Package 'SurfR'

Title:	Surface Protein Prediction and Identification
Description:	Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots.
Authors:	Aurora Maurizio [aut, cre] , Anna Sofia Tascini [aut, ctb]
Maintainer:	Aurora Maurizio <[email protected]>
License:	GPL-3 + file LICENSE
Version:	1.3.5
Built:	2024-12-23 03:40:06 UTC
Source:	https://github.com/bioc/SurfR

Annotate_SPID

Description

Annotate Surface Protein Coding genes according to EnrichR libraries

Usage

Annotate_SPID(
  DGE,
  enrich.database = "WikiPathway_2021_Human",
  output_tsv = FALSE
)
Annotate_SPID(
  DGE,
  enrich.database = "WikiPathway_2021_Human",
  output_tsv = FALSE
)

Arguments

`DGE`	Data.frame containing annotated DEG list, as the output of DGE or Gene2SProtein functions.
`enrich.database`	String containing the EnrichR databases you would like to consult. Default: WikiPathway_2021_Human.
`output_tsv`	Logical. If `TRUE`, outputs a tsv file with the results. By default, FALSE.

Value

A dataframe with surface protein coding DEGs annotation.

Warning

Be sure that enrich.database exists.

Examples

## Not run: 
# Deseq2 output sample
DGE = data.frame(GeneID = c("DLK1", "TOP2A"),
                 Mean_CPM_T = c(5.92, 9.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(10.22, 8.42),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67))
annotated_DGE = Annotate_SPID(DGE, "WikiPathway_2021_Human")

# Output of Gene2SProtein function
GeneNames = c("CIITA", "EPCAM", "DLK1", "CD24")
SurfaceProteins_df = Gene2SProtein(GeneNames, input_type = "gene_name")
annotated_SP = Annotate_SPID(SurfaceProteins_df, "GO_Biological_Process_2021")
## End(Not run)
## Not run: 
# Deseq2 output sample
DGE = data.frame(GeneID = c("DLK1", "TOP2A"),
                 Mean_CPM_T = c(5.92, 9.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(10.22, 8.42),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67))
annotated_DGE = Annotate_SPID(DGE, "WikiPathway_2021_Human")

# Output of Gene2SProtein function
GeneNames = c("CIITA", "EPCAM", "DLK1", "CD24")
SurfaceProteins_df = Gene2SProtein(GeneNames, input_type = "gene_name")
annotated_SP = Annotate_SPID(SurfaceProteins_df, "GO_Biological_Process_2021")
## End(Not run)

combine_fisher_invnorm

Description

Combine Meta-Analysis results with individual DE tables

Usage

combine_fisher_invnorm(
  ind_deg,
  invnorm,
  fishercomb,
  adjpval = 0.05,
  output_tsv = TRUE,
  output_filename = "combine_fisher_invnorm.tsv"
)
combine_fisher_invnorm(
  ind_deg,
  invnorm,
  fishercomb,
  adjpval = 0.05,
  output_tsv = TRUE,
  output_filename = "combine_fisher_invnorm.tsv"
)

Arguments

`ind_deg`	List of indipendent DEG dataframes with p-values to be combined.
`invnorm`	inverse normal p-value combination technique dataframe (output of metaRNAseq)
`fishercomb`	Fisher p-value combination technique dataframe (output of metaRNAseq)
`adjpval`	threshold to represent as binary the Meta-Analysis output adjpval.
`output_tsv`	logical. If TRUE, it outputs table with results. Default: TRUE
`output_filename`	File name for the results file.

Value

A dataframe with DEindices and DEname of DEG at the chosen Benjamini Hochberg threshold, and TestStatistic, rawpval, adjpval, binaryadjpval vectors for differential expression in the meta-analysis.

Examples

## Not run: 
# Deseq2 output samples
DGE1 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(5.92, 9.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(10.22, 8.42),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
DGE2 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(3.92, 8.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(7.22, 5.81),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
# input list
ind_deg <- list(DEG1_df = DGE1, DEG2_df = DGE2)
# perform invnorm meta-analysis
invnorm <- metaRNAseq(ind_deg, test_statistic = "invnorm", BHth = 0.05, nrep = c(2,2))
# perform fishercomb meta-analysis
fishercomb <- metaRNAseq(ind_deg, test_statistic = "fishercomb", BHth = 0.05)
# combine results
comb_pval_df <- combine_fisher_invnorm(ind_deg,
                                      invnorm, fishercomb,
                                      adjpval = 0.05,
                                      output_tsv = FALSE)
## End(Not run)
## Not run: 
# Deseq2 output samples
DGE1 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(5.92, 9.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(10.22, 8.42),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
DGE2 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(3.92, 8.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(7.22, 5.81),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
# input list
ind_deg <- list(DEG1_df = DGE1, DEG2_df = DGE2)
# perform invnorm meta-analysis
invnorm <- metaRNAseq(ind_deg, test_statistic = "invnorm", BHth = 0.05, nrep = c(2,2))
# perform fishercomb meta-analysis
fishercomb <- metaRNAseq(ind_deg, test_statistic = "fishercomb", BHth = 0.05)
# combine results
comb_pval_df <- combine_fisher_invnorm(ind_deg,
                                      invnorm, fishercomb,
                                      adjpval = 0.05,
                                      output_tsv = FALSE)
## End(Not run)

countData

Description

Simulated raw counts to use as input for DGE and plotPCA functions. metadata is available.

