# Install required packages. ----------------------------------------------
install.packages(c("devtools", "BiocManager"), dependencies = TRUE)
BiocManager::install("apeglm")
# install Hotgenes -------------------------------------------------------
# set repos
options(repos = c(
CRAN = "https://cran.rstudio.com/",
BiocManager::repositories()[1:4]
))
devtools::install_github("pfizer-opensource/Open-Hotgenes",
dependencies = TRUE)The contents of this repository are provided under the Apache v2.0 license as laid out in the LICENSE file.
Hotgenes is an R package built to remove bottlenecks from omics collaborations. It contains a modular shiny application with a wide range of flexible tools (PCA, GSEA, GSVA, and more!) designed for brainstorming omics data interpretations among collaborators. The functions supporting these tools are accessible outside of the shiny application, which can be used for adhoc queries or for building custom pipelines.
Science will win! Simplified omics data analysis means more winning.
Omics analysis with fewer bottlenecks.
You should fork this repository and submit a pull-request.
Richard Virgen-Slane
-
Sample metadata
-
Normalized expression data
-
Feature-aliases
-
Feature-associated statistics
-
Auxiliary assays
Omics data and be directly imported from DESeq2 (via HotgenesDEseq2()) or limma (via Hotgeneslimma()).
For others platforms, check out the HotgenesUniversal() function.
library("airway")
library("DESeq2")
library(Hotgenes)
# load the data
data("airway")
se <- airway
# in case you wanted to include aliases for your genes
# requires a "Feature" column that contains gene names in expression matrix
dbCon <- org.Hs.eg.db::org.Hs.eg_dbconn()
sqlQuery <- "SELECT * FROM ENSEMBL, gene_info WHERE ENSEMBL._id == gene_info._id;"
ensembl_Symbol <- DBI::dbGetQuery(dbCon, sqlQuery) %>%
dplyr::select(c("Feature" = "ensembl_id", "symbol")) %>%
tibble::tibble()
ensembl_Symbol## # A tibble: 45,689 × 2
## Feature symbol
## <chr> <chr>
## 1 ENSG00000121410 A1BG
## 2 ENSG00000175899 A2M
## 3 ENSG00000291190 A2MP1
## 4 ENSG00000171428 NAT1
## 5 ENSG00000156006 NAT2
## # ℹ 45,684 more rows
# prepare DESeq2 object and model -----------------------------------------
ddsSE <- DESeq2::DESeqDataSet(se, design = ~ cell + dex)
ddsSE## class: DESeqDataSet
## dim: 63677 8
## metadata(2): '' version
## assays(1): counts
## rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492 ENSG00000273493
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample
# run DESeq2 analysis
dds <- DESeq2::DESeq(ddsSE)## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
