| Title: | Analysis of Graph-Structured Data with a Focus on Protein-Protein Interaction Networks |
| Version: | 1.0.5 |
| Description: | Provides a general toolkit for drug target identification. We include functionality to reduce large graphs to subgraphs and prioritize nodes. In addition to being optimized for use with generic graphs, we also provides support to analyze protein-protein interactions networks from online repositories. For more details on core method, refer to Weaver et al. (2021) https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008755. |
| License: | GPL (≥ 3) |
| Imports: | rlang, magrittr, withr, readr, dplyr, stringr, tidyr, tibble, igraph (≥ 1.2.0), Matrix, ensembldb, foreach, doParallel, Rcpp, iterators, ggplot2, STRINGdb |
| LinkingTo: | Rcpp |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | tidygraph, ggraph, testthat (≥ 2.0.0), knitr, EnsDb.Hsapiens.v86, rmarkdown, here |
| Config/testthat/edition: | 2 |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | yes |
| Packaged: | 2024-05-03 14:08:32 UTC; dtw43 |
| Author: | Davis Weaver [aut, cre] (orcid: 0000-0003-3086-497X) |
| Maintainer: | Davis Weaver <davis.weaver@case.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-05-17 11:40:09 UTC |
attach expression values from user-provided expression vector to graph.
Description
attach expression values from user-provided expression vector to graph.
Usage
add_expression(exp, g)
Arguments
exp |
expression vector - assumed to be a named vector where the values are expression and the names are the gene name |
g |
igraph object - will be filtered so that only nodes found in both exp and g are kept |
Value
subgraph of g containing only shared keys with exp and with expression attached.
Attach a generic user-provided value to graph
Description
Attach a generic user-provided value to graph
Usage
add_value(val, g, val_name = "value")
Arguments
val |
named numeric vector where the names correspond to vertices in g |
g |
igraph object - will be filtered so that only nodes found in both exp and g are kept |
val_name |
str key for val |
Value
subgraph of g containing only shared keys with val and val attached
Convert from most other representations of gene name to gene.symbol
Description
Convert from most other representations of gene name to gene.symbol
Usage
as_gene_symbol(x, edb = NULL)
Arguments
x |
vector of ensemble.gene ids, ensemble.peptide ids, ensemble.transcript ids or entrez gene ids |
edb |
ensemble database object |
Value
vector of gene symbols
Examples
#1) from numeric formatted entrez id
as_gene_symbol(1956)
#2) from character formatted entrez id
as_gene_symbol("1956")
#3) from ensemble gene id
as_gene_symbol("ENSG00000146648")
#4) From a vector of entrez ids
as_gene_symbol(c("123", "1956", "2012"))
Bootstrap null distribution for RWR
Description
This function will generate a bootstrapped null distribution to identify signficant vertices in a PPI given a set of user-defined seed proteins. Bootstrapping is done by performing random walk with repeats repeatedly over "random" sets of seed proteins. Degree distribution of user-provided seeds is used to inform sampling.
Usage
bootstrap_null(
seed_proteins,
g,
n = 1000,
agg_int = 100,
gamma = 0.6,
eps = 1e-10,
tmax = 1000,
norm = TRUE,
set_seed = NULL,
cache = NULL,
seed_name = NULL,
ncores = 1
)
Arguments
seed_proteins |
user defined seed proteins |
g |
igraph object |
n |
number of random walks with repeats to create null distribution |
agg_int |
number of runs before we need to aggregate the results - necessary to save memory. set at lower numbers to save even more memory. |
gamma |
restart probability |
eps |
maximum allowed difference between the computed probabilities at the steady state |
tmax |
the maximum number of iterations for the RWR |
norm |
if True, w is normalized by dividing each value by the column sum. |
set_seed |
integer to set random number seed - for reproducibility |
cache |
A filepath to a folder downloaded files should be stored |
seed_name |
Name to give the cached ngull distribution - must be a character string |
ncores |
Number of cores to use - defaults to 1. Significant speedup can be achieved by using multiple cores for computation. |
Value
data frame containing mean/ standard deviation for null distribution
Examples
#g <- prep_biogrid()
#bootstrap_null(seed_proteins = c("EGFR", "KRAS"), g= g, ncores = 1, n = 10)
helper function to calculate dnp for one sample
Description
helper function to calculate dnp for one sample
Usage
calc_dnp_i(df, g, v_rm = NULL, keep_all = TRUE)
Arguments
df |
dataframe with one cell line + log expression |
g |
igraph object containing ppi info |
v_rm |
passed to |
keep_all |
logical flag denoting if we should keep genes that we didn't calculate dnp for |
Value
same dataframe with dnp calculated for each gene.
