Package 'InterCellar'

Title: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics
Description: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions.
Authors: Marta Interlandi [cre, aut]
Maintainer: Marta Interlandi <[email protected]>
License: MIT + file LICENSE
Version: 2.13.0
Built: 2024-10-31 06:09:16 UTC
Source: https://github.com/bioc/InterCellar

Help Index

Perform GO annotation of input data

Description

Perform GO annotation of input data

Usage

annotateGO(
  input_select_ensembl,
  input_go_evidence_exclude,
  input_go_sources_checkbox,
  input.data
)

Arguments

input_select_ensembl

ensembl version selected by user
input_go_evidence_exclude

evidence codes to exclude by user
input_go_sources_checkbox

GO sources to use by user
input.data

preprocessed input data

Value

GO_annotation

Annotate pathways for input data

Description

Annotate pathways for input data

Usage

annotatePathways(selected.db, input.data)

Arguments

selected.db

pathways sources to use
input.data

filtered input data

Value

pathways_annotation

Build binary matrix with int-pairs in rows, functions in cols

Description

Build binary matrix with int-pairs in rows, functions in cols

Usage

buildPairsbyFunctionMatrix(functions_df)

Arguments

functions_df

annotated df (GO/path/combined)

Value

binary matrix

Manually change the annotation of L-L and R-R pairs

Description

Manually change the annotation of L-L and R-R pairs

Usage

checkLL_RR(input.data)

Arguments

input.data

preprocessed table

Value

input.data

Examples

data(input.data)
checked.input.data <- checkLL_RR(input.data)

Plot circle plot

Description

Plot circle plot

Usage

circlePlot(data, cluster_colors, ipm_color, int_flow, link.color)

Arguments

data

subset of input data by flow / intpair module
cluster_colors

global
ipm_color

single color for chosen int-pair module
int_flow

string specifying the flow
link.color

string specifying variable by which to color links

Value

circle plot

Combine GO annotation and pathways in a unique object

Description

Combine GO annotation and pathways in a unique object

Usage

combineAnnotations(GO_annotation, pathways_annotation)

Arguments

GO_annotation

data
pathways_annotation

data

Value

combined annotation dataframe

Create Barplot cluster-verse

Description

Create Barplot cluster-verse

Usage

createBarPlot_CV(
  barplotDF,
  input_cluster_selected_checkbox,
  input_num_or_weight_bar1
)

Arguments

barplotDF

dataframe with N interactions per cluster (auto/para)
input_cluster_selected_checkbox

checkbox input
input_num_or_weight_bar1

number of int or weighted number by score

Value

plotly barplot

Create ggplot barplot to be saved in tiff

Description

Create ggplot barplot to be saved in tiff

Usage

createBarPlot1_ggplot(
  barplotDF,
  input_cluster_selected_checkbox,
  input_num_or_weight_bar1
)

Arguments

barplotDF

dataframe with N interactions per cluster (auto/para)
input_cluster_selected_checkbox

checkbox input
input_num_or_weight_bar1

number of int or weighted number by score

Value

ggplot barplot

Create barplot of number of interaction for selected cluster

Description

Create barplot of number of interaction for selected cluster

Usage

createBarPlot2_CV(
  barplotDF2,
  input_cluster_selected_checkbox,
  input_clust_barplot2
)

Arguments

barplotDF2

dataframe with barplot data
input_cluster_selected_checkbox

selected clusters to keep
input_clust_barplot2

selected cluster to plot

Value

plotly fig

Create ggplot barplot of Nint per cluster selected

Description

Create ggplot barplot of Nint per cluster selected

Usage

createBarPlot2_ggplot(
  barplotDF2,
  input_cluster_selected_checkbox,
  input_clust_barplot2
)

Arguments

barplotDF2

dataframe with barplot data
input_cluster_selected_checkbox

selected clusters to keep
input_clust_barplot2

selected cluster to plot

Value

ggplot barplot

Create Network of clusters

Description

Create Network of clusters

Usage

createNetwork(data.filt.cluster, input_num_or_weight_radio, input_edge_weight)

Arguments

data.filt.cluster

filtered input data (by clusters)
input_num_or_weight_radio

either number of interactions or weighted by score
input_edge_weight

small,medium or large from user input

Value

list containing nodes and edges for network

Get dendrogram of int pair modules

Description

Get dendrogram of int pair modules

Usage

dendroIntPairModules(pairs_func_matrix)

Arguments

pairs_func_matrix

binary matrix pairs x functions

Value

list with dendrogram, hclust and umap

Determine the elbow point on a curve (from package akmedoids)

Description

Given a list of x, y coordinates on a curve, function determines the elbow point of the curve.