# Convert to Hotgenes object
Hotgenes_airway <- Hotgenes::HotgenesDEseq2(
DEseq2_object = dds,
lfcShrink_type = "apeglm",
# optional
Mapper = ensembl_Symbol,
ExpressionData = "vsd" )## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
## Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
## sequence count data: removing the noise and preserving large differences.
## Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
# shiny Hotgenes ----------------------------------------------------------
if(FALSE){
# switch FALSE to TRUE
Hotgenes::Shiny_Hotgenes(Hotgenes_airway)
} require(Hotgenes)
# incase you wanted to include aliases for your genes
# requires a "Feature" column that contains gene names in expression matrix
dbCon <- org.Hs.eg.db::org.Hs.eg_dbconn()
sqlQuery <-
"SELECT * FROM ENSEMBL, gene_info WHERE ENSEMBL._id == gene_info._id;"
ensembl_Symbol <- DBI::dbGetQuery(dbCon, sqlQuery) %>%
dplyr::select(c("Feature" = "symbol", "ensembl_id"))
# Hotgeneslimma -----------------------------------------------------------
require(DESeq2)
dds_con_dir <- system.file("extdata",
"dds_con.Rdata",
package = "Hotgenes",
mustWork = TRUE)
load(dds_con_dir)
# Example Expression data and coldata
cts <- counts(dds_con) %>% as.data.frame()
Design <- colData(dds_con) %>%
base::as.data.frame() %>%
dplyr::select_if(is.factor) %>%
dplyr::mutate(Time = as.numeric(levels(.data$Hrs))[.data$Hrs])
# Create DGEList object
# and calculate normalization factors
d0 <- edgeR::DGEList(cts)
d0 <- edgeR::calcNormFactors(d0)
# Filter low-expressed genes
# disabled in this example
if (FALSE) {
cutoff <- 1
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # number of genes lef
}
d <- d0
# make a model.matrix
model_Matrix <- model.matrix( ~ sh * Hrs,
data = Design)
# voom
vm_exp <- limma::voom(d, model_Matrix)
# make fit
fit <- limma::lmFit(vm_exp, model_Matrix)
fit <- limma::eBayes(fit)
# Get alternative exps
alt_Exp <- list(counts = data.matrix(d0))
# Convert to Hotgenes Object
fit_Hotgenes <- Hotgeneslimma(
limmafit = fit,
coldata = Design,
Expression = vm_exp,
Expression_name = "logCPM",
Exps_list = alt_Exp,
Mapper = ensembl_Symbol
)library(Hotgenes)
# load example data -------------------------------------------------------
dds_Hotgenes_dir <- system.file("extdata",
paste0("dds_Hotgenes", ".RDS"),
package = "Hotgenes",
mustWork = TRUE
)
htgs <- readRDS(dds_Hotgenes_dir)
# preparing data -----------------------------------------------------------
# Getting example named list of DE statistics
NewDE <- Output_DE_(htgs, as_list = TRUE, padj_cut = 1)
# Getting example named list of normalized data
NormlData <- Normalized_Data_(htgs)
# Getting example coldata
ExpColdata <- coldata_(htgs)
# Getting example original data object used for DE analysis
# This example was generated from DESeq2
OrigDEObj <- O_(htgs)
OrigDEObj %>% class()## [1] "DESeqDataSet"
## attr(,"package")
## [1] "DESeq2"
# Getting example design matrix
DE_design <- designMatrix_(htgs)
# Getting example mapper
MapperDF <- Mapper_(htgs)
# Converting example objects to hotgenes
Hotgenes_Object <- HotgenesUniversal(