Usage

  data(countData)
data(countData)

Format

dataframe

Details

A dataframe with 2500 rows and 4 columns (sample names).

Value

A dataframe.

DGE function

Description

Perform Differential Gene Expression Analysis of RNA-Seq Data

Usage

DGE(
  expression,
  metadata,
  Nreplica,
  design = "~condition",
  condition = "condition",
  TEST,
  CTRL,
  alpha = 0.05,
  FC_filt = 0,
  output_tsv = FALSE,
  output_filename = "DEGs.tsv"
)
DGE(
  expression,
  metadata,
  Nreplica,
  design = "~condition",
  condition = "condition",
  TEST,
  CTRL,
  alpha = 0.05,
  FC_filt = 0,
  output_tsv = FALSE,
  output_filename = "DEGs.tsv"
)

Arguments

`expression`	Dataframe with counts
`metadata`	Dataframe with sample metadata
`Nreplica`	Double. Minimum number of replicates in each group
`design`	Design formula for DGE
`condition`	Column of the metadata ti use for DGE results
`TEST`	Character. sample name in metadata
`CTRL`	Character. sample name in metadata
`alpha`	Double. the significance cutoff used for optimizing the independent filtering (by default 0.1). If the adjusted p-value cutoff (FDR) will be a value other than 0.1, alpha should be set to that value.
`FC_filt`	Dataframe with counts
`output_tsv`	Logical. If `TRUE`, outputs a tsv file with the results. By default, FALSE.
`output_filename`	Name of the tsv output file. Default is DEGs.tsv.

Value

A dataframe with DEGs

Examples

## Not run: 
# Simulation of bulk RNA data
countData <- matrix(floor(runif(10000, min=0, max=101)),ncol=4)
colnames(countData) <- paste("sample", seq_len(ncol(countData)), sep = "")
rownames(countData) <- paste("gene", seq_along(seq_len(10000/4)), sep = "")
metadata <- data.frame(samplesID = paste("sample", seq_len(ncol(countData)), sep = ""),
                       condition = factor(c("A","A","B","B")))
row.names(metadata) <- metadata$samplesID
# Perform DGE
DGEresults <- DGE(expression = countData, metadata = metadata,
                 Nreplica = 2,
                 design = "~condition",condition = "condition",
                 TEST = "A", CTRL = "B")
## End(Not run)
## Not run: 
# Simulation of bulk RNA data
countData <- matrix(floor(runif(10000, min=0, max=101)),ncol=4)
colnames(countData) <- paste("sample", seq_len(ncol(countData)), sep = "")
rownames(countData) <- paste("gene", seq_along(seq_len(10000/4)), sep = "")
metadata <- data.frame(samplesID = paste("sample", seq_len(ncol(countData)), sep = ""),
                       condition = factor(c("A","A","B","B")))
row.names(metadata) <- metadata$samplesID
# Perform DGE
DGEresults <- DGE(expression = countData, metadata = metadata,
                 Nreplica = 2,
                 design = "~condition",condition = "condition",
                 TEST = "A", CTRL = "B")
## End(Not run)

DownloadArchS4 function

Description

Download count matrix from https://maayanlab.cloud/archs4/, given a vector of input GEO Sample accessions numbers (GSM).

Usage

DownloadArchS4(GSM, species, print_tsv = FALSE, filename = NULL)
DownloadArchS4(GSM, species, print_tsv = FALSE, filename = NULL)

Arguments

`GSM`	Vector with the GSM ids of the samples to consider.
`species`	Specify the specie of yuor GSM samples. Either human or mouse.
`print_tsv`	Logical. If `TRUE`, outputs a tsv file with the count matrix. By default, FALSE.
`filename`	Name of the tsv output file. Default is matrix.tsv.

Value

A count matrix with gene on the row and GSM ID on the column.

Warning

If the defined GSM ids do not have any match in ArchS4 database, we suggest to contact ArchS4 curator to add them.

Examples

## Not run: 
GSM <- c("GSM3447008", "GSM3447009")
GEO_count_matrix <- DownloadArchS4(GSM, species = "human",
                                  print_tsv = FALSE, filename = NULL)
## End(Not run)
## Not run: 
GSM <- c("GSM3447008", "GSM3447009")
GEO_count_matrix <- DownloadArchS4(GSM, species = "human",
                                  print_tsv = FALSE, filename = NULL)
## End(Not run)

enrichedList

Description

Input list for Enrichment_barplot function. enrichedList is the output of Enrichment function applied to ind_deg object when enrich.databases is equal to GO_Cellular_Component_2021, default parameters.