calculate network potential for one node.
Description
calculate network potential for one node.
Usage
calc_np(c_i, c_j)
Arguments
c_i |
expression for a given node. |
c_j |
vector of expressions for each neighbor of c_i |
function to calculate the network potential for each protein in a user-provided vector - cpp internal version
Description
function to calculate the network potential for each protein in a user-provided vector - cpp internal version
Usage
calc_np_all(exp, g, v = "default", neighbors = NULL)
Arguments
exp |
expression vector - assumed to be a named vector where the values are expression and the names are the gene name |
g |
igraph object - will be filtered so that only nodes found in both exp and g are kept |
v |
character vector of nodes over which to calculate network potential. |
neighbors |
named list containing the neighbors for each node of graph g. If not provided, it will be computed |
Value
dataframe containing network potential for each of the inputed gene names.
function to calculate the network potential for each protein in a user-provided vector
Description
Mostly just used to help debug the CPP version - not exported
Usage
calc_np_all_legacy(
exp,
g,
v = as.character(names(igraph::V(g))),
neighbors = NULL
)
Arguments
exp |
expression vector - assumed to be a named vector where the values are expression and the names are the gene name |
g |
igraph object - will be filtered so that only nodes found in both exp and g are kept |
v |
character vector of nodes over which to calculate network potential. |
neighbors |
named list containing the neighbors for each node of graph g. If not provided, it will be computed |
Value
dataframe containing network potential for each of the inputed gene names.
helper function to calculate np for one sample
Description
helper function to calculate np for one sample
Usage
calc_np_i(df, g)
Arguments
df |
dataframe with one cell line + log expression |
g |
igraph object containing ppi info |
Value
same dataframe with np calculated for each gene.
Check to make sure incoming object is a valid crosstalk df.
Description
This function is a helper function for plot_ct that verifies the input is a valid output of compute_crosstalk
Usage
check_crosstalk(crosstalk_df)
Arguments
crosstalk_df |
a dataframe containing the results of |
Value
message if not correct object type, null otherwise
.combine function for compute_null foreach looping structure
Description
.combine function for compute_null foreach looping structure
Usage
combine_null(x)
Arguments
x |
aggregated data structure |
Value
data.frame
Identify proteins with a statistically significant relationship to user-provided seeds.
Description
compute_crosstalk returns a dataframe of proteins that are significantly
associated with user-defined seed proteins. These identified "crosstalkers"
can be combined with the user-defined seed proteins to identify functionally
relevant subnetworks. Affinity scores for every protein in the network are
calculated using a random-walk with repeats (sparseRWR). Significance is
determined by comparing these affinity scores to a bootstrapped null distribution
(see bootstrap_null). If using non-human PPI from string, refer to the stringdb documentation
for how to specify proteins
Usage
compute_crosstalk(
seed_proteins,
g = NULL,
use_ppi = TRUE,
ppi = "stringdb",
species = "homo sapiens",
n = 1000,
union = FALSE,
intersection = FALSE,
gamma = 0.6,
eps = 1e-10,
tmax = 1000,
norm = TRUE,
set_seed,
cache = NULL,
min_score = 700,
seed_name = NULL,
ncores = 1,
significance_level = 0.95,
p_adjust = "bonferroni",
agg_int = 100,
return_g = FALSE
)
Arguments
seed_proteins |
user defined seed proteins |
g |
igraph network object. |
use_ppi |
bool, should g be a protein-protein interaction network? If
false, user must provide an igraph object in |
ppi |
character string describing the ppi to use: currently only "stringdb" and "biogrid" are supported. |
species |
character string describing the species of interest.