Output_DE = NewDE,
Normalized_Expression = NormlData,
coldata = ExpColdata,
Original_Object = OrigDEObj,
designMatrix = DE_design,
Mapper = MapperDF
)calling a Hotgenes object returns a summary table
library(Hotgenes)
fit_Hotgenes## class: Hotgenes
## Original class/package: EList/limma
##
## Differential expression (default thresholds):
## |contrast | total|
## |:--------------|-----:|
## |Hrs_2_vs_0 | 45|
## |Hrs_6_vs_0 | 71|
## |sh_EWS_vs_Ctrl | 51|
## |shEWS.Hrs2 | 8|
## |shEWS.Hrs6 | 1|
##
## Available feature mapping: Feature, ensembl_id
## ExpressionSlots: logCPM, counts
## Total auxiliary assays: 2
## Total samples: 12
Sample metadata
coldata_(fit_Hotgenes)## sh Bio_Rep Hrs Time
## shCON_0hrs_1 Ctrl 1 0 0
## shCON_0hrs_2 Ctrl 2 0 0
## shCON_2hrs_1 Ctrl 1 2 2
## shCON_2hrs_2 Ctrl 2 2 2
## shCON_6hrs_1 Ctrl 1 6 6
## shCON_6hrs_2 Ctrl 2 6 6
## shEWS_0hrs_1 EWS 1 0 0
## shEWS_0hrs_2 EWS 2 0 0
## shEWS_2hrs_1 EWS 1 2 2
## shEWS_2hrs_2 EWS 2 2 2
## shEWS_6hrs_1 EWS 1 6 6
## shEWS_6hrs_2 EWS 2 6 6
Normalized expression data
ExpressionData_(fit_Hotgenes)[c(1:3), c(1:3)]## shCON_0hrs_1 shCON_0hrs_2 shCON_2hrs_1
## AGER 5.699205 6.356261 6.030818
## ALOX12 3.377277 3.372749 3.472823
## ALOX15 5.699205 5.795961 5.512351
Available aliases
Mapper_(fit_Hotgenes) %>% head()## # A tibble: 6 × 2
## Feature ensembl_id
## <chr> <chr>
## 1 AGER ENSG00000204305
## 2 AGER ENSG00000230514
## 3 AGER ENSG00000229058
## 4 AGER ENSG00000231268
## 5 AGER ENSG00000237405
## # ℹ 1 more row
Available auxiliary_assays
auxiliary_assays_(fit_Hotgenes)## assay1 assay2
## shCON_0hrs_1 -1.4805676 -0.77956651
## shCON_0hrs_2 1.5771695 0.01195176
## shCON_2hrs_1 -0.9567445 -0.15241624
## shCON_2hrs_2 -0.9200052 -0.70346425
## shCON_6hrs_1 -1.9976421 1.18887916
## shCON_6hrs_2 -0.2722960 0.34051227
## shEWS_0hrs_1 -0.3153487 0.50696817
## shEWS_0hrs_2 -0.6282552 -0.29330515
## shEWS_2hrs_1 -0.1064639 0.22364142
## shEWS_2hrs_2 0.4280148 2.00720146
## shEWS_6hrs_1 -0.7777196 1.01197912
## shEWS_6hrs_2 -1.2938823 -0.30245925
DE(fit_Hotgenes, Topn = 3)## $Hrs_2_vs_0
## # A tibble: 3 × 11
## Feature contrast_dir baseMean log2FoldChange FC stat pvalue padj t B ensembl_id
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 IL6 Hrs_2_vs_0_up 10.6 2.52 5.72 31.1 4.06e-24 1.04e-21 31.1 45.1 ENSG00000136244
## 2 CXCL8 Hrs_2_vs_0_up 10.9 3.51 11.4 26.0 6.99e-22 8.92e-20 26.0 40.0 ENSG00000169429
## 3 TNFAIP3 Hrs_2_vs_0_up 10.5 2.14 4.42 24.9 2.21e-21 1.88e-19 24.9 38.8 ENSG00000118503
##
## $Hrs_6_vs_0
## # A tibble: 3 × 11
## Feature contrast_dir baseMean log2FoldChange FC stat pvalue padj t B ensembl_id
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 CXCL8 Hrs_6_vs_0_up 10.9 3.56 11.8 26.4 4.21e-22 1.07e-19 26.4 40.5 ENSG00000169429
## 2 CXCL1 Hrs_6_vs_0_up 11.3 2.35 5.09 24.5 3.71e-21 4.73e-19 24.5 38.2 ENSG00000163739
## 3 CCL2 Hrs_6_vs_0_up 12.5 1.25 2.37 23.0 2.14e-20 1.56e-18 23.0 36.3 ENSG00000108691
##
## $sh_EWS_vs_Ctrl
## # A tibble: 3 × 11
## Feature contrast_dir baseMean log2FoldChange FC stat pvalue padj t B ensembl_id
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 C1R sh_EWS_vs_Ctrl_down 13.5 -0.856 0.553 -16.7 1.33e-16 3.39e-14 -16.7 27.7 ENSG00000159403