Usage

  data(enrichedList)
data(enrichedList)

Format

list

Details

enrichedList$fdr_up$GO_Cellular_Component_2021 contains upregulated gene enrichments, enrichedList$fdr_down$GO_Cellular_Component_2021 contains downregulated gene enrichments.

Value

A list of lists.

Enrichment function

Description

Perform enrichment Analysis of RNA-Seq Data

Usage

Enrichment(
  dfList,
  enrich.databases = c("GO_Biological_Process_2021", "GO_Cellular_Component_2021",
    "GO_Molecular_Function_2021", "KEGG_2021_Human", "MSigDB_Hallmark_2020",
    "WikiPathways_2016", "BioCarta_2016", "Jensen_TISSUES", "Jensen_COMPARTMENTS",
    "Jensen_DISEASES"),
  p_adj = 0.05,
  logFC = 1,
  save.results = FALSE
)
Enrichment(
  dfList,
  enrich.databases = c("GO_Biological_Process_2021", "GO_Cellular_Component_2021",
    "GO_Molecular_Function_2021", "KEGG_2021_Human", "MSigDB_Hallmark_2020",
    "WikiPathways_2016", "BioCarta_2016", "Jensen_TISSUES", "Jensen_COMPARTMENTS",
    "Jensen_DISEASES"),
  p_adj = 0.05,
  logFC = 1,
  save.results = FALSE
)

Arguments

`dfList`	Dataframes list
`enrich.databases`	Vector of EnrichR databases to consult
`p_adj`	Double. Adjusted pvalue threshold for the enrichment
`logFC`	Double. Fold change threshold for the enrichment
`save.results`	Logical. If TRUE saves input gene lists and enrichment results.

Value

A list of enrichment tables for upregulated and downregulated genes in the different enrichr databases

Examples

## Not run: 
df1 <- data.frame(GeneID  = c("MEST", "CDK1", "PCLAF", "BIRC5"),
                  baseMean = c(13490.22, 10490.23, 8888.33, 750.33),
                  log2FoldChange = c(5.78, 6.76, -7.78, -8.78),
                  padj = c(2.28e-143, 2.18e-115, 2.18e-45, 0.006),
                  row.names = c("MEST", "CDK1", "PCLAF", "BIRC5"))
df2 <- data.frame(GeneID  = c("MEST", "CDK1", "PCLAF", "BIRC5"),
                  baseMean = c(13490.22, 10490.23, 8888.33, 750.33),
                  log2FoldChange = c(5.78, 6.76, -7.78, -8.78),
                  padj = c(2.28e-143, 2.18e-115, 2.18e-45, 0.006),
                  row.names = c("MEST", "CDK1", "PCLAF", "BIRC5"))
dfList <- list(df1 = df1, df2 = df2)
test <- Enrichment(dfList, enrich.databases = c("GO_Cellular_Component_2021"),
                   save.results = FALSE)
## End(Not run)
## Not run: 
df1 <- data.frame(GeneID  = c("MEST", "CDK1", "PCLAF", "BIRC5"),
                  baseMean = c(13490.22, 10490.23, 8888.33, 750.33),
                  log2FoldChange = c(5.78, 6.76, -7.78, -8.78),
                  padj = c(2.28e-143, 2.18e-115, 2.18e-45, 0.006),
                  row.names = c("MEST", "CDK1", "PCLAF", "BIRC5"))
df2 <- data.frame(GeneID  = c("MEST", "CDK1", "PCLAF", "BIRC5"),
                  baseMean = c(13490.22, 10490.23, 8888.33, 750.33),
                  log2FoldChange = c(5.78, 6.76, -7.78, -8.78),
                  padj = c(2.28e-143, 2.18e-115, 2.18e-45, 0.006),
                  row.names = c("MEST", "CDK1", "PCLAF", "BIRC5"))
dfList <- list(df1 = df1, df2 = df2)
test <- Enrichment(dfList, enrich.databases = c("GO_Cellular_Component_2021"),
                   save.results = FALSE)
## End(Not run)

Enrichment_barplot

Description

Barplot representing the top up-regulated or down-regulated significant pathways

Usage

Enrichment_barplot(
  Enrich,
  enrich.databases = c("GO_Biological_Process_2021", "GO_Cellular_Component_2021",
    "GO_Molecular_Function_2021"),
  p_adj = 0.05,
  num_term = 10,
  cond = "UP",
  plot = FALSE
)
Enrichment_barplot(
  Enrich,
  enrich.databases = c("GO_Biological_Process_2021", "GO_Cellular_Component_2021",
    "GO_Molecular_Function_2021"),
  p_adj = 0.05,
  num_term = 10,
  cond = "UP",
  plot = FALSE
)

Arguments

`Enrich`	A list of enrichment tables for up and down-regulated genes in the different enrichR databases. Output of Enrichment.R function for one DGE experiment.
`enrich.databases`	Vector of EnrichR databases to consider. These databases must be present in the Enrich list.
`p_adj`	Double. Minimum Adjusted pvalue threshold for the enrichment
`num_term`	Double. Number of up-regulated and dw-regulated terms to represent
`cond`	String. Title of the plot.
`plot`	Logical. If TRUE save plot as pdf.