For a list of supported species, see |
n |
number of random walks with repeats to create null distribution |
union |
bool, should we take the union of string db and biogrid to compute the PPI? Only applicable for the human PPI |
intersection |
bool, should we take the intersection of string db and biogrid to compute the PPI? Only applicable for the human PPI |
gamma |
restart probability |
eps |
maximum allowed difference between the computed probabilities at the steady state |
tmax |
the maximum number of iterations for the RWR |
norm |
if True, w is normalized by dividing each value by the column sum. |
set_seed |
integer to set random number seed - for reproducibility |
cache |
A filepath to a folder downloaded files should be stored |
min_score |
minimum connectivity score for each edge in the network. |
seed_name |
Name to give the cached ngull distribution - must be a character string |
ncores |
Number of cores to use - defaults to 1. Significant speedup can be achieved by using multiple cores for computation. |
significance_level |
user-defined signficance level for hypothesis testing |
p_adjust |
adjustment method to correct for multiple hypothesis testing:
defaults to "holm". see |
agg_int |
number of runs before we need to aggregate the results - necessary to save memory. set at lower numbers to save even more memory. |
return_g |
bool, should we return the graph used? mostly for internal use |
Value
data frame containing affinity score, p-value, for all "crosstalkers" related to a given set of seeds
Examples
#1) easy to use for querying biological networks - n = 10000 is more appropriate for actual analyses
#compute_crosstalk(c("EGFR", "KRAS"), n =10)
#2) Also works for any other kind of graph- just specify g (must be igraph formatted as of now)
g <- igraph::sample_gnp(n = 1000, p = 10/1000)
compute_crosstalk(c(1,3,5,8,10), g = g, use_ppi = FALSE, n = 100)
main function to compute delta np for every gene in a given dataframe - assumes compute_np has already been run for a given dataset
Description
This function takes a tidy dataframe as input containing RNA sequencing data for one or more samples and conducts in-silico repression. Make sure to run with the same arguments for ppi and cache to maintain consistency for a given pipeline.
Usage
compute_dnp(
cache = NULL,
df,
experiment_name,
ppi,
ncores = 1,
min_score = NULL
)
Arguments
cache |
user-provided filepath for where to store data etc |
df |
dataframe output of compute_np |
experiment_name |
name of the experiment for saving output. |
ppi |
should we use biogrid or stringdb for the PPI |
ncores |
number of cores to use for calculations |
min_score |
if ppi is stringdb, which mininum score should we use to filter edges? |
Value
data.frame
main function to compute np from a user-provided expression matrix.
Description
main function to compute np from a user-provided expression matrix.
Usage
compute_np(
cache = NULL,
experiment_name,
ppi = "biogrid",
min_score = NULL,
exp_mat,
mir_paper = TRUE,
ncores = 1
)
Arguments
cache |
user-provided filepath for where to store data etc |
experiment_name |
name of the experiment for saving output. |
ppi |
should we use biogrid or stringdb for the PPI |
min_score |
if ppi is stringdb, which mininum score should we use to filter edges? |
exp_mat |
expression matrix where columns are samples and rows are features |
mir_paper |
are we running this in the context of the mir paper? a few quirks of that data |
ncores |
number of cores to use for calculations |
Value
tidy data frame with one column for expression and another for np
function to compute null distribution of dnp
Description
compute_null_dnp calculates a null distribution for the change in network potential for
for each node in a cell signaling network.
Usage
compute_null_dnp(
cache = NULL,
df,
ppi = "biogrid",
n,
n_genes = 50,
experiment_name,
ncores = 4,
min_score = NULL
)
Arguments
cache |
user-provided filepath for where to store data etc |
df |
output of |
ppi |
should we use biogrid or stringdb for the PPI |
n |
number of permutations |
n_genes |
integer describing number of genes per sample that we will compute the null distribution for |
experiment_name |
name of the experiment for saving output. |
ncores |
number of cores to use for calculations |
min_score |
if ppi is stringdb, which mininum score should we use to filter edges? |
Details
The input for this function will be the output of compute_dnp().
