@@ -11,8 +11,10 @@ Authors@R:

       person(given = "Yu",

              family = "Gu",

              role = "aut",

-             email = "Yu_Gu@urmc.rochester.edu"))

-Maintainer: Andrew McDavid <Andrew_McDavid@urmc.rochester.edu>

+             email = "Yu_Gu@urmc.rochester.edu"),

+      person(given = 'Thomas Lin',

+             family = 'Pedersen', 

+             role = 'ctb'))

 Description: Methods to cluster and analyze high-throughput

     single cell immune cell repertoires, especially from the 10X Genomics

     VDJ solution. Contains an R interface to CD-HIT (Li and Godzik 2006).   

@@ -36,7 +38,9 @@ Imports:

     S4Vectors,

     tidyr,

     forcats,

-    progress

+    progress,

+    stats,

+    utils

 Suggests: 

     testthat,

     readr,

@@ -61,3 +65,5 @@ RoxygenNote: 6.1.1

 URL: https://github.com/amcdavid/CellaRepertorium

 BugReports: https://github.com/amcdavid/CellaRepertorium/issues

 Roxygen: list(markdown = TRUE)

+biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing, 

+    Technology, ImmunoOncology, Clustering

@@ -19,6 +19,7 @@ cluster_test_by = function(ccdb, fields  = 'chain', tbl = 'cluster_tbl', ...){

 #' @inheritParams canonicalize_cell

 #' @return table with one row per cluster/term.

 #' @export

+#' @importFrom stats as.formula

 #'

 #' @examples

 #' library(dplyr)

@@ -159,7 +159,7 @@ pairing_tables = function(ccdb,  canonicalize_fun = canonicalize_by_chain, table

     # In how many cells do each cluster pairing appear?

     cluster_pair_tbl = oligo_cluster_pairs %>% group_by(!!!syms(cluster_ids)) %>% summarize(n_clone_pairs = dplyr::n())

     # which clusters are expanded

-    expanded_cluster = cluster_pair_tbl %>% dplyr::filter(n_clone_pairs >= min_expansion) %>% dplyr::filter_at(.vars = cluster_ids, .vars_predicate = all_vars(!is.na(.)))

+    expanded_cluster = cluster_pair_tbl %>% dplyr::filter(n_clone_pairs >= min_expansion) %>% dplyr::filter_at(.vars = cluster_ids, .vars_predicate = dplyr::all_vars(!is.na(.)))

     expanded_cluster = ungroup(expanded_cluster) %>% dplyr::select(!!!syms(cluster_ids_to_select), max_pairs = n_clone_pairs)

     if(!is.null(cluster_whitelist)){

         expanded_cluster = bind_rows(expanded_cluster, cluster_whitelist)

@@ -78,6 +78,7 @@ cluster_permute_test = function(ccdb, cell_covariate_keys,

 #' @param labels \code{factor} of length n

 #' @param covariates \code{factor} of length n

 #' @param statistic function of label and covariate

+#' @return a list containing the observed value of the statistic, its expectation (under independence), a p-value, and the Monte Carlo standard error (of the expected value).

 #' @inheritParams cluster_permute_test

 .cluster_permute_test = function(labels, covariates, statistic, n_perm, alternative,  ...){

     permp = rep(NA, n_perm)

@@ -20,6 +20,9 @@

 \item{...}{passed to \code{statistic}}

 }

+\value{

+a list containing the observed value of the statistic, its expectation (under independence), a p-value, and the Monte Carlo standard error (of the expected value).

+}

 \description{

 Cell permutation tests (internal)

 }

@@ -15,6 +15,9 @@ equalize_ccdb(x, cell = TRUE, contig = TRUE, cluster = TRUE)

 \item{cluster}{\code{logical} equalize clusters}

 }

+\value{

+\code{\link[=ContigCellDB]{ContigCellDB()}}

+}

 \description{

 The cells in \code{cell_tbl}, and clusters in \code{cluster_tbl} can potentially be a superset of the \code{contig_tbl}.

 }

@@ -13,6 +13,9 @@

 \item{deparse.level}{ignored}

 }

+\value{

+\code{\link[=ContigCellDB]{ContigCellDB()}}

+}

 \description{

 The union of the rows in each of the objects is taken,

 thus removing any rows that has an exact duplicate.  This

@@ -34,6 +34,9 @@

 \item{drop}{ignored}

 }

+\value{

+See details.

+}

 \description{

 A \code{ContigCellDB} pretend to be a \code{cell_tbl} data.frame in several regards.

 This is to enable nesting \code{ContigCellDB} objects in the \code{colData} of a \code{SingleCellExperiment}