| Title: | Transcriptional analysis based on transcriptograms |
|---|---|
| Description: | R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered. |
| Authors: | Diego Morais [aut, cre], Rodrigo Dalmolin [aut] |
| Maintainer: | Diego Morais <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.29.0 |
| Built: | 2024-11-30 05:42:33 UTC |
| Source: | https://github.com/bioc/transcriptogramer |
R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expression are altered.
Diego Morais <[email protected]> [maintainer]
Rodrigo Dalmolin <[email protected]>
da Silva, S. R. M., Perrone, G. C., Dinis, J. M., and de Almeida, R. M. C. (2014). Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics.
de Almeida, R. M. C., Clendenon, S. G., Richards, W. G., Boedigheimer, M., Damore, M., Rossetti, S., Harris, P. C., Herbert, B. S., Xu, W. M., Wandinger-Ness, A., Ward, H. H., Glazier, J. A. and Bacallao, R. L. (2016). Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD. Human Genomics, 10(1), 1–24.
Ferrareze, P. A. G., Streit, R. S. A., Santos, P. R. dos, Santos, F. M. dos, de Almeida, R. M. C., Schrank, A., Kmetzsch, L., Vainstein, M. H. and Staats, C. C. (2017). Transcriptional Analysis Allows Genome Reannotation and Reveals that Cryptococcus gattii VGII Undergoes Nutrient Restriction during Infection. Microorganisms.
Morais, D. A. A., Almeida, R. M. C. and Dalmolin, R. J. S. (2019). Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction. Bioinformatics.
Rybarczyk-Filho, J. L., Castro, M. A. A., Dalmolin, R. J. S., Moreira, J. C. F., Brunnet, L. G., and de Almeida, R. M. C. (2011). Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case. Nucleic Acids Research, 39(8), 3005-3016.
Xavier, L. A. da C., Bezerra, J. F., de Rezende, A. A., Oliveira, R. A. de C., Dalmolin, R. J. S., do Amaral, V. S. (2017). Analysis of genome instability biomarkers in children with non-syndromic orofacial clefts. Mutagenesis, 32(2), 313–321.
Github: source code, bug reports
References: (da Silva et al., 2014; de Almeida et al., 2016; Ferrareze et al., 2017; Morais et al., 2019; Rybarczyk-Filho et al., 2011; Xavier et al., 2017)
A subset of the Homo sapiens protein network data from STRINGdb, release 11. This subset contains only associations of proteins of combined score greater than or equal to 900.
associationassociation
Each row of the data.frame contains two variables:
The ENSEMBL Peptide ID of the first protein
The ENSEMBL Peptide ID of the second protein
Diego Morais
associationassociation
If species is a character, this method uses the biomaRt package
to build a Protein2GO list, if species is a data.frame, it will be used
instead.
The Protein2GO list will be used with the
topGO package to detect the most significant terms of each cluster
present in the DE slot of the object.
clusterEnrichment(object, universe = NULL, species, ontology = "biological process", algorithm = "classic", statistic = "fisher", pValue = 0.05, adjustMethod = "BH", nCores = 1L, onlyGenesInDE = FALSE) ## S4 method for signature 'Transcriptogram' clusterEnrichment(object, universe = NULL, species, ontology = "biological process", algorithm = "classic", statistic = "fisher", pValue = 0.05, adjustMethod = "BH", nCores = 1L, onlyGenesInDE = FALSE)clusterEnrichment(object, universe = NULL, species, ontology = "biological process", algorithm = "classic", statistic = "fisher", pValue = 0.05, adjustMethod = "BH", nCores = 1L, onlyGenesInDE = FALSE) ## S4 method for signature 'Transcriptogram' clusterEnrichment(object, universe = NULL, species, ontology = "biological process", algorithm = "classic", statistic = "fisher", pValue = 0.05, adjustMethod = "BH", nCores = 1L, onlyGenesInDE = FALSE)
object |
An object of class Transcriptogram. |
universe |
A character vector containing ENSEMBL Peptide IDs, or NULL,
if the universe
is composed by all the proteins present in the ordering slot of
|
species |
A character string specifying the species; or a data.frame containing two columns, the first one with ENSEMBL Peptide IDs (character), which may, or not, to contain the taxonomy ID of the species as prefix, and the second containing its respective Gene Ontology term (character). |
ontology |