To compute the null distribution, the nodes in the provided cell signaling
network will be randomly permuted n times, with dnp computed or each new
cell signaling network. The mean and standard error of dnp for this set of random
networks will constitute the null model that we will use for comparison.
Be warned that this operation is extremely expensive computationally. It is
recommended to either use a high-performance cluster or limit the computation of the
null distribution to a small number of nodes.
To distribute the workload over multiple cores, just specify ncores.
Value
df, also saves to cache if specified
See Also
compute_dnp() and compute_np()
Helper function to generate subgraph from crosstalk_df output of compute_crosstalk
Description
Useful if the user wants to carry out further analysis or design custom visualizations.
Usage
crosstalk_subgraph(crosstalk_df, g, seed_proteins, tg = TRUE)
Arguments
crosstalk_df |
a dataframe containing the results of |
g |
igraph network object. |
seed_proteins |
user defined seed proteins |
tg |
bool do we want to tidy the graph for plotting? |
Value
a tidygraph structure containing information about the crosstalkr subgraph
Examples
## Not run:
ct_df <- compute_crosstalk(c("EGFR", "KRAS"))
g <- prep_biogrid()
crosstalk_subgraph(ct_df, g = g, seed_proteins = c("EGFR", "KRAS"))
## End(Not run)
crosstalkr: A package for the identification of functionally relevant subnetworks from high-dimensional omics data.
Description
crosstalkr provides a key user function, compute_crosstalk as well
as several additional functions that assist in setup and visualization (under development).
crosstalkr functions
compute_crosstalk calculates affinity scores of all proteins in a network
relative to user-provided seed proteins. Users can use the human interactome or
provide a network represented as an igraph object.
sparseRWR performs random walk with restarts on a sparse matrix.
Compared to dense matrix implementations, this should be extremely fast.
bootstrap_null Generates a null distribution based on n calls to
sparseRWR
setup_init manages download and storage of interactome data to
speed up future analysis
plot_ct allows users to visualize the subnetwork identified in
compute_crosstalk. This function relies on the ggraph framework.
Users are encouraged to use ggraph or other network visualization packages for
more customized figures.
crosstalk_subgraph converts the output of compute_crosstalk to a
tidygraph object containing only the identified nodes and their connections to the
user-provided seed_proteins. This function also adds degree, degree_rank, and
seed_label as attributes to the identified subgraph to assist in plotting.
Determine which format of gene is used to specify by user-defined seed proteins
Description
Determine which format of gene is used to specify by user-defined seed proteins
Usage
detect_inputtype(x)
Arguments
x |
vector of gene symbols |
Value
"gene_symbol", "entrez_id", "ensemble_id" or "other"
Internal function that computes the mean/stdev for each gene from a wide-format data frame.
Description
This function is called by the high-level function "bootstrap_null". Not expected to be used by end-users - we only export it so that environments inside foreach loops can find it.
Usage
dist_calc(df, seed_proteins)
Arguments
df |
: numeric vector |
seed_proteins |
user defined seed proteins |
Value
data.frame containing summary statistics for the computed null distribution
Determine if ensembl id is a Protein, gene, or transcript_id
Description
Determine if ensembl id is a Protein, gene, or transcript_id
Usage
ensembl_type(x)
Arguments
x |
vector or single gene symbol |
Value
character: "PROTEINID", "GENEID", "TRANSCRIPTID"
helper function to split experiment names into constituent parts
Description
this is highly specific to the miR paper
Usage
experiment_breakout(df)
Arguments
df |
dataframe |
Value
data.frame
Function to calculate the network potential for vertices v
Description
Function to calculate the network potential for vertices v
Usage
fcalc_np_all(neighbors, vertices, v, exp)
Arguments
neighbors |
list of neighbors for every node in the graph, type Rcpp::list |
vertices |
node list for graph, type Rcpp::StringVector |
v |
list of nodes for which we plan to calculate network potential |
exp |
named vector of expression for each node in vertices |
final .combine function to run in compute_null_dnp foreach looping structure
Description
final .combine function to run in compute_null_dnp foreach looping structure
Usage
final_combine(x)
Arguments
x |
aggregated info |
Value
data.frame
Internal function that computes the mean/stdev for each gene from a wide-format data frame.
