vis.shared.clonotypes: Visualisation of shared clonotypes occurrences among...
In tcR: Advanced Data Analysis of Immune Receptor Repertoires
Description
Usage
Arguments
Value
See Also
Examples
Description
Visualise counts or proportions of shared clonotypes among repertoires.
Code adapted from https://www.r-bloggers.com/ggplot2-cheatsheet-for-visualizing-distributions/.
Usage
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vis.shared.clonotypes(
.shared.rep,
.x.rep = NA,
.y.rep = NA,
.title = NA,
.ncol = 3,
.point.size.modif = 1,
.cut.axes = T,
.density = T,
.lm = T,
.radj.size = 3.5,
.plot = T
)
Arguments
.shared.rep
Shared repertoires, as from shared.repertoire function.
.x.rep
Which repertoire show on x-axis. Either a name or an index of a repertoire
in the .shared.rep or NA to choose all repertoires.
.y.rep
Which repertoire show on y-axis. Either a name or an index of a repertoire
in the .shared.rep or NA to choose all repertoires.
.title
Main title of the plot.
.ncol
Number of columns in the resulting plot.
.point.size.modif
Modify this to correct sizes of points.
.cut.axes
If T than cut axes' limits to show only frequencies that exists.
.density
If T than plot densities of shared and unique clonotypes.
.lm
If T than fit and plot a linear model to shared clonotypes.
.radj.size
Size of the text for R^2-adjusted.
.plot
If F than return grobs instead of plotting.
Value
ggplot2 object or plot
See Also
shared.repertoire
Examples
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## Not run:
data(twb)
# Show shared nucleotide clonotypes of all possible pairs
# using the Read.proportion column
twb.sh <- shared.repertoire(twb, "n0rp")
vis.shared.clonotypes(twb.sh, .ncol = 4)
# Show shared amino acid + Vseg clonotypes of pairs
# including the Subj.A (the first one) using
# the Read.count column.
twb.sh <- shared.repertoire(twb, "avrc")
vis.shared.clonotypes(twb.sh, 1, NA, .ncol = 4)
# same, just another order of axis
vis.shared.clonotypes(twb.sh, NA, 1, .ncol = 4)
# Show shared nucleotide clonotypes of Subj.A (the first one)
# Subj.B (the second one) using the Read.proportion column.
twb.sh <- shared.repertoire(twb, "n0rp")
vis.shared.clonotypes(twb.sh, 1, 2)
# Show the same plot, but with much larget points.
vis.shared.clonotypes(twb.sh, 1, 2, .point.size.modif = 3)
## End(Not run)
Example output
Loading required package: ggplot2
Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Loading required package: gridExtra
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
Loading required package: reshape2
Loading required package: igraph
Attaching package: ‘igraph’
The following objects are masked from ‘package:dplyr’:
as_data_frame, groups, union
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
========================================
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! The tcR package WILL SOON BE ORPHANED
!! AND REMOVED FROM CRAN.
!!
!! A new package is available that is
!! designed to replace tcR:
!! immunarch -- https://immunarch.com/
!!
!! We will be happy to help you to move
!! to the new package. Feel free to contact us:
!! http://github.com/immunomind/immunarch
!!
!! Sincerely,
!! immunarch dev team and
!! Vadim I. Nazarov, lead developer of tcR
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
=======================================
Attaching package: ‘tcR’
The following object is masked from ‘package:igraph’:
diversity
Aggregating sequences...
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|======================================================================| 100%
Merging data tables...
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|======================= | 33%
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|=============================================== | 67%
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|======================================================================| 100%
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Warning message:
Removed 1 rows containing missing values (geom_smooth).
Aggregating sequences...
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|=================================== | 50%
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|==================================================== | 75%
|
|======================================================================| 100%
Merging data tables...
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|======================= | 33%
|
|=============================================== | 67%
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|======================================================================| 100%
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Aggregating sequences...
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|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
Merging data tables...