Value

bar plot of significant pathways.

Examples

## Not run: 
dbs <- c("GO_Biological_Process_2021")
dfList <- list()
dfList[["fdr_up"]]$GO_Biological_Process_2021 <- data.frame(
  Term = c("peripheral nervous system neuron differentiation (GO:0048934)",
           "apoptotic chromosome condensation (GO:0030263)",
           "negative regulation of CD4-positive, alpha-beta T cell differentiation (GO:0043371)"),
  Overlap = c("1/5", "1/5", "1/5"),
  P.value = c(0.0007498315, 0.0007498315, 0.0007498315),
  Adjusted.P.value = c(0.00893491, 0.00893491, 0.00893491),
  Old.P.value = c(0, 0, 0),
  Old.Adjusted.P.value = c(0, 0, 0),
  Odds.Ratio = c(2499.125, 2499.125, 2499.125),
  Combined.Score = c(17982.86, 17982.86, 17982.86),
  Genes = c("RUNX1", "TOP2A", "RUNX1"))
dfList[["fdr_down"]]$GO_Biological_Process_2021 <- data.frame(
  Term = c("skin morphogenesis (GO:0043589)",
           "skin development (GO:0043588)",
           "collagen fibril organization (GO:0030199)"),
  Overlap = c("2/7", "2/80", "2/89"),
  P.value = c(3.149296e-07, 4.727687e-05, 5.856991e-05),
  Adjusted.P.value = c(1.291211e-05, 8.004554e-04, 8.004554e-04),
  Old.P.value = c(0, 0, 0),
  Old.Adjusted.P.value = c(0, 0, 0),
  Odds.Ratio = c(7996.8000, 510.7436, 457.7011),
  Combined.Score = c(119719.427, 5086.745, 4460.430),
  Genes = c("COL1A1;COL1A2", "COL1A1;COL1A2", "COL1A1;COL1A2"))
Enrichment_barplot(dfList,
                   enrich.databases = dbs,
                   p_adj = 0.01, num_term = 3, cond = "UP")

## End(Not run)
## Not run: 
dbs <- c("GO_Biological_Process_2021")
dfList <- list()
dfList[["fdr_up"]]$GO_Biological_Process_2021 <- data.frame(
  Term = c("peripheral nervous system neuron differentiation (GO:0048934)",
           "apoptotic chromosome condensation (GO:0030263)",
           "negative regulation of CD4-positive, alpha-beta T cell differentiation (GO:0043371)"),
  Overlap = c("1/5", "1/5", "1/5"),
  P.value = c(0.0007498315, 0.0007498315, 0.0007498315),
  Adjusted.P.value = c(0.00893491, 0.00893491, 0.00893491),
  Old.P.value = c(0, 0, 0),
  Old.Adjusted.P.value = c(0, 0, 0),
  Odds.Ratio = c(2499.125, 2499.125, 2499.125),
  Combined.Score = c(17982.86, 17982.86, 17982.86),
  Genes = c("RUNX1", "TOP2A", "RUNX1"))
dfList[["fdr_down"]]$GO_Biological_Process_2021 <- data.frame(
  Term = c("skin morphogenesis (GO:0043589)",
           "skin development (GO:0043588)",
           "collagen fibril organization (GO:0030199)"),
  Overlap = c("2/7", "2/80", "2/89"),
  P.value = c(3.149296e-07, 4.727687e-05, 5.856991e-05),
  Adjusted.P.value = c(1.291211e-05, 8.004554e-04, 8.004554e-04),
  Old.P.value = c(0, 0, 0),
  Old.Adjusted.P.value = c(0, 0, 0),
  Odds.Ratio = c(7996.8000, 510.7436, 457.7011),
  Combined.Score = c(119719.427, 5086.745, 4460.430),
  Genes = c("COL1A1;COL1A2", "COL1A1;COL1A2", "COL1A1;COL1A2"))
Enrichment_barplot(dfList,
                   enrich.databases = dbs,
                   p_adj = 0.01, num_term = 3, cond = "UP")

## End(Not run)

Gene enrichment using Enrichr

Description

Gene enrichment using Enrichr

Usage

enrichr(genes, databases)
enrichr(genes, databases)

Arguments

`genes`	(Required). Character vector of Entrez gene symbols as input. A data.frame of gene symbols in first column is also acceptable, optionally a score denoting the degree of membership between 0 and 1 in the second column.
`databases`	(Required). Character vector of databases to search. See https://maayanlab.cloud/Enrichr/ for available databases.

Details

Gene enrichment using Enrichr, slighthly modified by Aurora Maurizio.