Usage

elbowPoint(x, y)

Arguments

x

vector of x coordinates of points on the curve
y

vector of y coordinates of points on the curve

Details

highlight the maximum curvature to identify the elbow point (credit: 'github.com/agentlans')

Value

an x, y coordinates of the elbow point.

Get html link to ensembl

Description

Get html link to ensembl

Usage

ensemblLink(ensembl)

Arguments

ensembl

symbol

Value

html link to website

Get back-to-back barplot for 2 conditions comparison

Description

Get back-to-back barplot for 2 conditions comparison

Usage

getBack2BackBarplot(tab_c1, tab_c2, lab_c1, lab_c2)

Arguments

tab_c1

barplot dataframe generated by getBarplotDF() for condition 1
tab_c2

barplot dataframe generated by getBarplotDF() for condition 1
lab_c1

label for condition 1
lab_c2

label for condition 2

Value

ggplot object

Get dataframe for plotting barplot (all clusters)

Description

Get dataframe for plotting barplot (all clusters)

Usage

getBarplotDF(
  data.filt.bar,
  input_cluster_selected_checkbox,
  input_num_or_weight_bar1
)

Arguments

data.filt.bar

filtered object (checkbox auto/para)
input_cluster_selected_checkbox

checkbox input
input_num_or_weight_bar1

number of int or weighted number by score

Value

dataframe with number of interactions per cluster auto/para

Get dataframe for barplot (by cluster)

Description

Get dataframe for barplot (by cluster)

Usage

getBarplotDF2(filt.data, input_cluster_selected_checkbox, input_clust_barplot2)

Arguments

filt.data

input data filtered in cluster-verse
input_cluster_selected_checkbox

selected clusters to keep
input_clust_barplot2

selected cluster to plot

Value

dataframe with num int per cluster

Get cluster names only from sender cluster A

Description

Get cluster names only from sender cluster A

Usage

getClusterA_Names(input.data)

Arguments

input.data

preprocessed input data

Value

named list of clusters

Get colors for clusters

Description

Get colors for clusters

Usage

getClusterColors(input.data)

Arguments

input.data

preprocessed input data

Value

named vector with colors per cluster

Get clusters names from initial input data

Description

Get clusters names from initial input data

Usage

getClusterNames(input.data)

Arguments

input.data

preprocessed input data

Value

named list of clusters

Examples

data(input.data)
cluster_list <- getClusterNames(input.data)

Creating edges dataframe for network of clusters

Description

Creating edges dataframe for network of clusters

Usage

getClusterNetwork(input.data, input_num_or_weight_radio, input_edge_weight)

Arguments

input.data

preprocessed input data
input_num_or_weight_radio

either num of interactions or weighted by score
input_edge_weight

small,medium or large from user input

Value

edges dataframe

Get Clusters size

Description

Get Clusters size

Usage

getClusterSize(cl, edges.df, input_num_or_weight_radio)

Arguments

cl

cluster name
edges.df

dataframe with edges for network
input_num_or_weight_radio

either num of interactions or weighted by score

Value

sum of n interactions or weighted num for that cluster

Get table of unique int-pairs/clust-pairs couplets

Description

Get table of unique int-pairs/clust-pairs couplets

Usage

getDistinctCouplets(
  data_cond1,
  data_cond2,
  data_cond3 = NULL,
  lab_c1,
  lab_c2,
  lab_c3 = NULL
)

Arguments

data_cond1

filt.data() corresponding to chosen condition 1
data_cond2

filt.data() corresponding to chosen condition 2
data_cond3

filt.data() corresponding to chosen condition 3
lab_c1

data label for condition 1
lab_c2

data label for condition 2
lab_c3

data label for condition 3

Value

modified filt.data containing only unique couplets

Functions to plot DotPlots

Description

Functions to plot DotPlots

Usage

getDotPlot_selInt(
  selected_tab,
  clust.order,
  low_color = "aquamarine",
  high_color = "#131780"
)