A character string specifying the Gene Ontology domain, ignoring case sensitivity, the possible values are 'biological process', 'cellular component' and 'molecular function'. The default value of this argument is 'biological process'. |
algorithm |
Character string specifying which algorithm to use, the possible values are 'classic', 'elim', 'weight', 'weight01', 'lea' and 'parentchild'. The default value of this argument is 'classic'. |
statistic |
Character string specifying which test to use, the possible values are 'fisher', 'ks', 't', 'sum' and 'globaltest'. The default value of this argument is 'fisher'. |
pValue |
A numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate. The default value of this argument is 0.05. |
adjustMethod |
Character string specifying p-value adjustment method, the possible values are 'none', 'BH', 'fdr' (equivalent to 'BH'), 'BY', 'hochberg', 'hommel', 'bonferroni', and 'holm'. The default value of this argument is 'BH'. |
nCores |
An integer number, referring to the number of processing cores to be used; or a logical value, TRUE indicating that all processing cores should be used, and FALSE indicating the use of just one processing core. The default value of this argument is 1. |
onlyGenesInDE |
Logical value, set as TRUE to use only the genes in the DE slot. Set as FALSE to use all the genes referring to the positions in the clusters slot. The default value of this argument is FALSE. |
This method creates a data.frame, containing the most significant terms of each cluster, to feed the Terms slot of an object of class Transcriptogram.
Diego Morais
differentiallyExpressed, transcriptogramPreprocess, GSE9988, GPL570, Hs900, HsBPTerms, association, transcriptogramStep1, transcriptogramStep2
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) transcriptogram <- clusterEnrichment(transcriptogram, species = "Homo sapiens", pValue = 0.005) ## this call also works transcriptogram <- clusterEnrichment(transcriptogram, species = HsBPTerms, pValue = 0.005) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) transcriptogram <- clusterEnrichment(transcriptogram, species = "Homo sapiens", pValue = 0.005) ## this call also works transcriptogram <- clusterEnrichment(transcriptogram, species = HsBPTerms, pValue = 0.005) ## End(Not run)
This method uses the RedeR package to display graphs of the differentially expressed clusters.
clusterVisualization(object, maincomp = FALSE, connected = FALSE, host = "127.0.0.1", port = 9091, clusters = NULL, onlyGenesInDE = FALSE, colors = NULL) ## S4 method for signature 'Transcriptogram' clusterVisualization(object, maincomp = FALSE, connected = FALSE, host = "127.0.0.1", port = 9091, clusters = NULL, onlyGenesInDE = FALSE, colors = NULL)clusterVisualization(object, maincomp = FALSE, connected = FALSE, host = "127.0.0.1", port = 9091, clusters = NULL, onlyGenesInDE = FALSE, colors = NULL) ## S4 method for signature 'Transcriptogram' clusterVisualization(object, maincomp = FALSE, connected = FALSE, host = "127.0.0.1", port = 9091, clusters = NULL, onlyGenesInDE = FALSE, colors = NULL)
object |
An object of class Transcriptogram. |
maincomp |
Logical value, set as TRUE if you want to display only the main component of each cluster. The default value of this argument is FALSE. |
connected |
Logical value, set as TRUE if you want to display only connected nodes. The default value of this argument is FALSE. |
host |
The domain name of the machine that is running the RedeR XML-RPC server. |
port |
An integer specifying the port on which the XML-RPC server should listen. |
clusters |
An integer vector specifying the clusters to be displayed. If NULL, all clusters will be displayed. |
onlyGenesInDE |
Logical value, set as TRUE to use only the genes in the DE slot. Set as FALSE to use all the genes referring to the positions in the clusters slot. The default value of this argument is FALSE. |
colors |
Color vector used to distinguish the clusters. If NULL, the rainbow palette will be used to generate the colors. The color vector must contain a color for each cluster. |
RedeR package requirements: Java Runtime Environment (>= 6).
This method returns an object of the RedPort Class.