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|======================= | 33%
|
|=============================================== | 67%
|
|======================================================================| 100%
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
tcR documentation built on July 2, 2020, 3:18 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value See Also Examples
Description
Visualise counts or proportions of shared clonotypes among repertoires. Code adapted from https://www.r-bloggers.com/ggplot2-cheatsheet-for-visualizing-distributions/.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 | vis.shared.clonotypes(
.shared.rep,
.x.rep = NA,
.y.rep = NA,
.title = NA,
.ncol = 3,
.point.size.modif = 1,
.cut.axes = T,
.density = T,
.lm = T,
.radj.size = 3.5,
.plot = T
)
|
Arguments
.shared.rep |
Shared repertoires, as from shared.repertoire function. |
.x.rep |
Which repertoire show on x-axis. Either a name or an index of a repertoire
in the |
.y.rep |
Which repertoire show on y-axis. Either a name or an index of a repertoire
in the |
.title |
Main title of the plot. |
.ncol |
Number of columns in the resulting plot. |
.point.size.modif |
Modify this to correct sizes of points. |
.cut.axes |
If T than cut axes' limits to show only frequencies that exists. |
.density |
If T than plot densities of shared and unique clonotypes. |
.lm |
If T than fit and plot a linear model to shared clonotypes. |
.radj.size |
Size of the text for R^2-adjusted. |
.plot |
If F than return grobs instead of plotting. |
Value
ggplot2 object or plot
See Also
shared.repertoire
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## Not run:
data(twb)
# Show shared nucleotide clonotypes of all possible pairs
# using the Read.proportion column
twb.sh <- shared.repertoire(twb, "n0rp")
vis.shared.clonotypes(twb.sh, .ncol = 4)
# Show shared amino acid + Vseg clonotypes of pairs
# including the Subj.A (the first one) using
# the Read.count column.
twb.sh <- shared.repertoire(twb, "avrc")
vis.shared.clonotypes(twb.sh, 1, NA, .ncol = 4)
# same, just another order of axis
vis.shared.clonotypes(twb.sh, NA, 1, .ncol = 4)
# Show shared nucleotide clonotypes of Subj.A (the first one)
# Subj.B (the second one) using the Read.proportion column.
twb.sh <- shared.repertoire(twb, "n0rp")
vis.shared.clonotypes(twb.sh, 1, 2)
# Show the same plot, but with much larget points.
vis.shared.clonotypes(twb.sh, 1, 2, .point.size.modif = 3)
## End(Not run)
|
Example output
Loading required package: ggplot2
Loading required package: dplyr
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Loading required package: gridExtra
Attaching package: ‘gridExtra’
The following object is masked from ‘package:dplyr’:
combine
Loading required package: reshape2
Loading required package: igraph
Attaching package: ‘igraph’
The following objects are masked from ‘package:dplyr’:
as_data_frame, groups, union
The following objects are masked from ‘package:stats’:
decompose, spectrum
The following object is masked from ‘package:base’:
union
========================================
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! The tcR package WILL SOON BE ORPHANED
!! AND REMOVED FROM CRAN.
!!
!! A new package is available that is
!! designed to replace tcR:
!! immunarch -- https://immunarch.com/
!!
!! We will be happy to help you to move
!! to the new package. Feel free to contact us:
!! http://github.com/immunomind/immunarch
!!
!! Sincerely,
!! immunarch dev team and
!! Vadim I. Nazarov, lead developer of tcR
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
=======================================
Attaching package: ‘tcR’
The following object is masked from ‘package:igraph’:
diversity
Aggregating sequences...
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
Merging data tables...
|
| | 0%
|
|======================= | 33%
|
|=============================================== | 67%
|
|======================================================================| 100%
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Warning message:
Removed 1 rows containing missing values (geom_smooth).
Aggregating sequences...
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
Merging data tables...
|
| | 0%
|
|======================= | 33%
|
|=============================================== | 67%
|
|======================================================================| 100%
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
Aggregating sequences...
|
| | 0%
|
|================== | 25%
|
|=================================== | 50%
|
|==================================================== | 75%
|
|======================================================================| 100%
Merging data tables...
|
| | 0%
|
|======================= | 33%
|
|=============================================== | 67%
|
|======================================================================| 100%
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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