Value

Returns a list of data.frame of enrichment terms, p-values, ...

Author(s)

Wajid Jawaid [email protected]

Examples

## Not run: 
GeneID  = c("MEST", "CDK1", "PCLAF", "BIRC5")
dbs <- c("GO_Molecular_Function_2023", "GO_Cellular_Component_2023",
         "GO_Biological_Process_2023")
enriched1 <- enrichr(GeneID, dbs)
print(head(enriched1[[1]]))
## End(Not run)
## Not run: 
GeneID  = c("MEST", "CDK1", "PCLAF", "BIRC5")
dbs <- c("GO_Molecular_Function_2023", "GO_Cellular_Component_2023",
         "GO_Biological_Process_2023")
enriched1 <- enrichr(GeneID, dbs)
print(head(enriched1[[1]]))
## End(Not run)

Download data from enrichr in the form of a named list - function from hypeR

Description

Download data from enrichr in the form of a named list - function from hypeR

Usage

enrichr_download(genesets, db = c("Enrichr"))
enrichr_download(genesets, db = c("Enrichr"))

Arguments

`genesets`	A name corresponding to available genesets
`db`	A species

Value

A list of genesets

Examples

ATLAS <- enrichr_download("Human_Gene_Atlas")

ATLAS <- enrichr_download("Human_Gene_Atlas")

Gene2SProtein function

Description

Detect Surface Proteins from a vector of genes. The surface proteins are identified according to the in silico human surfaceome database, available at https://wlab.ethz.ch/surfaceome.

Usage

Gene2SProtein(
  genes,
  input_type = "gene_name",
  output_tsv = FALSE,
  output_filename = "surfaceProteins.tsv",
  Surfy_version = "log"
)
Gene2SProtein(
  genes,
  input_type = "gene_name",
  output_tsv = FALSE,
  output_filename = "surfaceProteins.tsv",
  Surfy_version = "log"
)

Arguments

`genes`	A vector of genes.
`input_type`	The gene identification type: `gene_name`, `ensembl`, `entrez` or `uniProt_name`. By default: `gene_name`.
`output_tsv`	Logical. If `TRUE`, outputs a tsv file with the results. By default, FALSE.
`output_filename`	Name of the tsv output file. Default is surfaceProteins.tsv.
`Surfy_version`	The version of surfy dataframe you wish to use. Choose between `log` or `newest`. By default use the most recent `log` version. If a log dataframe does not exist the `newest` is downloaded from https://wlab.ethz.ch/surfaceome.

Value

A data frame with filtered surface proteins from the genes array. The dataframe contains also addition information obtained from surfy.

Warning

The surfy database is interrogated using the gene identification type of your preference between gene_name, ensembl, entrez or uniProt_name. Note that you might loose some matches due to different gene version IDs.

Examples

 ## Not run: 
 # from gene name IDs to Surface proteins
 GeneNames <- c("CIITA", "EPCAM", "DLK1", "CD24", "CDCP1", "LYVE1", "ABCD1", "VAMP1")
 SurfaceProteins_df <- Gene2SProtein(GeneNames, input_type = "gene_name")

 # from ensembl IDs to Surface proteins
 Ensembl <- c("ENSG00000178343", "ENSG00000176895", "ENSG00000162419", "ENSG00000170776",
             "ENSG00000092529", "ENSG00000135926", "ENSG00000152595", "ENSG00000121577",
             "ENSG00000186094", "ENSG00000126773", "ENSG00000198918", "ENSG00000167378",
             "ENSG00000095574", "ENSG00000140678", "ENSG00000262484", "ENSG00000133739",
             "ENSG00000172469", "ENSG00000112992", "ENSG00000148343", "ENSG00000138593")
 SurfaceProteins_df <- Gene2SProtein(Ensembl, input_type = "ensembl",
                                   output_tsv = FALSE, Surfy_version = "new")
## End(Not run)
## Not run: 
 # from gene name IDs to Surface proteins
 GeneNames <- c("CIITA", "EPCAM", "DLK1", "CD24", "CDCP1", "LYVE1", "ABCD1", "VAMP1")
 SurfaceProteins_df <- Gene2SProtein(GeneNames, input_type = "gene_name")

 # from ensembl IDs to Surface proteins
 Ensembl <- c("ENSG00000178343", "ENSG00000176895", "ENSG00000162419", "ENSG00000170776",
             "ENSG00000092529", "ENSG00000135926", "ENSG00000152595", "ENSG00000121577",
             "ENSG00000186094", "ENSG00000126773", "ENSG00000198918", "ENSG00000167378",
             "ENSG00000095574", "ENSG00000140678", "ENSG00000262484", "ENSG00000133739",
             "ENSG00000172469", "ENSG00000112992", "ENSG00000148343", "ENSG00000138593")
 SurfaceProteins_df <- Gene2SProtein(Ensembl, input_type = "ensembl",
                                   output_tsv = FALSE, Surfy_version = "new")
## End(Not run)

GEOmetadata function

Description

Download metadata from https://www.ncbi.nlm.nih.gov/geo, given an input GEO accession series.