Description
This function is called by the high-level function "bootstrap_null".
Usage
final_dist_calc(df_list)
Arguments
df_list |
: list of dataframes from foreach loop in bootstrap_null |
Value
data.frame
function to get graph neighbors (along with their expression values) for a given gene in a given network g
Description
just a wrapper around igraph::neighbors() for convenience
Usage
get_neighbors(gene, g)
Arguments
gene |
gene to grab neighbors from. |
g |
igraph object - will be filtered so that only nodes found in both exp and g are kept |
Value
named numeric vector.
Helper function for compute_null_dnp - returns a graph with randomly permuted edges.
Description
currently just a wrapper for igraph::rewire but may add more functionality in the future
Usage
get_random_graph(g)
Arguments
g |
graph to be permuted |
Value
igraph
See Also
Helper function for compute_null_dnp - returns the top n genes by dnp for each sample
Description
Helper function for compute_null_dnp - returns the top n genes by dnp for each sample
Usage
get_topn(df, n_genes)
Arguments
df |
output of |
n_genes |
integer describing number of genes per sample that we will compute the null distribution for |
Generic function to filter either an igraph object or a PPI network
Description
Generic function to filter either an igraph object or a PPI network
Usage
gfilter(
method = NULL,
g = NULL,
val = NULL,
use_ppi,
igraph_method = NULL,
n = 100,
desc = TRUE,
...
)
Arguments
method |
str |
g |
igraph object |
val |
named numeric vector - some measure of node state (i.e. gene expression in the case of a PPI) |
use_ppi |
bool - should we use a ppi from online repository? |
igraph_method |
bool - is the user-provided method an igraph node scoring function? |
n |
int - number of nodes to include in the returned subgraph |
desc |
bool - do we want the top or bottom examples of the provided metric |
... |
additional params passed to |
Value
igraph
See Also
gfilter.ct, gfilter.np, gfilter.igraph_method
Method to filter the graph based on parameters passed to compute_crosstalk
Description
Method to filter the graph based on parameters passed to compute_crosstalk
Usage
gfilter.ct(seeds, return_df = FALSE, ...)
Arguments
seeds |
vector (str or numeric) user provided vertex ids to use as seeds in the random walk with restarts' |
return_df |
bool should we return a list containing the filtered graph + the RWR output that was used to do the filtering? |
... |
additional arguments passed to |
Value
igraph object
Method to filter graph based on any igraph method that scores verticies.
Description
Method to filter graph based on any igraph method that scores verticies.
Usage
gfilter.igraph_method(g, use_ppi = TRUE, method, n = 500, desc, val_name, ...)
Arguments
g |
igraph object |
use_ppi |
bool - should we use a ppi from online repository? |
method |
str |
n |
int - number of nodes to include in the returned subgraph |
desc |
bool - do we want the top or bottom examples of the provided metric |
val_name |
str |
... |
additional parameters passed to load_ppi |
Value
igraph
Method to filter graph based on network potential values.
Description
convenience function - it just calls gfilter.value after computing np
Usage
gfilter.np(g, val, use_ppi = TRUE, n = 500, desc, ...)
Arguments
g |
igraph object |
val |
named numeric vector - some measure of node state (i.e. gene expression in the case of a PPI) |
use_ppi |
bool - should we use a ppi from online repository? |
n |
int - number of nodes to include in the returned subgraph |
desc |
bool - do we want the top or bottom examples of the provided metric |
... |
additional params passed to |
Details
For more information on network potential, see related paper
Value
igraph
Method to filter graph based on user provided value
Description
Method to filter graph based on user provided value
Usage
gfilter.value(g, val, use_ppi = TRUE, n = 500, val_name = "value", desc, ...)
