr(gr_summary, main=gr_summary$main, show_bgedges=TRUE)

 plot_gr(gr_summary, main=gr_summary$main, show_bgedges=TRUE, interactive=TRUE)

Description

Determines cell hierarchy layout and returns the X, Y coordinate of each cell population.

Usage

set_layout_graph(gr, layout_fun = "layout.reingold.tilford")

Arguments

Value

A list containing data frames e and v; each data frame contains an X, Y column or coordinate for each node and edge.

Examples

no_cores <- 1
   data(fg_data_pos30)
   fg <- flowGraph(fg_data_pos30$count, class=fg_data_pos30$meta$class,
                                   prop=FALSE, specenr=FALSE,
                                   no_cores=no_cores)

   head(set_layout_graph(fg_get_graph(fg)))

Description

Summarizes a numeric matrix.

Usage

summary_table(m, feat_type = "")

Arguments

Value

A data frame containing one row summarizing m; see fg_get_feature_desc.

Examples

summary_table(matrix(rnorm(12),nrow=3), feat_type='random')

Description

Converts input into a significance test function

Usage

test_c(test_custom)

Arguments

Value

a statistical significance test function.

See Also

t.test,wilcox.test, ks.test,var.test, chisq.test

Description

Given a time, prints the time elapsed from that time until now.

Usage

time_output(start, msg = "")

Arguments

Value

Prints to console, the time from which process started start - ended, and > time elapsed from start until now.

Examples

start <- Sys.time()
 flowGraph:::time_output(start,'start - now > time elapsed')

Description

Formats time into a string HH:MM:SS given time zone.

Usage

tstr(time)

Arguments

Value

Time formatted as a string; used in time_output function.

Examples

# NOT EXPORTED
 flowGraph:::tstr(Sys.time())