Usage

GEOmetadata(GSE, GPL = "")
GEOmetadata(GSE, GPL = "")

Arguments

`GSE`	The GSE series ID.
`GPL`	The GPL series numbers. Required only if the chosen GSE series ID include data from multiple sequencing platforms.

Value

A dataframe with all the available characteristics in GEO metadata genes array.

Warning

If the GEO accession series has more than 1 sequencing platforms you need to specify the GPL series numbers.

Examples

# only one sequencing platform
## Not run: 
mGSE133671 <- GEOmetadata(GSE = "GSE133671")
# multiple sequencing platforms
mGSE59483 <- GEOmetadata("GSE59483", GPL = c("GPL11154", "GPL15520"))
## End(Not run)
# only one sequencing platform
## Not run: 
mGSE133671 <- GEOmetadata(GSE = "GSE133671")
# multiple sequencing platforms
mGSE59483 <- GEOmetadata("GSE59483", GPL = c("GPL11154", "GPL15520"))
## End(Not run)

Check if EnrichR is online

Description

Helper function

Usage

getEnrichr(url, ...)
getEnrichr(url, ...)

Arguments

`url`	(Required). URL address to check
`...`	(Optional). Additional parameters to pass to GET

Details

Helper function for HTTP methods GET

Value

Invisible response object from GET request if successful, or NULL on failure

ind_deg

Description

Input list for metaRNAseq function made of 2 different small Deseq2 output samples dataframes for testing purposes: DEG1_df and DEG2_df.

Usage

  data(ind_deg)
data(ind_deg)

Format

dataframe list

Details

Each dataframe has 2 rows and 9 columns.

Value

A list of dataframes.

Look up available databases on Enrichr

Description

Look up available databases on Enrichr

Usage

listEnrichrDbs()
listEnrichrDbs()

Details

Look up available databases on Enrichr

Value

A data.frame of available Enrichr databases

Author(s)

Wajid Jawaid [email protected]

Examples

dbs <- listEnrichrDbs()
dbs <- listEnrichrDbs()

metadata

Description

Metadata associated with countData for testing purposes (functions DGE, plotPCA).

Usage

  data(metadata)
data(metadata)

Format

dataframe.

Details

A dataframe with 4 rows (sample names) and 3 columns (samplesID, condition A and B, therapy T1 and T2).

Value

A dataframe.

metaRNAseq function

Description

Perform Meta-Analysis of RNA-Seq Data

Usage

metaRNAseq(
  ind_deg,
  test_statistic = "fishercomb",
  BHth = 0.05,
  adjpval.t = 0.05,
  nrep = NULL,
  plot = FALSE
)
metaRNAseq(
  ind_deg,
  test_statistic = "fishercomb",
  BHth = 0.05,
  adjpval.t = 0.05,
  nrep = NULL,
  plot = FALSE
)

Arguments

`ind_deg`	List of indipendent named DEG dataframes with p-values to be combined.
`test_statistic`	p-value combination technique (inverse normal or Fisher): `fishercomb`, `invnorm`. By default: `fishercomb`.
`BHth`	Benjamini Hochberg threshold.
`adjpval.t`	threshold to represent as binary the Meta-Analysis output adjpval.
`nrep`	Vector of numbers of replicates used in each study to calculate the previous one-sided p-values.
`plot`	Logical. If TRUE plot histogram of pvalues. By default, the False Discovery Rate is controlled at 0.05.

Value

A list with DEindices of DEG at the chosen Benjamini Hochberg threshold, and TestStatistic, rawpval, adjpval, binaryadjpval vectors for differential expression in the meta-analysis.

Examples

## Not run: 
# Deseq2 output samples
DGE1 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(5.92, 9.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(10.22, 8.42),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
DGE2 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(3.92, 8.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(7.22, 5.81),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
# input list
ind_deg <- list(DEG1_df = DGE1, DEG2_df = DGE2)
# perform meta-analysis
comb_pval_df <- metaRNAseq(ind_deg, test_statistic = "invnorm", BHth = 0.05, nrep = c(2,2))
## End(Not run)
## Not run: 
# Deseq2 output samples
DGE1 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(5.92, 9.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(10.22, 8.42),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
DGE2 <- data.frame(GeneID = c("DLK1", "EPCAM"),
                 Mean_CPM_T = c(3.92, 8.91),
                 Mean_CPM_C = c(0.04, 0.03),
                 log2FoldChange = c(7.22, 5.81),
                 lfcSE = c(0.80, 0.48),
                 stat = c(12.68, 17.69),
                 pvalue = c(7.30135e-37, 4.37011e-70),
                 padj = c(1.49936e-35, 1.12976e-67),
                 row.names = c("DLK1", "EPCAM"))
# input list
ind_deg <- list(DEG1_df = DGE1, DEG2_df = DGE2)
# perform meta-analysis
comb_pval_df <- metaRNAseq(ind_deg, test_statistic = "invnorm", BHth = 0.05, nrep = c(2,2))
## End(Not run)