Arguments
g |
igraph object |
val |
named numeric vector - some measure of node state (i.e. gene expression in the case of a PPI) |
use_ppi |
bool - should we use a ppi from online repository? |
n |
int - number of nodes to include in the returned subgraph |
val_name |
str |
desc |
bool - do we want the top or bottom examples of the provided metric |
... |
additional params passed to |
Value
igraph
Determine if a character vector contains ensembl gene_ids
Description
Determine if a character vector contains ensembl gene_ids
Usage
is_ensembl(x)
Arguments
x |
vector or single gene symbol |
Value
logical
Determine if a character vector contains entrez gene_ids
Description
Determine if a character vector contains entrez gene_ids
Usage
is_entrez(x)
Arguments
x |
vector or single gene symbol |
Value
logical
Helper function to load requested PPI w/ parameters
Description
Helper function to load requested PPI w/ parameters
Usage
load_ppi(
cache = NULL,
union = FALSE,
intersection = FALSE,
species = "9606",
min_score = 0,
ppi = "stringdb"
)
Arguments
cache |
A filepath to a folder downloaded files should be stored |
union |
bool |
intersection |
bool |
species |
species code either using latin species name or taxon id |
min_score |
minimum connectivity score for each edge in the network. |
ppi |
str |
Value
igraph object
Identify random sets of seeds with similar degree distribution to parent seed proteins
Description
This function will generate n character vectors of seeds to be passed to sparseRWR as part of the construction of a boostrapped null distribution for significance testing.
Usage
match_seeds(g, seed_proteins, n, set_seed = NULL)
Arguments
g |
igraph object representing the network under study. specified by "ppi" in bootstrap_null |
seed_proteins |
user defined seed proteins |
n |
number of random walks with repeats to create null distribution |
set_seed |
integer to set random number seed - for reproducibility |
Value
list of character vectors: randomly generated seed proteins with a similar degree distribution to parent seed proteins
Function to eliminate a node from a network g and calculate the change in some measure of network state
Description
this function is still under development.
Usage
node_repression(
g,
v_rm = as.character(names(igraph::V(g))),
exp,
state_function = calc_np_all,
neighbors_only = TRUE,
...
)
Arguments
g |
igraph network object |
v_rm |
index of vertices to remove |
exp |
expression vector for nodes in graph g |
state_function |
function to use to calculate network state before and after node_repression |
neighbors_only |
logical designating whether state function should be calculated for all nodes or just neighbors |
... |
additional parameters passed to state function. |
Function to normalize adjacency matrix by dividing each value by the colsum.
Description
Function to normalize adjacency matrix by dividing each value by the colsum.
Usage
norm_colsum(w)
Arguments
w |
The adjacency matrix of a given graph in sparse format - dgCMatrix |
Value
input matrix, normalized by column sums
Examples
# 1) Normalize by column sum on a simple matrix
v1 = (c(1,1,1,0))
v2 = c(0,0,0,1)
v3 = c(1,1,1,0)
v4 = c(0,0,0,1)
w = matrix(data = c(v1,v2,v3,v4), ncol = 4, nrow = 4)
norm_colsum(w)
Plot subnetwork identified using the compute_crosstalk function
Description
Convenience function for plotting crosstalkers - if you want to make more
customized/dynamic figures, there are lots of packages that can facilitate that,
including: visnetwork, ggraph, and even the base R plotting library
Usage
plot_ct(crosstalk_df, g, label_prop = 0.1, prop_keep = 0.4)
Arguments
crosstalk_df |
a dataframe containing the results of |
g |
igraph network object. |
label_prop |
Proportion of nodes to label - based on degree |
prop_keep |
How many proteins do we want to keep in the visualization (as a proportion of total) - subsets on top x proteins ranked by affinity score |
Value
NULL, draws the identified subgraph to device\
Examples
## Not run:
ct_df <- compute_crosstalk(c("EGFR", "KRAS"))
g <- prep_biogrid()
plot_ct(ct_df, g = g)
## End(Not run)
Function to allow users to choose the intersection of stringdb and biogrid Only works with the human PPI. min_score parameter only applies to strindb
Description
Function to allow users to choose the intersection of stringdb and biogrid Only works with the human PPI. min_score parameter only applies to strindb
Usage
ppi_intersection(cache = NULL, min_score = 800, edb = "default")
Arguments
cache |
A filepath to a folder downloaded files should be stored |
min_score |
minimum connectivity score for each edge in the network. |
edb |
ensemble database object |
Value
igraph object corresponding to PPI following intersection
Function to allow users to choose the union of stringdb and biogrid Only works with the human PPI. min_score parameter only applies to strindb
Description
Function to allow users to choose the union of stringdb and biogrid Only works with the human PPI. min_score parameter only applies to strindb
Usage
ppi_union(cache = NULL, min_score = 0, edb = "default")
Arguments
cache |
A filepath to a folder downloaded files should be stored |
min_score |
minimum connectivity score for each edge in the network. |
edb |
ensemble database object |
Value
igraph object corresponding to PPI following union
Prepare biogrid for use in analyses
Description
Prepare biogrid for use in analyses
Usage
prep_biogrid(cache = NULL)
Arguments
cache |
A filepath to a folder downloaded files should be stored |
Value
igraph object built from the adjacency matrix downloaded from thebiogrid.org.