Diego Morais
differentiallyExpressed, transcriptogramPreprocess, GSE9988, GPL570, Hs900, association, transcriptogramStep1, transcriptogramStep2, RedPort
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, DEsymbols) rdp <- clusterVisualization(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, DEsymbols) rdp <- clusterVisualization(transcriptogram) ## End(Not run)
Calculates network properties as function of node connectivity/degree (k), such as: probability of a protein of the graph has degree k, average assortativity of the nodes of degree k, and the average clustering coefficient of the nodes of degree k.
connectivityProperties(object) ## S4 method for signature 'Transcriptogram' connectivityProperties(object)connectivityProperties(object) ## S4 method for signature 'Transcriptogram' connectivityProperties(object)
object |
An object of class Transcriptogram. |
The assortativity of a node can be measured by the average degree of its neighbors.
This method returns a data.frame containing: unique degrees (k) of the nodes of the graph, probability (pk) of a node of the graph has degree k, average assortativity (ak) of the nodes of degree k, and the average clustering coefficient (ck) of the nodes of degree k.
Diego Morais
transcriptogramPreprocess, Hs900, association
transcriptogram <- transcriptogramPreprocess(association, Hs900) ## Not run: cProperties <- connectivityProperties(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900) ## Not run: cProperties <- connectivityProperties(transcriptogram) ## End(Not run)
Gets the content of the DE slot of an object of class Transcriptogram.
DE(object) ## S4 method for signature 'Transcriptogram' DE(object)DE(object) ## S4 method for signature 'Transcriptogram' DE(object)
object |
An object of class Transcriptogram. |
This method returns the content of the DE slot of an object of class Transcriptogram.
Diego Morais
Hs900, association, transcriptogramPreprocess
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) DE(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) DE(transcriptogram) ## End(Not run)
A mapping between ENSEMBL Peptide ID and Symbol (Gene Name). This dataset was created to map the Homo sapiens proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
DEsymbolsDEsymbols
Each row of the data.frame contains two variables:
The ENSEMBL Peptide ID
The Gene Name
Diego Morais
DEsymbolsDEsymbols
This method uses the limma package to identify which genes are
differentially expressed,
meeting the pValue requirement, for the contrast "case-control".
The levels lenght must be
equal to the number of samples present in the transcriptogramS2 slot of
the object, and its contents
is related to the order that the samples appear. FALSE must be used to
indicate case samples,
and TRUE to indicate control samples. If species is NULL, no
translation will be done, if species is a character,
the biomaRt package will be used to translate the ENSEMBL
Peptide ID to Symbol
(Gene Name), and if species is a data.frame, it will be used
instead.
If the translation fail for some protein, its ENSEMBL
Peptide ID will be present
into the Symbol column. This method also groups the differentially expressed proteins detected
in clusters, and plots a graphical representation of this clustering.
differentiallyExpressed(object, levels, pValue = 0.05, species = object@Protein2Symbol, adjustMethod = "BH", trend = FALSE, title = "Differential expression", boundaryConditions = TRUE, colors = NULL) ## S4 method for signature 'Transcriptogram' differentiallyExpressed(object, levels, pValue = 0.05, species = object@Protein2Symbol, adjustMethod = "BH", trend = FALSE, title = "Differential expression", boundaryConditions = TRUE, colors = NULL)differentiallyExpressed(object, levels, pValue = 0.05, species = object@Protein2Symbol, adjustMethod = "BH", trend = FALSE, title = "Differential expression", boundaryConditions = TRUE, colors = NULL) ## S4 method for signature 'Transcriptogram' differentiallyExpressed(object, levels, pValue = 0.05, species = object@Protein2Symbol, adjustMethod = "BH", trend = FALSE, title = "Differential expression", boundaryConditions = TRUE, colors = NULL)
object |
An object of class Transcriptogram. |
levels |
A logical vector that classify the columns, referring to
samples, of the transcriptogramS2 slot of the |
pValue |
A numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate. The default value of this argument is 0.05. |
species |
A character string that will be used, ignoring case sensitivity, to translate the ENSEMBL Peptide ID to Symbol (Gene Name); or a data.frame containing two columns, the first one with ENSEMBL Peptide IDs (character), which may, or not, to contain the taxonomy ID of the species as prefix, and the second containing its respective Symbol (character). The default value of this argument is the content of the object Protein2Symbol slot. |
adjustMethod |
Character string specifying p-value adjustment method, the possible values are 'none', 'BH', 'fdr' (equivalent to 'BH'), 'BY' and 'holm'. The default value for this argument is 'BH'. |
trend |
Logical value, set as TRUE to use the limma-trend approach for RNA-Seq. The default value of this argument is FALSE. |
title |
An overall title for the plot. The default value of this argument is "Differential expression" |
boundaryConditions |
Logical value, defines whether the clusters limits will be extended using the current value of the radius slot. If TRUE, nearby clusters will be merged if its limits overlap. The default value of this argument is TRUE. |
colors |
Color vector used to distinguish the clusters. If NULL, the rainbow palette will be used to generate the colors. The color vector must contain a color for each cluster. |
This method creates a data.frame to feed the DE slot of an object of class Transcriptogram. This data.frame of differentially expressed proteins contains log2-fold-change, raw p-values, adjusted p-values, and an integer number that indicates if the protein is downregulated (-1) or upregulated (1).