Arguments

selected_tab

selected rows of filt.data by selection from gene table
clust.order

how to order clusters
low_color

of dotplot
high_color

of dotplot

Value

list with modified selected data and ggplot2 dotplot

Get table for gene-verse

Description

Get table for gene-verse

Usage

getGeneTable(input.data)

Arguments

input.data

preprocessed input data

Value

gene table with unique intpairs (no connection to clusters)

Examples

data(input.data)
gene_table <- getGeneTable(input.data)

Connection to Ensembl via biomaRt to get GO terms

Description

Connection to Ensembl via biomaRt to get GO terms

Usage

getGObiomaRt(input_select_ensembl, input.data)

Arguments

input_select_ensembl

chosen version of Ensembl
input.data

filtered input data

Value

dataframe with GO annotation

Subfunction to calculate significant functions by permutation test

Description

Subfunction to calculate significant functions by permutation test

Usage

getHitsf(mat, gpModules_assign)

Arguments

mat

binary matrix of functional terms by int-pairs
gpModules_assign

assignment of intpairs to modules

Value

matrix with hits

Example

Get subset of interactions corresponding to a certain viewpoint and flow

Description

Get subset of interactions corresponding to a certain viewpoint and flow

Usage

getIntFlow(vp, input.data, flow)

Arguments

vp

viewpoint cluster
input.data

preprocessed/filtered input data
flow

one among directed_out, directed_in or undirected

Value

subset of data

Examples

data(input.data)
caf_out <- getIntFlow(vp = "CAF", input.data, flow = "directed_out")

Calculate number of terms of a database

Description

Calculate number of terms of a database

Usage

getNtermsBYdb(annotation)

Arguments

annotation

data from either pathways, GO or combined

Value

number of terms by dataset

Get number of unique ligands and receptors

Description

Get number of unique ligands and receptors

Usage

getNumLR(gene.table, type)

Arguments

gene.table

gene table of unique int-pairs
type

either L or R

Value

number of L or R genes

Get Pie Chart of unique couplets

Description

Get Pie Chart of unique couplets

Usage

getPieChart(data_dotplot)

Arguments

data_dotplot

same data used to generate dotplot

Value

pie chart

#' Get radar plot of relative numbers of interactions for a certain cell type #' #' @param tab_c1 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 1 #' @param tab_c2 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 2 #' @param tab_c3 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 3 #' @param lab_c1 label for condition 1 #' @param lab_c2 label for condition 2 #' @param lab_c3 label for condition 3 #' @param cell_name label of cell type of interest #' #' @return plot #' @importFrom fmsb radarchart #' @importFrom data.table transpose getRadarPlot <- function(tab_c1, tab_c2, tab_c3, lab_c1, lab_c2, lab_c3, cell_name) if(is.null(tab_c3)) df <- merge(tab_c1, tab_c2, by = "Clusters", all = TRUE) colnames(df) <- c("Clusters", "nint_c1", "nint_c2") else df <- merge(tab_c1, tab_c2, by = "Clusters", all = TRUE) df <- merge(df, tab_c3, by = "Clusters", all = TRUE) colnames(df) <- c("Clusters", "nint_c1", "nint_c2", "nint_c3") df[is.na(df)] <- 0 cluster_names <- df$Clusters # add max and min max_nint <- max(df[, -1]) df <- add_column(df, max_nint, .after = "Clusters") df <- add_column(df, "min_nint" = 0, .after = "max_nint") radar_df <- data.table::transpose(df[, -1]) if(is.null(lab_c3)) rownames(radar_df) <- c("max", "min", lab_c1, lab_c2) else rownames(radar_df) <- c("max", "min", lab_c1, lab_c2, lab_c3) colnames(radar_df) <- cluster_names color <- c("#438ECC", "#E97778", "#00BA38") fmsb::radarchart( radar_df, axistype = 1, # Customize the polygon pcol = color, pfcol = scales::alpha(color, 0.5), plwd = 2, plty = 1, # Customize the grid cglcol = "grey", cglty = 1, cglwd = 0.8, # Customize the axis axislabcol = "grey30", # Variable labels vlcex = 1.2, vlabels = colnames(radar_df), caxislabels = round(seq(from = 0, to = radar_df["max",1], length.out = 5)), title = cell_name ) legend( x = "bottomleft", legend = rownames(radar_df[-c(1,2),]), horiz = FALSE, bty = "n", pch = 20 , col = color, text.col = "black", cex = 1, pt.cex = 1.5 ) Get radar df of relative numbers of interactions for a certain cell type