## 2 C1R sh_EWS_vs_Ctrl_down 13.5 -0.856 0.553 -16.7 1.33e-16 3.39e-14 -16.7 27.7 ENSG00000288512
## 3 CCL2 sh_EWS_vs_Ctrl_down 12.5 -1.00 0.500 -15.8 5.92e-16 7.55e-14 -15.8 26.4 ENSG00000108691
##
## $shEWS.Hrs2
## # A tibble: 3 × 11
## Feature contrast_dir baseMean log2FoldChange FC stat pvalue padj t B ensembl_id
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 TNFAIP3 shEWS.Hrs2_down 10.5 -0.565 0.676 -4.69 0.0000584 0.0149 -4.69 1.77 ENSG00000118503
## 2 CXCL3 shEWS.Hrs2_down 7.90 -1.05 0.482 -3.94 0.000457 0.0233 -3.94 -0.0744 ENSG00000163734
## 3 FOS shEWS.Hrs2_up 8.98 0.645 1.56 4.14 0.000267 0.0233 4.14 0.398 ENSG00000170345
##
## $shEWS.Hrs6
## # A tibble: 1 × 11
## Feature contrast_dir baseMean log2FoldChange FC stat pvalue padj t B ensembl_id
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 FOS shEWS.Hrs6_up 8.98 0.826 1.77 4.53 0.0000892 0.0227 4.53 1.12 ENSG00000170345
all comparisons
DEPlot(fit_Hotgenes, .log2FoldChange = 0, padj_cut = 0.1)
Check for a feature of interest
# Check a feature across comparisons
DEPlot(fit_Hotgenes, hotList = "CSF1", .log2FoldChange = 0, padj_cut = 0.1)
VPlot(fit_Hotgenes,
.log2FoldChange = 1, padj_cut = 0.1,
contrasts = "sh_EWS_vs_Ctrl")
# Venn Diagram plot
fit_Hotgenes %>%
DE(
Report = "Features",
contrasts = c("sh_EWS_vs_Ctrl", "Hrs_2_vs_0", "Hrs_6_vs_0"),
.log2FoldChange = 0, padj_cut = 0.1
) %>%
Venn_Report()## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
## $vennD
##
## $Intsect
## $Intsect$Hrs_2_vs_0
## [1] "NFE2L2" "KEAP1" "PDGFA" "HDAC4" "OXER1" "GAPDH" "MEF2C"
##
## $Intsect$Hrs_6_vs_0
## [1] "CXCL5" "STAT2" "NR3C1" "MAP3K1" "HSPB2" "MAPK8" "DAXX" "MKNK1" "MAP2K6" "IL1B" "BCL6" "TLR3" "GRB2"
## [14] "IL6R" "IL15" "CREB1" "IL1RN" "RELA" "IFIT3" "MAP3K5" "TGFB3" "TGFB2" "IL1A" "CCL20" "PGK1" "MAPK3"
##
## $Intsect$sh_EWS_vs_Ctrl
## [1] "HIF1A" "C3" "RAC1" "GNB1" "TUBB" "BCL2L1" "CSF1" "PTGER3" "ROCK2" "MX2" "HMGN1" "CLTC" "GNAQ"
## [14] "LY96" "CD40" "CFD" "HRAS" "RHOA" "HPRT1" "TCF4" "MX1" "OAS2" "LTB4R2"
##
## $Intsect$`Hrs_2_vs_0:Hrs_6_vs_0`
## [1] "CXCL8" "TNFAIP3" "CXCL1" "IL11" "PTGS2" "DDIT3" "IFIT2" "TGFBR1" "MAFF" "CXCR4" "MAFK"
## [12] "PTGFR" "FOS" "MYC" "RIPK2" "IL2" "MAFG" "CSF2" "TWIST2" "IFIT1" "FLT1"
##
## $Intsect$`sh_EWS_vs_Ctrl:Hrs_2_vs_0`
## [1] "HMGB2" "MAP3K9" "CEBPB" "IRF1"
##
## $Intsect$`sh_EWS_vs_Ctrl:Hrs_6_vs_0`
## [1] "C1R" "C1S" "MMP3" "CXCL6" "STAT1" "PTGS1" "HMGB1" "MASP1" "TRAF2" "IFI44" "CCL7"
##
## $Intsect$`sh_EWS_vs_Ctrl:Hrs_2_vs_0:Hrs_6_vs_0`
## [1] "CCL2" "IL1R1" "MEF2D" "JUN" "BIRC2" "CXCL3" "MEF2A" "CXCL2" "IL6" "NFKB1"
## [11] "SMAD7" "MAPKAPK2" "RELB"
##
##
## $Names
## [1] "Hrs_2_vs_0:Hrs_6_vs_0" "sh_EWS_vs_Ctrl:Hrs_2_vs_0" "sh_EWS_vs_Ctrl:Hrs_6_vs_0"
## [4] "sh_EWS_vs_Ctrl:Hrs_2_vs_0:Hrs_6_vs_0"
# run PCA
# set contrast of choice and metadata variables
FactoOutput <- FactoWrapper(fit_Hotgenes,
contrasts = "Hrs_6_vs_0",
coldata_ids = c("Hrs", "sh"),
biplot = FALSE