Diego Morais
transcriptogramPreprocess, GSE9988, GPL570, Hs900, association, DEsymbols, transcriptogramStep1, transcriptogramStep2
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) ## translating ENSEMBL Peptide IDs to Symbols transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, "Homo sapiens") ## these calls also works transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, "H sapiens") transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, DEsymbols) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) ## translating ENSEMBL Peptide IDs to Symbols transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, "Homo sapiens") ## these calls also works transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, "H sapiens") transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01, DEsymbols) ## End(Not run)
Plots the ratio (number of genes related to a term inside the window/total number of genes in the window) from a set of Gene Ontology terms.
enrichmentPlot(object, nCores = 1L, nTerms = 1L, GOIDs = NULL, title = "Enrichment", alpha = 0.15, colors = NULL) ## S4 method for signature 'Transcriptogram' enrichmentPlot(object, nCores = 1L, nTerms = 1L, GOIDs = NULL, title = "Enrichment", alpha = 0.15, colors = NULL)enrichmentPlot(object, nCores = 1L, nTerms = 1L, GOIDs = NULL, title = "Enrichment", alpha = 0.15, colors = NULL) ## S4 method for signature 'Transcriptogram' enrichmentPlot(object, nCores = 1L, nTerms = 1L, GOIDs = NULL, title = "Enrichment", alpha = 0.15, colors = NULL)
object |
An object of class Transcriptogram. |
nCores |
An integer number, referring to the number of processing cores to be used; or a logical value, TRUE indicating that all processing cores should be used, and FALSE indicating the use of just one processing core. The default value of this argument is 1. |
nTerms |
An integer number referring to the number of top terms from each cluster. The default value of this argument is 1. |
GOIDs |
A character vector containing the Gene Ontology
accessions to be plotted. If NULL, the top |
title |
An overall title for the plot. The default value of this argument is "Enrichment" |
alpha |
The alpha value indicates the color transparency of the clusters regions. This value goes from 0 to 1, where 0 is completely transparent, and 1 is opaque. |
colors |
Color vector used to distinguish the clusters. If NULL, the rainbow palette will be used to generate the colors. The color vector must contain a color for each cluster. |
This method returns an ggplot2 object.
Diego Morais
differentiallyExpressed, transcriptogramPreprocess, GSE9988, GPL570, Hs900, HsBPTerms, association, transcriptogramStep1, transcriptogramStep2, clusterEnrichment
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) transcriptogram <- clusterEnrichment(transcriptogram, species = "Homo sapiens", pValue = 0.005) enrichmentPlot(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) transcriptogram <- clusterEnrichment(transcriptogram, species = "Homo sapiens", pValue = 0.005) enrichmentPlot(transcriptogram) ## End(Not run)
A mapping between ENSEMBL Peptide ID and probe identifier, for the Homo sapiens and the platform GPL570, [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array.
GPL570GPL570
Each row of the data.frame contains two variables:
The ENSEMBL Peptide ID
The probe identifier
This dataset was created to map the Homo sapiens proteins, from STRINGdb release 11, of combined score greater than or equal to 700.
Diego Morais
GPL570GPL570
Expression values, obtained by microarray, of 3 cases and 3 controls referring to the Gene Expression Omnibus accession number GSE9988. The data.frame has 6 columns, each one contains expression values of a sample, the first 3 columns are case samples, and the last 3 are control samples. Each row contains expression values obtained by the probe mentioned in its respective rowname. The expression values were normalized using the affy package and, to reduce the required storage space, this data.frame contains only 6 samples (GSM252443, GSM252444, GSM252445, GSM252465, GSM252466, GSM252467). The rows of each sample are composed only by probes mapped, by the GPL570 dictionary, to proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
GSE9988GSE9988
An object of class data.frame with 27828 rows and 6 columns.