Description

#' Get radar plot of relative numbers of interactions for a certain cell type #' #' @param tab_c1 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 1 #' @param tab_c2 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 2 #' @param tab_c3 barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 3 #' @param lab_c1 label for condition 1 #' @param lab_c2 label for condition 2 #' @param lab_c3 label for condition 3 #' @param cell_name label of cell type of interest #' #' @return plot #' @importFrom fmsb radarchart #' @importFrom data.table transpose getRadarPlot <- function(tab_c1, tab_c2, tab_c3, lab_c1, lab_c2, lab_c3, cell_name) if(is.null(tab_c3)) df <- merge(tab_c1, tab_c2, by = "Clusters", all = TRUE) colnames(df) <- c("Clusters", "nint_c1", "nint_c2") else df <- merge(tab_c1, tab_c2, by = "Clusters", all = TRUE) df <- merge(df, tab_c3, by = "Clusters", all = TRUE) colnames(df) <- c("Clusters", "nint_c1", "nint_c2", "nint_c3")

df[is.na(df)] <- 0

cluster_names <- df$Clusters # add max and min max_nint <- max(df[, -1]) df <- add_column(df, max_nint, .after = "Clusters") df <- add_column(df, "min_nint" = 0, .after = "max_nint")

radar_df <- data.table::transpose(df[, -1])

if(is.null(lab_c3)) rownames(radar_df) <- c("max", "min", lab_c1, lab_c2) else rownames(radar_df) <- c("max", "min", lab_c1, lab_c2, lab_c3)

colnames(radar_df) <- cluster_names

color <- c("#438ECC", "#E97778", "#00BA38")

fmsb::radarchart( radar_df, axistype = 1, # Customize the polygon pcol = color, pfcol = scales::alpha(color, 0.5), plwd = 2, plty = 1, # Customize the grid cglcol = "grey", cglty = 1, cglwd = 0.8, # Customize the axis axislabcol = "grey30", # Variable labels vlcex = 1.2, vlabels = colnames(radar_df), caxislabels = round(seq(from = 0, to = radar_df["max",1], length.out = 5)), title = cell_name ) legend( x = "bottomleft", legend = rownames(radar_df[-c(1,2),]), horiz = FALSE, bty = "n", pch = 20 , col = color, text.col = "black", cex = 1, pt.cex = 1.5 )

Get radar df of relative numbers of interactions for a certain cell type

Usage

getRadar_df(tab_c1, tab_c2, tab_c3, lab_c1, lab_c2, lab_c3)

Arguments

tab_c1

barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 1
tab_c2

barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 2
tab_c3

barplot dataframe from Viewpoint generated by getBarplotDF2() containing data for condition 3
lab_c1

label for condition 1
lab_c2

label for condition 2
lab_c3

label for condition 3

Value

df to be then used with fmsb radarchart

Get table with ranked functional terms

Description

Get table with ranked functional terms

Usage

getRankedTerms(data.fun.annot)

Arguments

data.fun.annot

annotated df (GO/path/combined)

Value

table with ranking

Wrapper for other functions to get significant table of func terms

Description

Wrapper for other functions to get significant table of func terms

Usage

getSignif_table(
  data_cond1,
  data_cond2,
  data_cond3,
  lab_c1,
  lab_c2,
  lab_c3,
  annot_cond1,
  annot_cond2,
  annot_cond3
)