plotPCA function

Description

Plot PCA highlighting one or two data features

Usage

plotPCA(
  matrix,
  metadata,
  nTOP = 500,
  dims = c(1, 2),
  centering = TRUE,
  scaling = TRUE,
  color.by = NULL,
  shape.by = NULL,
  pt.size = 6,
  cols.use = NULL,
  shape.use = NULL,
  main = "PCA",
  label = FALSE,
  new.label = NULL
)
plotPCA(
  matrix,
  metadata,
  nTOP = 500,
  dims = c(1, 2),
  centering = TRUE,
  scaling = TRUE,
  color.by = NULL,
  shape.by = NULL,
  pt.size = 6,
  cols.use = NULL,
  shape.use = NULL,
  main = "PCA",
  label = FALSE,
  new.label = NULL
)

Arguments

`matrix`	Filtered count matrix in CPM or RPKM with gene on the row and sample ID on the column.
`metadata`	Sample metadata, row.names must be samples names.
`nTOP`	number of top genes to use for principal components, selected by highest row variance
`dims`	Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions
`centering`	Logical. If `TRUE` center PCs
`scaling`	Logical. If `TRUE` scales PCs
`color.by`	Name of one or more metadata columns to color point by.
`shape.by`	Name of one or more metadata columns to shape point by. If NULL, all points are circles $default$.
`pt.size`	Size of the points in the plot.
`cols.use`	Vector of colors, each color corresponds to an identity class. By default, ggplot assigns colors.
`shape.use`	Vector of shape, each shape corresponds to an identity class.
`main`	Plot title. Default = PCA.
`label`	Logical. If `TRUE` adds samples label. Default = FALSE.
`new.label`	If NULL, use the sample names as in metadata row.names. Otherwise you can specify new labels.

Value

PCA plot objec created by ggplot2, which can be assigned and further customized.

Examples

## Not run: 
# Simulation of bulk RNA data
countData <- matrix(floor(runif(10000, min=0, max=101)),ncol=4)
colnames(countData) <- paste("sample", seq_len(ncol(countData)), sep = "")
rownames(countData) <- paste("gene", seq_along(seq_len(10000/4)), sep = "")
metadata <- data.frame(samplesID = paste("sample", seq_len(ncol(countData)), sep = ""),
                     condition = factor(c("A","A","B","B")),
                     therapy = factor(c("T1","T2","T1","T2")))
row.names(metadata) <- metadata$samplesID
library(edgeR)
SurfR::plotPCA(matrix = cpm(countData),
        metadata = metadata,
        nTOP = 100,
        dims = c(1,2),
        color.by = "condition", shape.by = "therapy",
        label = FALSE, main = "PCA")
## End(Not run)
## Not run: 
# Simulation of bulk RNA data
countData <- matrix(floor(runif(10000, min=0, max=101)),ncol=4)
colnames(countData) <- paste("sample", seq_len(ncol(countData)), sep = "")
rownames(countData) <- paste("gene", seq_along(seq_len(10000/4)), sep = "")
metadata <- data.frame(samplesID = paste("sample", seq_len(ncol(countData)), sep = ""),
                     condition = factor(c("A","A","B","B")),
                     therapy = factor(c("T1","T2","T1","T2")))
row.names(metadata) <- metadata$samplesID
library(edgeR)
SurfR::plotPCA(matrix = cpm(countData),
        metadata = metadata,
        nTOP = 100,
        dims = c(1,2),
        color.by = "condition", shape.by = "therapy",
        label = FALSE, main = "PCA")
## End(Not run)

Set Enrichr Website

Description

Set Enrichr Website

Usage

setEnrichrSite(site)

setEnrichrSite(site)
setEnrichrSite(site)

setEnrichrSite(site)

Arguments

site

site requested

Details

Set Enrichr Website

Value

Changes Enrichr Website connection

Author(s)

Alexander Blume

Splot function

Description

Plot a barplot with features of Surface Protein

Usage

Splot(
  SurfaceProteins_df,
  group.by = "Membranome.Almen.main-class",
  cols.use = NULL,
  main = "Almen main class"
)
Splot(
  SurfaceProteins_df,
  group.by = "Membranome.Almen.main-class",
  cols.use = NULL,
  main = "Almen main class"
)

Arguments

`SurfaceProteins_df`	Output dataframe of Gene2SProtein function.
`group.by`	Name of columns to plot. Default = Membranome.Almen.main-class.
`cols.use`	Vector of colors, each color corresponds to an identity class. By default, ggplot assigns colors.
`main`	Plot title. Default = Almen main class.

Value

plot objec created by ggplot2, which can be assigned and further customized.