Diego Morais
GSE9988GSE9988
A character vector containing the Homo sapiens proteins, from STRINGdb release 11, of combined score greater than or equal to 700.
Hs700Hs700
An object of class character of length 17185.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 1451645000.
Diego Morais
Hs700Hs700
A character vector containing the Homo sapiens proteins, from STRINGdb release 11, of combined score greater than or equal to 800.
Hs800Hs800
An object of class character of length 14711.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 808731532.
Diego Morais
Hs800Hs800
A character vector containing the Homo sapiens proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
Hs900Hs900
An object of class character of length 12396.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 489730786.
Diego Morais
Hs900Hs900
A mapping between ENSEMBL Peptide ID and Gene Ontology, biological processes, terms. This dataset was created to map the Homo sapiens proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
HsBPTermsHsBPTerms
Each row of the data.frame contains two variables:
The ENSEMBL Peptide ID
The Gene Ontology ID
Diego Morais
HsBPTermsHsBPTerms
A character vector containing the Mus musculus proteins, from STRINGdb release 11, of combined score greater than or equal to 700.
Mm700Mm700
An object of class character of length 16690.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 964301098.
Diego Morais
Mm700Mm700
A character vector containing the Mus musculus proteins, from STRINGdb release 11, of combined score greater than or equal to 800.
Mm800Mm800
An object of class character of length 13655.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 497514808.
Diego Morais
Mm800Mm800
A character vector containing the Mus musculus proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
Mm900Mm900
An object of class character of length 11141.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 282900384.
Diego Morais
Mm900Mm900
Calculates protein (node) properties, such as: degree/connectivity, number of triangles and clustering coefficient; and properties of the window, region of n (radius * 2 + 1) proteins centered at a protein, such as: connectivity, clustering coefficient and modularity.
orderingProperties(object, nCores = 1L) ## S4 method for signature 'Transcriptogram' orderingProperties(object, nCores = 1L)orderingProperties(object, nCores = 1L) ## S4 method for signature 'Transcriptogram' orderingProperties(object, nCores = 1L)
object |
An object of class Transcriptogram. |
nCores |
An integer number, referring to the number of processing cores to be used; or a logical value, TRUE indicating that all processing cores should be used, and FALSE indicating the use of just one processing core. The default value of this argument is 1. |
Connectivity/degree of a node is the number of edges it presents. A triangle of a node represents a pair of connected neighbors, the number of triangles on the adjacency list of a node is required to calculate its clustering coefficient. The clustering coefficient of a node measures, in the interval [0, 1], the likelihood that any two of its neighbors are themselves connected, this is calculated by the ratio between the number of triangles that the node has, and the maximum possible number of edges on its cluster (nodeTriangles / (nodeDegree * (nodeDegree - 1) / 2)). The window connectivity is the average connectivity calculated over the window. The window clustering coefficient, a value in the interval [0, 1], is the average clustering coefficient calculated over the window. The window modularity, a value in the interval [0, 1], is defined as the ratio between the total number of edges between any two nodes of the window, and the sum of the degrees of the nodes presents in the window. The window considers periodic boundary conditions to deal with proteins near the ends of the ordering.
This method returns a data.frame containing: ENSEMBL Peptide ID, its position on the ordering, node degree, number of triangles and clustering coefficient, and window connectivity, clustering coefficient and modularity.
Diego Morais
da Silva, S. R. M., Perrone, G. C., Dinis, J. M., and de Almeida, R. M. C. (2014). Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics.
de Almeida, R. M. C., Clendenon, S. G., Richards, W. G., Boedigheimer, M., Damore, M., Rossetti, S., Harris, P. C., Herbert, B. S., Xu, W. M., Wandinger-Ness, A., Ward, H. H., Glazier, J. A. and Bacallao, R. L. (2016). Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD. Human Genomics, 10(1), 1–24.
Ferrareze, P. A. G., Streit, R. S. A., Santos, P. R. dos, Santos, F. M. dos, de Almeida, R. M. C., Schrank, A., Kmetzsch, L., Vainstein, M. H. and Staats, C. C. (2017). Transcriptional Analysis Allows Genome Reannotation and Reveals that Cryptococcus gattii VGII Undergoes Nutrient Restriction during Infection. Microorganisms.
