a1725dd6 |
Type: Package
|
d31ff2a7 |
Package: CellaRepertorium
|
4688031c |
Title: Data structures, clustering and testing for single
cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq)
|
5cf49a54 |
Version: 1.6.0
|
d31ff2a7 |
Authors@R:
c(person(given = "Andrew",
family = "McDavid",
role = c('aut', 'cre'),
email = "Andrew_McDavid@urmc.rochester.edu"),
person(given = "Yu",
family = "Gu",
role = "aut",
|
d507c94c |
email = "Yu_Gu@urmc.rochester.edu"),
|
a32b7c8b |
person(given = 'Erik',
family = 'VonKaenel',
role = 'aut'
),
|
328e977d |
person(given = 'Aaron',
family = 'Wagner',
role = 'aut'),
|
d507c94c |
person(given = 'Thomas Lin',
family = 'Pedersen',
role = 'ctb'))
|
d31ff2a7 |
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).
Methods to visualize and analyze paired heavy-light chain data.
Tests for specific expansion, as well as omnibus oligoclonality under
hypergeometric models.
|
a1725dd6 |
License: GPL-3
|
d31ff2a7 |
Depends:
|
608f89fd |
R (>= 4.0)
|
a1725dd6 |
Imports:
|
d31ff2a7 |
dplyr,
tibble,
stringr,
Biostrings,
Rcpp,
reshape2,
methods,
|
608f89fd |
rlang (>= 0.3),
|
d31ff2a7 |
purrr,
Matrix,
S4Vectors,
|
6c82e731 |
BiocGenerics,
|
d31ff2a7 |
tidyr,
|
4688031c |
forcats,
|
d507c94c |
progress,
stats,
utils
|
66eb4793 |
Suggests:
|
efd7d3a1 |
testthat,
readr,
knitr,
rmarkdown,
ggplot2,
BiocStyle,
|
d31ff2a7 |
ggdendro,
broom,
lme4,
|
5a52904d |
RColorBrewer,
SingleCellExperiment,
|
a32b7c8b |
scater,
|
6432de04 |
broom.mixed,
|
befdd6f5 |
cowplot,
igraph,
ggraph
|
d31ff2a7 |
LinkingTo:
Rcpp
VignetteBuilder:
knitr
Encoding: UTF-8
|
a1725dd6 |
NeedsCompilation: yes
|
dfaded11 |
RoxygenNote: 7.1.2
|
31cd01a2 |
URL: https://github.com/amcdavid/CellaRepertorium
BugReports: https://github.com/amcdavid/CellaRepertorium/issues
|
be99b48a |
Roxygen: list(markdown = TRUE)
|
d507c94c |
biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing,
Technology, ImmunoOncology, Clustering
|