Examples

## Not run: 
GeneNames <- c("CIITA", "EPCAM", "DLK1", "CD24", "CDCP1", "LYVE1", "ABCD1", "VAMP1")
SurfaceProteins_df <- Gene2SProtein(GeneNames, input_type = "gene_name")
Splot(SurfaceProteins_df)
## End(Not run)
## Not run: 
GeneNames <- c("CIITA", "EPCAM", "DLK1", "CD24", "CDCP1", "LYVE1", "ABCD1", "VAMP1")
SurfaceProteins_df <- Gene2SProtein(GeneNames, input_type = "gene_name")
Splot(SurfaceProteins_df)
## End(Not run)

SVenn

Description

Venn diagram of common surface proteins overexpressed among up to 7 different studies

Usage

SVenn(
  S_list,
  cols.use = NULL,
  opacity = 0.5,
  output_intersectionFile = TRUE,
  filename = "intersection.xlsx"
)
SVenn(
  S_list,
  cols.use = NULL,
  opacity = 0.5,
  output_intersectionFile = TRUE,
  filename = "intersection.xlsx"
)

Arguments

`S_list`	A list of a maximum of 7 surface protein sets detected in different studies.
`cols.use`	Vector of colors, each color corresponds to a study. By default, ggplot assigns colors.
`opacity`	Degree of opacity for the colors specified with cols.use (less opacity, more transparency).
`output_intersectionFile`	logical. If TRUE (default) write an xlsx output of protein in the intersections.
`filename`	Name of the output file with the intersections.

Value

venn plot of common genes.

Examples

## Not run: 
S_list <- list(SP1 <- c("EPCAM", "CD24",  "DLK1",  "CDCP1", "LYVE1"),
              SP2 <- c("DLK1", "EPCAM", "EGFR", "UPK1A", "UPK2"))
SP <- SVenn(S_list, cols.use = c("pink", "yellow"), output_intersectionFile = FALSE)
## End(Not run)
## Not run: 
S_list <- list(SP1 <- c("EPCAM", "CD24",  "DLK1",  "CDCP1", "LYVE1"),
              SP2 <- c("DLK1", "EPCAM", "EGFR", "UPK1A", "UPK2"))
SP <- SVenn(S_list, cols.use = c("pink", "yellow"), output_intersectionFile = FALSE)
## End(Not run)

TCGA_download function

Description

Downloads count matrix data from TCGA

Usage

TCGA_download(
  project,
  whichcounts = "unstranded",
  save.matrix = FALSE,
  save.metadata = FALSE,
  barcodes = NULL
)
TCGA_download(
  project,
  whichcounts = "unstranded",
  save.matrix = FALSE,
  save.metadata = FALSE,
  barcodes = NULL
)

Arguments

`project`	Character. A valid project from TCGAbiolinks:::getGDCprojects()$project_id
`whichcounts`	Character. Counts data to use. Choose from: unstranded, stranded_first,stranded_second. By default, unstranded.
`save.matrix`	Logical. If `TRUE`, outputs a tsv file with the Matrix. By default, FALSE.
`save.metadata`	Logical. If `TRUE`, outputs a tsv file with the metadata. By default, FALSE.
`barcodes`	Character. A vector with names of the barcodes you want to download. If NULL (default) it downloads all the available barcodes in the project.

Value

A list containing the Matrix and the metadata.

Examples

## Not run: 
GBM_list_s1 <- TCGA_download(project="TCGA-GBM",
                             whichcounts = "unstranded",
                             save.matrix = FALSE, save.metadata = FALSE,
                             barcodes = c("TCGA-06-0878-01A-01R-1849-01"))
remove downloaded data from TCGA
unlink('GDCdata', recursive = TRUE, force = TRUE)
file.remove("MANIFEST.txt")

## End(Not run)
## Not run: 
GBM_list_s1 <- TCGA_download(project="TCGA-GBM",
                             whichcounts = "unstranded",
                             save.matrix = FALSE, save.metadata = FALSE,
                             barcodes = c("TCGA-06-0878-01A-01R-1849-01"))
remove downloaded data from TCGA
unlink('GDCdata', recursive = TRUE, force = TRUE)
file.remove("MANIFEST.txt")

## End(Not run)

Package 'SurfR'

Help Index

Annotate_SPID

Description

Usage

Arguments

Value

Warning

See Also

Examples

combine_fisher_invnorm

Description

Usage

Arguments

Value

See Also

Examples

countData

Description

Usage

Format

Details

Value

DGE function

Description

Usage

Arguments

Value

Examples

DownloadArchS4 function

Description

Usage

Arguments

Value

Warning

See Also

Examples

enrichedList

Description

Usage

Format

Details

Value

Enrichment function

Description

Usage

Arguments

Value

See Also

Examples

Enrichment_barplot

Description

Usage

Arguments

Value

See Also

Examples

Gene enrichment using Enrichr

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Download data from enrichr in the form of a named list - function from hypeR

Description

Usage

Arguments

Value

Examples

Gene2SProtein function

Description

Usage

Arguments

Value

Warning

See Also

Examples

GEOmetadata function