Morais, D. A. A., Almeida, R. M. C. and Dalmolin, R. J. S. (2019). Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction. Bioinformatics.
Rybarczyk-Filho, J. L., Castro, M. A. A., Dalmolin, R. J. S., Moreira, J. C. F., Brunnet, L. G., and de Almeida, R. M. C. (2011). Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case. Nucleic Acids Research, 39(8), 3005-3016.
Xavier, L. A. da C., Bezerra, J. F., de Rezende, A. A., Oliveira, R. A. de C., Dalmolin, R. J. S., do Amaral, V. S. (2017). Analysis of genome instability biomarkers in children with non-syndromic orofacial clefts. Mutagenesis, 32(2), 313–321.
transcriptogramPreprocess, Hs900, association
transcriptogram <- transcriptogramPreprocess(association, Hs900, 2) ## Not run: oProperties <- orderingProperties(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 2) ## Not run: oProperties <- orderingProperties(transcriptogram) ## End(Not run)
Retrieve or set the content of the radius slot of an object of class Transcriptogram.
radius(object) <- value radius(object) ## S4 replacement method for signature 'Transcriptogram' radius(object) <- value ## S4 method for signature 'Transcriptogram' radius(object)radius(object) <- value radius(object) ## S4 replacement method for signature 'Transcriptogram' radius(object) <- value ## S4 method for signature 'Transcriptogram' radius(object)
object |
An object of class Transcriptogram. |
value |
An non-negative integer referring to the window radius required for some methods. |
This method returns the content of the radius slot of an object of class Transcriptogram.
Diego Morais
Hs900, association, transcriptogramPreprocess, transcriptogramStep2, orderingProperties
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) radius(transcriptogram) <- 80 radius(transcriptogram)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) radius(transcriptogram) <- 80 radius(transcriptogram)
A character vector containing the Rattus norvegicus proteins, from STRINGdb release 11, of combined score greater than or equal to 700.
Rn700Rn700
An object of class character of length 17021.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 920071574.
Diego Morais
Rn700Rn700
A character vector containing the Rattus norvegicus proteins, from STRINGdb release 11, of combined score greater than or equal to 800.
Rn800Rn800
An object of class character of length 13887.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 453159596.
Diego Morais
Rn800Rn800
A character vector containing the Rattus norvegicus proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
Rn900Rn900
An object of class character of length 10788.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 222438518.
Diego Morais
Rn900Rn900
A character vector containing the Saccharomyces cerevisiae proteins, from STRINGdb release 11, of combined score greater than or equal to 700.
Sc700Sc700
An object of class character of length 6157.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 163374428.
Diego Morais
Sc700Sc700
A character vector containing the Saccharomyces cerevisiae proteins, from STRINGdb release 11, of combined score greater than or equal to 800.
Sc800Sc800
An object of class character of length 5847.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 85930192.
Diego Morais
Sc800Sc800
A character vector containing the Saccharomyces cerevisiae proteins, from STRINGdb release 11, of combined score greater than or equal to 900.
Sc900Sc900
An object of class character of length 5213.
Generated by The Transcriptogramer V.1.0 for Windows. Input arguments: isothermal steps - 100; Monte Carlo steps - 20000; cooling factor - 0.5; alpha value - 1.0; percentual energy for initial temperature - 0.0001. Final energy: 38447790.
Diego Morais
Sc900Sc900
Gets the content of the Terms slot of an object of class Transcriptogram.
Terms(object) ## S4 method for signature 'Transcriptogram' Terms(object)Terms(object) ## S4 method for signature 'Transcriptogram' Terms(object)
object |
An object of class Transcriptogram. |
This method returns the content of the Terms slot of an object of class Transcriptogram.