)# plot
FactoOutput$res_PPI_pa_1
# getting HCPC details
FactoOutput$TopTibble # Feature## # A tibble: 88 × 9
## Cluster Interpretation Feature v.test `Mean in category` `Overall mean` `sd in category` `Overall sd` p.value
## <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Cluster has elevated IFIT2 2.91 10.3 9.89 0.100 0.339 0.00362
## 2 1 Cluster has elevated DDIT3 2.75 10.4 10.1 0.0402 0.231 0.00593
## 3 1 Cluster has elevated MAP3K1 2.72 10.0 9.75 0.0537 0.216 0.00660
## 4 1 Cluster has elevated PTGFR 2.65 10.4 10.1 0.0489 0.285 0.00797
## 5 1 Cluster has elevated PGK1 2.63 15.3 15.2 0.0224 0.0484 0.00851
## # ℹ 83 more rows
FactoOutput$TopGroups # TopGroups## # A tibble: 1 × 8
## Cluster Interpretation Category `Cla/Mod` `Mod/Cla` Global p.value v.test
## <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Cluster has elevated Hrs=Hrs_0 100 100 33.3 0.00202 3.09
# Having metadata embedded with expression data means easier plotting
yvar <- c("CSF2", "IL6")
xvar <- "Hrs"
ExpsPlot(fit_Hotgenes,
xVar = xvar,
yVar = yvar,
fill = "Hrs",
boxplot = TRUE
)
# Subset data on the fly
ExpsPlot(fit_Hotgenes,
xVar = xvar,
yVar = yvar,
filter_eval = Hrs != 2,
fill = "Hrs",
boxplot = TRUE
)
# Reorder data on the fly
ExpsPlot(fit_Hotgenes,
xVar = xvar,
yVar = yvar,
boxplot = TRUE,
fill = "Hrs",
named_levels = list(Feature = "IL6",
Hrs = c("6", "2", "0"))
)
DEphe(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
Topn = 5,
cellheight = 10,
cellwidth = 8,
annotation_colors = coldata_palettes(fit_Hotgenes),
annotations = c("Hrs", "sh"))
# change labels to ensembl_id
DEphe(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
label_by = "ensembl_id",
Topn = 5,
cellheight = 10,
cellwidth = 8,
annotation_colors = coldata_palettes(fit_Hotgenes),
annotations = c("Hrs", "sh"))
# get geneset
H_paths <- msigdbr_wrapper(
species = "human",
set = "CP:KEGG_MEDICUS",
gene_col = "gene_symbol"
)
# Get ranks
InputRanks <- fit_Hotgenes %>%
DE(
Report = "Ranks",
contrasts = "Hrs_6_vs_0",
Rank_name = "Feature",
padj_cut = 0.1
)
# fgsea wrapper --------
Out_GSEA <- fgsea_(
Ranks = InputRanks,
pathways = H_paths,
nproc = 1,
minSize = 5,
maxSize = Inf
)## [1] "Hrs_6_vs_0"
## | | | 0% | |======================================================== | 50% | |=================================================================================================================| 100%
# Get details for all
Out_GSEA %>%
fgsea_Results(
contrasts = "Hrs_6_vs_0",
padj_cut = 0.2,
mode = "D"
)## $Hrs_6_vs_0
## # A tibble: 3 × 9
## pathway pval padj log2err ES NES size leadingEdge sign_NES
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list> <dbl>
## 1 kegg_medicus_pathogen_sars_cov_2_s_to_angii_at1r_nox2_signa… 0.0116 0.0809 0.381 0.767 1.61 7 <chr [4]> 1
## 2 kegg_medicus_pathogen_kshv_vgpcr_to_gnb_g_pi3k_jnk_signalin… 0.0412 0.128 0.322 0.776 1.50 5 <chr [4]> 1
## 3 kegg_medicus_pathogen_hsv_gd_to_hvem_nfkb_signaling_pathway 0.0549 0.128 0.228 0.739 1.43 5 <chr [4]> 1
# Or for one
Out_GSEA %>%
fgsea_Results(
contrasts = "Hrs_6_vs_0",
padj_cut = 0.2,
mode = "leadingEdge"