Arguments

data_cond1

filt.data() corresponding to chosen condition 1
data_cond2

filt.data() corresponding to chosen condition 2
data_cond3

filt.data() corresponding to chosen condition 3
lab_c1

data label for condition 1
lab_c2

data label for condition 2
lab_c3

data label for condition 3
annot_cond1

binary matrix int-pair by functions for cond1
annot_cond2

binary matrix int-pair by functions for cond2
annot_cond3

binary matrix int-pair by functions for cond3

Value

list containing pvalue_df and unique_intpairs df

Calculate significant function per intpair module

Description

Calculate significant function per intpair module

Usage

getSignificantFunctions(
  subGenePairs_func_mat,
  gpModules_assign,
  rank.terms,
  input_maxPval
)

Arguments

subGenePairs_func_mat

subset of binary mat
gpModules_assign

assignment of intpairs to modules
rank.terms

table of ranked functions
input_maxPval

threshold of significance

Value

table with significant functions

Get significance of functional terms related to unique int-pairs per condition

Description

Get significance of functional terms related to unique int-pairs per condition

Usage

getSignificantFunctions_multiCond(sub_annot, unique_intpairs)

Arguments

sub_annot

annotation matrix subset to unique int-pairs
unique_intpairs

data.frame with unique int-pairs by condition

Value

data.frame with calculated pvalue of significance

Get Sunburst plot of selected functional terms

Description

Get Sunburst plot of selected functional terms

Usage

getSunburst(
  sel.data,
  func_selected,
  int_p_fun,
  cluster.colors,
  input_num_or_weight_radio
)

Arguments

sel.data

dataframe of selected functions
func_selected

the selected functional term
int_p_fun

dataframe with int pairs annotated to this function
cluster.colors

for plotting
input_num_or_weight_radio

either num of interactions or weighted by score

Value

plotly figure

Get UMAP for IP modules

Description

Get UMAP for IP modules

Usage

getUMAPipModules(intPairs.dendro, gpModules_assign, ipm_colors)

Arguments

intPairs.dendro

list output of dendrogram
gpModules_assign

named vector of module assignment
ipm_colors

for intpair modules

Value

plotly umap

Plot dotplot containing only unique int-pair/cluster pairs with many conditions

Description

Plot dotplot containing only unique int-pair/cluster pairs with many conditions

Usage

getUniqueDotplot(data_dotplot, clust.order)

Arguments

data_dotplot

table with selected int_pairs for multiple conditions
clust.order

how to order clusters

Value

ggplot object

Get table of unique int-pairs by condition

Description

Get table of unique int-pairs by condition

Usage

getUniqueIntpairs_byCond(
  data_cond1,
  data_cond2,
  data_cond3 = NULL,
  lab_c1,
  lab_c2,
  lab_c3 = NULL
)

Arguments

data_cond1

filt.data() corresponding to chosen condition 1
data_cond2

filt.data() corresponding to chosen condition 2
data_cond3

filt.data() corresponding to chosen condition 3
lab_c1

data label for condition 1
lab_c2

data label for condition 2
lab_c3

data label for condition 3

Value

modified merged filt.data containing only unique intpairs

Get GO link

Description

Get GO link

Usage

goLink(go_id)

Arguments

go_id

string

Value

html link to website

Input Data example

Description

A dataset obtained from Tirosh et al melanoma dataset, running CellPhoneDBv2. This data is generated by InterCellar running read.CPDBv2()

Usage

input.data

Format

A data frame with 5638 rows and 11 variables:

int_pair

interaction pair name, geneA & geneB

geneA

name, hgnc_symbol

geneB

name, hgnc_symbol

typeA

molecular type of geneA, either L (ligand) or R (receptor)

typeB

molecular type of geneB, either L (ligand) or R (receptor)

clustA

name of first cluster, either character or number

clustB

name of second cluster, either character or number

score

int-pair score as avg expression of geneA and geneB over clustA and clustB, decimal

p_value

int-pair pvalue, decimal

annotation_strategy

database from which the int-pair was retrieved

int.type

either autocrine or paracrine

Read dataframe of cell-cell communication from CellChat (ligand/receptor)

Description

Read dataframe of cell-cell communication from CellChat (ligand/receptor)

Usage

read.cellchat(file_tab)

Arguments

file_tab

dataframe from cellchat

Value

input.data formatted for InterCellar

Read output from CellPhoneDB v2.