Diego Morais
differentiallyExpressed, transcriptogramPreprocess, GSE9988, GPL570, Hs900, HsBPTerms, association, transcriptogramStep1, transcriptogramStep2, clusterEnrichment
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) transcriptogram <- clusterEnrichment(transcriptogram, species = "Homo sapiens", pValue = 0.005) Terms(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) levels <- c(rep(FALSE, 3), rep(TRUE, 3)) transcriptogram <- differentiallyExpressed(transcriptogram, levels, 0.01) transcriptogram <- clusterEnrichment(transcriptogram, species = "Homo sapiens", pValue = 0.005) Terms(transcriptogram) ## End(Not run)
This S4 class includes methods to use expression values with ordered proteins.
associationA data.frame containing two columns, with rows containing ENSEMBL Peptide IDs that are connected.
orderingA data.frame containing two columns, the first one with ENSEMBL Peptide IDs, and the second containing its respective position.
transcriptogramS1A data.frame produced as the result of averaging over all identifiers related to the same protein.
transcriptogramS2A data.frame produced as the result of averaging over the window.
radiusAn non-negative integer referring to the window radius.
statusAn integer used internally to check the status of the object.
DEA data.frame of differentially expressed proteins.
clustersA list indicating the first and the last position belonging to each cluster.
pbcLogical value used internally to indicate the overlapping of the first and the last cluster.
Protein2SymbolA data.frame containing two columns, the first one with ENSEMBL Peptide IDs, and the second containing its respective Symbol.
Protein2GOA data.frame containing two columns, the first one with ENSEMBL Peptide IDs, and the second containing its respective Gene Ontology accession.
TermsA data.frame containing the enriched Gene Ontology terms.
genesInTermA list of GO terms and its respective ENSEMBL Peptide IDs, feeded by the clusterEnrichment() method.
Diego Morais
transcriptogramPreprocess, DE, radius, orderingProperties, connectivityProperties, transcriptogramStep1, transcriptogramStep2, differentiallyExpressed, clusterVisualization, clusterEnrichment, enrichmentPlot
Constructor for the Transcriptogram object.
transcriptogramPreprocess(association, ordering, radius = 0L)transcriptogramPreprocess(association, ordering, radius = 0L)
association |
A matrix, or data.frame, containing two columns of ENSEMBL Peptide IDs (character); or the path for a file containing two columns, no header, with rows composed by the ENSEMBL Peptide IDs of two proteins that are connected. |
ordering |
A character vector containing ordered ENSEMBL Peptide IDs; a data.frame containing two columns, the first one with ENSEMBL Peptide IDs (character), and the second containing its respective position (non-negative integer); or the path for a file containing two columns, a row for the headers, with rows composed respectively by a ENSEMBL Peptide ID and its respective position. |
radius |
An non-negative integer referring to the window radius required for some methods. |
A preprocessed object of class Transcriptogram.
Diego Morais
Transcriptogram-class, association, Hs900
transcriptogram <- transcriptogramPreprocess(association, Hs900)transcriptogram <- transcriptogramPreprocess(association, Hs900)
For each transcriptome sample, this method assigns to each protein the
average of the expression values of all the identifiers related to
it. It is necessary a
dictionary to map the identifiers to proteins.
transcriptogramStep1(object, expression, dictionary, nCores = 1L) ## S4 method for signature 'Transcriptogram' transcriptogramStep1(object, expression, dictionary, nCores = 1L)transcriptogramStep1(object, expression, dictionary, nCores = 1L) ## S4 method for signature 'Transcriptogram' transcriptogramStep1(object, expression, dictionary, nCores = 1L)
object |
An object of class Transcriptogram. |
expression |
A matrix, or data.frame, containing normalized expression values from samples of microarrays or RNA-Seq (log2-counts-per-million). |
dictionary |
A matrix, or data.frame, containing two columns, the first
column must contains the
ENSEMBL Peptide ID, and the second column must contains values that appear
as rownames in |
nCores |
An integer number, referring to the number of processing cores to be used; or a logical value, TRUE indicating that all processing cores should be used, and FALSE indicating the use of just one processing core. The default value of this argument is 1. |
This method creates a data.frame to feed the transcriptogramS1 slot of an object of class Transcriptogram. Each row of the data.frame contains: an ENSEMBL Peptide ID, its respective position in the ordering and the mean of the expression values of the identifiers related to the same protein.
Diego Morais
da Silva, S. R. M., Perrone, G. C., Dinis, J. M., and de Almeida, R. M. C. (2014). Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics.
de Almeida, R. M. C., Clendenon, S. G., Richards, W. G., Boedigheimer, M., Damore, M., Rossetti, S., Harris, P. C., Herbert, B. S., Xu, W. M., Wandinger-Ness, A., Ward, H. H., Glazier, J. A. and Bacallao, R. L. (2016). Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD. Human Genomics, 10(1), 1–24.