Prepare Stringdb for use in analyses
Description
Basically a wrapper around the get_graph method from the stringdb package
Usage
prep_stringdb(
cache = NULL,
edb = "default",
min_score = 200,
version = "11.5",
species = "homo sapiens"
)
Arguments
cache |
A filepath to a folder downloaded files should be stored |
edb |
ensemble database object |
min_score |
minimum connectivity score for each edge in the network. |
version |
stringdb version |
species |
species code either using latin species name or taxon id |
Value
igraph object built from the adjacency matrix downloaded from stringdb.
Perform random walk with repeats on a sparse matrix
Description
This function borrows heavily from the RWR function in the RANKS package (cite here)
Usage
sparseRWR(seed_proteins, w, gamma = 0.6, eps = 1e-10, tmax = 1000, norm = TRUE)
Arguments
seed_proteins |
user defined seed proteins |
w |
The adjacency matrix of a given graph in sparse format - dgCMatrix |
gamma |
restart probability |
eps |
maximum allowed difference between the computed probabilities at the steady state |
tmax |
the maximum number of iterations for the RWR |
norm |
if True, w is normalized by dividing each value by the column sum. |
Value
numeric vector, affinity scores for all nodes in graph relative to provided seeds
Examples
# 1) Run Random walk with restarts on a simple matrix
v1 = (c(1,1,1,0))
v2 = c(0,0,0,1)
v3 = c(1,1,1,0)
v4 = c(0,0,0,1)
w = matrix(data = c(v1,v2,v3,v4), ncol = 4, nrow = 4)
sparseRWR(seed_proteins = c(1,3), w = w, norm = TRUE)
# 2) Works just as well on a sparse matrix
v1 = (c(1,1,1,0))
v2 = c(0,0,0,1)
v3 = c(1,1,1,0)
v4 = c(0,0,0,1)
w = matrix(data = c(v1,v2,v3,v4), ncol = 4, nrow = 4)
w = Matrix::Matrix(w, sparse = TRUE)
sparseRWR(seed_proteins = c(1,4), w = w, norm = TRUE)
#3) Sample workflow for use with human protein-protein interaction network
#g <- prep_biogrid()
#w <- igraph::as_adjacency_matrix(g)
#sparseRWR(seed_proteins = c("EGFR", "KRAS"), w = w, norm = TRUE)
returns a dataframe with information on supported species
Description
returns a dataframe with information on supported species
Usage
supported_species()
Value
dataframe
helper function to convert expression matrix to tidy dataframe (if not already)
Description
helper function to convert expression matrix to tidy dataframe (if not already)
Usage
tidy_expression(df)
Arguments
df |
dataframe |
Value
data.frame
helper to convert user-inputs to ncbi reference taxonomy.
Description
helper to convert user-inputs to ncbi reference taxonomy.
Usage
to_taxon_id(species)
Arguments
species |
user-inputted species |
Value
string corresponding to taxon id