)## $Hrs_6_vs_0
## $Hrs_6_vs_0$kegg_medicus_pathogen_sars_cov_2_s_to_angii_at1r_nox2_signaling_pathway
## [1] "CXCL8" "CCL2" "IL6" "NFKB1"
##
## $Hrs_6_vs_0$kegg_medicus_pathogen_kshv_vgpcr_to_gnb_g_pi3k_jnk_signaling_pathway
## [1] "CXCL8" "IL6" "NFKB1" "MAPK8"
##
## $Hrs_6_vs_0$kegg_medicus_pathogen_hsv_gd_to_hvem_nfkb_signaling_pathway
## [1] "CCL2" "NFKB1" "BIRC2" "TRAF2"
# Generate a summary plot
Out_GSEA %>%
GSEA_Plots(
contrasts = "Hrs_6_vs_0",
padj_cut = 0.2,
width = 30,
Topn = 2
)## $Hrs_6_vs_0
# plotEnrichment_
plotEnrichment_(
Out_GSEA, "Hrs_6_vs_0",
"kegg_medicus_pathogen_sars_cov_2_s_to_angii_at1r_nox2_signaling_pathway"
)## leading edge genes for kegg_medicus_pathogen_sars_cov_2_s_to_angii_at1r_nox2_signaling_pathway
## c("CXCL8", "CCL2", "IL6", "NFKB1")
choice_set <- "CP:KEGG_MEDICUS"
choice_id <- "gene_symbol"
gsList <- msigdbr_wrapper(
species = "human",
set = choice_set,
gene_col = choice_id
)
# HotgeneSets -------------------------------------------------------------
HotgeneSets_out <- HotgeneSets(
Hotgenes = fit_Hotgenes,
geneSets = gsList,
kcdf = "Gaussian",
method = "ssgsea",
minSize = 2,
maxSize = Inf
)## [1] "using Feature col"
## [1] "building mapper"
## Estimating ssGSEA scores for 235 gene sets.
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."
## | | | 0% | |========= | 8% | |=================== | 17% | |============================ | 25% | |====================================== | 33% | |=============================================== | 42% | |======================================================== | 50% | |================================================================== | 58% | |=========================================================================== | 67% | |===================================================================================== | 75% | |============================================================================================== | 83% | |======================================================================================================== | 92% | |=================================================================================================================| 100%
##
## [1] "Normalizing..."
## using geneset weights
HotgeneSets_out## class: Hotgenes
## Original class/package: EList/limma
##
## Differential expression (default thresholds):
## |contrast | total|
## |:--------------|-----:|
## |Hrs_2_vs_0 | 151|
## |Hrs_6_vs_0 | 181|
## |sh_EWS_vs_Ctrl | 43|
## |shEWS.Hrs2 | 54|
## |shEWS.Hrs6 | 42|
##
## Available feature mapping: Feature, original_features, size
## ExpressionSlots: ssgsea
## Total auxiliary assays: 2
## Total samples: 12
# store your Hotgenes objects in a named list
# The Shiny_Hotgenes() will let you toggle between objects
if(FALSE){
List_Hotgenes <- list(HotgeneSets_out = HotgeneSets_out,
fit_Hotgenes = fit_Hotgenes)
Shiny_Hotgenes(List_Hotgenes)
}See manual for details!
if(FALSE){
Shiny_Hotgenes(dds_Hotgenes)
}library(Hotgenes)
help(package="Hotgenes")Please note that the Hotgenes project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.