Ferrareze, P. A. G., Streit, R. S. A., Santos, P. R. dos, Santos, F. M. dos, de Almeida, R. M. C., Schrank, A., Kmetzsch, L., Vainstein, M. H. and Staats, C. C. (2017). Transcriptional Analysis Allows Genome Reannotation and Reveals that Cryptococcus gattii VGII Undergoes Nutrient Restriction during Infection. Microorganisms.
Morais, D. A. A., Almeida, R. M. C. and Dalmolin, R. J. S. (2019). Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction. Bioinformatics.
Rybarczyk-Filho, J. L., Castro, M. A. A., Dalmolin, R. J. S., Moreira, J. C. F., Brunnet, L. G., and de Almeida, R. M. C. (2011). Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case. Nucleic Acids Research, 39(8), 3005-3016.
Xavier, L. A. da C., Bezerra, J. F., de Rezende, A. A., Oliveira, R. A. de C., Dalmolin, R. J. S., do Amaral, V. S. (2017). Analysis of genome instability biomarkers in children with non-syndromic orofacial clefts. Mutagenesis, 32(2), 313–321.
transcriptogramPreprocess, GSE9988, GPL570, Hs900, association
transcriptogram <- transcriptogramPreprocess(association, Hs900) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) ## End(Not run)
To each position of the ordering, this method assigns a value equal to the average of the expression values inside a window, region of n (radius * 2 + 1) proteins centered at a protein. The window considers periodic boundary conditions to deal with proteins near the ends of the ordering.
transcriptogramStep2(object, nCores = 1L) ## S4 method for signature 'Transcriptogram' transcriptogramStep2(object, nCores = 1L)transcriptogramStep2(object, nCores = 1L) ## S4 method for signature 'Transcriptogram' transcriptogramStep2(object, nCores = 1L)
object |
An object of class Transcriptogram. |
nCores |
An integer number, referring to the number of processing cores to be used; or a logical value, TRUE indicating that all processing cores should be used, and FALSE indicating the use of just one processing core. The default value of this argument is 1. |
This method creates a data.frame to feed the transcriptogramS2 slot of an object of class Transcriptogram. Each row of the data.frame contains: the ENSEMBL Peptide ID used as center of the window, its position on the ordering, and the mean of the expression values of the window.
Diego Morais
da Silva, S. R. M., Perrone, G. C., Dinis, J. M., and de Almeida, R. M. C. (2014). Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics.
de Almeida, R. M. C., Clendenon, S. G., Richards, W. G., Boedigheimer, M., Damore, M., Rossetti, S., Harris, P. C., Herbert, B. S., Xu, W. M., Wandinger-Ness, A., Ward, H. H., Glazier, J. A. and Bacallao, R. L. (2016). Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD. Human Genomics, 10(1), 1–24.
Ferrareze, P. A. G., Streit, R. S. A., Santos, P. R. dos, Santos, F. M. dos, de Almeida, R. M. C., Schrank, A., Kmetzsch, L., Vainstein, M. H. and Staats, C. C. (2017). Transcriptional Analysis Allows Genome Reannotation and Reveals that Cryptococcus gattii VGII Undergoes Nutrient Restriction during Infection. Microorganisms.
Morais, D. A. A., Almeida, R. M. C. and Dalmolin, R. J. S. (2019). Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction. Bioinformatics.
Rybarczyk-Filho, J. L., Castro, M. A. A., Dalmolin, R. J. S., Moreira, J. C. F., Brunnet, L. G., and de Almeida, R. M. C. (2011). Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case. Nucleic Acids Research, 39(8), 3005-3016.
Xavier, L. A. da C., Bezerra, J. F., de Rezende, A. A., Oliveira, R. A. de C., Dalmolin, R. J. S., do Amaral, V. S. (2017). Analysis of genome instability biomarkers in children with non-syndromic orofacial clefts. Mutagenesis, 32(2), 313–321.
transcriptogramPreprocess, GSE9988, GPL570, Hs900, association, transcriptogramStep1
transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) ## End(Not run)transcriptogram <- transcriptogramPreprocess(association, Hs900, 50) ## Not run: transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570) transcriptogram <- transcriptogramStep2(transcriptogram) ## End(Not run)