Description

Output is a folder containing 4 .txt files - deconvoluted.txt: containing list of single genes and their mean expression in each cluster (not considered); - means.txt: containing list of interacting pairs with info regarding L/R, annotation strategy and mean value of all pairs over cluster couples. - pvalues.txt: same as means, but containing pvalue of each pair, for each cluster couple. - significant_means.txt: only means of those pairs that have pvalue < 0.05. Has one more column:rank. If the statistical analysis is not run, the folder would contain only deconvoluted and means

Usage

read.CPDBv2(folder)

Arguments

folder

folder containing output

Value

input.data which is the pre-processed object with annotated L-R pairs

Read custom input file and re-structure it with InterCellar format

Description

Read custom input file and re-structure it with InterCellar format

Usage

read.customInput(tab, separator)

Arguments

tab

custom input table
separator

character that separates two elements of an interaction pair

Value

preprocessed table

Read ICELLNET dataframe

Description

Read ICELLNET dataframe

Usage

read.icellnet(tab, input_icellnet_CC, input_icellnet_dir)

Arguments

tab

dataframe with int-pairs in "X" column, other columns as cell types
input_icellnet_CC

central cell name
input_icellnet_dir

direction of interaction either out or in

Value

pre-processed input data

Read output from SingleCellSignalR

Description

SCSR description: the output folder is a collection of txt files, one for each clusters pair considered. The "paracrine" option looks for ligands expressed in cluster A and their associated receptors according to LRdb that are expressed in any other cluster but A. These interactions are labelled "paracrine". The interactions that involve a ligand and a receptor, both differentially expressed in their respective cell clusters according to the **edgeR** analysis performed by the **cluster_analysis()** function, are labelled "specific". The "autocrine" option searches for ligands expressed in cell cluster A and their associated receptors also expressed in A. These interactions are labelled "autocrine". Additionally, it searches for those associated receptors in the other cell clusters (not A) to cover the part of the signaling that is "autocrine" and "paracrine" simultaneously. These interactions are labelled "autocrine/paracrine". This file is a 4-column table: ligands, receptors, interaction types ("paracrine", "autocrine", "autocrine/paracrine" and "specific"), and the associated LRscore. InterCellar: rename autocrine|paracrine to paracrine

Usage

read.SCsignalR(folder)

Arguments

folder

containing output from SingleCellSignalR, named cell-signaling

Value

input.data: preprocessed object with annotated L-R pairs

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(reproducible = TRUE)

Arguments

reproducible

boolean for setting a seed, making plots reproducible

Value

a running instance of InterCellar

Examples

## Not run: 
run_app()

## End(Not run)

Subset int-pair by function matrices to unique int-pairs by condition

Description

Subset int-pair by function matrices to unique int-pairs by condition

Usage

subsetAnnot_multiCond(
  annot_cond1,
  annot_cond2,
  annot_cond3,
  unique_intpairs,
  lab_c1,
  lab_c2,
  lab_c3
)

Arguments

annot_cond1

binary matrix int-pair by functions for cond1
annot_cond2

binary matrix int-pair by functions for cond2
annot_cond3

binary matrix int-pair by functions for cond3
unique_intpairs

table of unique int-pairs by condition
lab_c1

label cond1
lab_c2

label cond2
lab_c3

label cond3

Value

subset merged matrix

Subset pairs-function matrix by selected flow

Description

Subset pairs-function matrix by selected flow

Usage

subsetFuncMatBYFlow(pairs_func_matrix, flow_df)

Arguments

pairs_func_matrix

binary
flow_df

subset of input data by flow

Value

subset of binary mat

Swaps interaction pairs that are R-L to L-R

Description

Swaps interaction pairs that are R-L to L-R

Usage

swap.RLint(RLint)

Arguments

RLint

subset of R-L interactions

Value

input data with ordered L-R pairs and L-L/R-R

Get html link to uniprot

Description

Get html link to uniprot

Usage

uniprotLink(uniprot)

Arguments

uniprot

symbol

Value

html link to website

Function that orders all interaction pairs as L-R. Leaves unchanged the R-R and L-L

Description

Function that orders all interaction pairs as L-R. Leaves unchanged the R-R and L-L

Usage

updateInputLR(input.data)

Arguments

input.data

uploaded data

Value

ordered input data