Overview

RGenEDA is designed to provide a streamlined, unified and reproducible framework for exploratory data analysis across multiple omics data types. This vignette introduces the key components of the RGenEDA package using bulk RNA-seq data from the paper Genomewide binding of transcription factor Snail1 in triple-negative breast cancer cell (Maturi, et al. 2018). Raw counts and metadata were obtained from The Gene Expression Atlas under ENA:ERP019920, E-MTAB-5244.

The data has be pre-wrangled but the standard deseq2 framework will be applied here to demonstrate the functionality of RGenEDA.

The paper explores epithelial cell line HS578T. The authors have introduced a knock-out of the Snail1 transcription factor and compare these with wild-type (WT) HS578T cells.

Load and inspect the data
Define metadata
Processing and normalization
Create a GenEDA object
Count distrubutions across samples
Sample Eucliden distances with hierarchical clustering
Identify highly variable genes
Principal component analysis
Extract and visualize PCA results
Explore Eigen vectors of individual PCs
Correlate PCs with metadata
Explore DEGs

Load and inspect the data

Data has been included in this package for your convenience. It can be easily accessed using the following commands. Here, we have one key variable of interest (Genotype) for simplicity sake (though your experiment may have many. RGenEDA can handle any number of covariates.)

# Load the counts and metadata and extract
data("Snail1KO")
counts <- Snail1KO[["rawCounts"]]
metadata <- Snail1KO[["metadata"]]

# Sanity check
head(counts)[1:6, 1:6]
#>          ERR1736465 ERR1736466 ERR1736467 ERR1736468 ERR1736469 ERR1736470
#> TSPAN6          434        829        334        779        578        602
#> TNMD              0          0          0          0          0          0
#> DPM1           2202       3461       1627       4241       3676       3946
#> SCYL3            45         72         31        119         69         92
#> C1orf112        320        558        235        516        468        417
#> FGR               0         10          0          0          0          0
head(metadata)
#>                Sample          Disease Genotype        CellLine_CellType
#> ERR1736465 ERR1736465 breast carcinoma       WT HS578T - epithelial cell
#> ERR1736466 ERR1736466 breast carcinoma       WT HS578T - epithelial cell
#> ERR1736467 ERR1736467 breast carcinoma       WT HS578T - epithelial cell
#> ERR1736468 ERR1736468 breast carcinoma Snai1_KO HS578T - epithelial cell
#> ERR1736469 ERR1736469 breast carcinoma Snai1_KO HS578T - epithelial cell
#> ERR1736470 ERR1736470 breast carcinoma Snai1_KO HS578T - epithelial cell

It is helpful to establish color palettes early on in an exploratory analysis to keep figures consistent. RGenEDA uses the list of named vectors convention for creating color vectors for plotting functions.

# Color palettes for plotting
colorList <- list(
    Genotype = c("WT" = "steelblue3", "Snai1_KO" = "firebrick3")
)

Processing and normalization

The standard DESeq2 workflow can now be applied. The end goal is to obtain normalized counts and differential expression results. Lowly expressed genes are filtered out before running DESeq2.

# Ensure tables are not scrambled
all(colnames(counts) == rownames(metadata))
#> [1] TRUE

dds <- DESeqDataSetFromMatrix(
    countData = counts,
    colData = metadata,
    design = ~Genotype
)
#> Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
#> design formula are characters, converting to factors

# Set reference levels
dds$Genotype <- relevel(dds$Genotype, ref = "WT")

# Pre-filter: keep genes with at least 10 counts in  at least 3 samples
keep <- rowSums(counts(dds) >= 10) >= 3
dds <- dds[keep, ]

# Run DESeq2
dds <- DESeq(dds)

# Rlog transform the data and extract normalized matrix
rld <- rlog(dds)
mat <- assay(rld)

Create a GenEDA object

With the normalized counts and metadata prepared, a GenEDA object can be initialized. This object will store all components of your analysis, starting with normalized data and metadata (bare minimum requirements).

# Factor metadata
metadata$Genotype <- factor(metadata$Genotype, levels = c("WT", "Snai1_KO"))

# Initialize GenEDA object with normalized counts and metadata
obj <- GenEDA(
    normalized = mat,
    metadata = metadata
)

# View object summary
obj
#> geneda object
#>   features: 11247
#>   samples:  6
#>   HVGs: 0
#>   DimReduction: 0
#>   counts: NULL
#>   DEGs: none

You will see that the object also contains slots for Highly Variable Genes (HVGs), DimReduction (PCA, etc..) and Differentially Expressed Genes (DEGs). These will be populated shortly with RGenEDA

Creating a GenEDA object from DESeq2 or SummarizedExperiment

If your data are already in a SummarizedExperiment or DESeqDataSet, use as.geneda() instead. It carries over the raw counts, normalized values, and colData in one step. Here, the rlog values computed above are used as the normalized data. If normalized is not supplied, as.geneda() computes a variance stabilizing transformation (transform = "vst") or log2 CPM (transform = "logcpm") from the counts.

obj_from_dds <- as.geneda(dds, normalized = rld)
obj_from_dds
#> geneda object
#>   features: 11247
#>   samples:  6
#>   HVGs: 0
#>   DimReduction: 0
#>   counts: present
#>   DEGs: none

Count distributions across samples

To visualize normalized count distributions across samples, the PlotCountDist() function can be used. This is a quick and helpful way to visualize effectiveness of normalization, as the overall distributions should be similar across samples. Samples with very low or very high overall counts compared to others might indicate problematic samples, technical artifacts, or batch effect. This function returns a ggplot2 object to facilitate any additional customization.

PlotCountDist(obj, split_by = "Genotype")

Sample Eucliden distances with hierarchical clustering

To visualize replicate similarity, Euclidean distances between samples can be calculated and plotted as a pheatmap heatmap using the PlotDistances() function. Darker colors indicate higher similarity, while lighter colors represent dissimilar samples. This provides a quick assessment of replicate quality and metadata features that drive clustering. To save pheatmap objects within RGenEDA, we can use the GenSave() function, which is similar in function to ggsave().

hm <- PlotDistances(
    obj,
    meta_cols = c("Genotype"),
    palettes = colorList,
    return = "plot"
)
hm$heatmap

# GenSave(hm, "/path/to/EuclidenDistance_Heatmap.png", width = 6, height = 8)

Identify highly variable genes

Next, genes are ranked by decreasing variance to find highly variable genes (HVGs) which most-likely drive biological differences. The full variance profile curve for all genes profiled with plotHVGVariance() as a means to pick a meaningful number of HVGs to retain for calculating principal components in the next section.

#----- Plot variance profile
PlotHVGVariance(obj)

Based on the curve, 2,000 genes seems sufficient in capturing the majority of variation. These genes can be extracted and retained in the HVGs slot with FindVariableFeatures()

#----- Add HVGs to object
obj <- FindVariableFeatures(obj, 2000)

# == Access HVGs with the accessor function
head(HVGs(obj))
#> [1] "THY1"   "CCL2"   "SSX1"   "AKR1C2" "GPX1"   "MYL12B"

Principal component analysis

Using the identified HVGs, principal components can be calculated using RunPCA() This function stores PCA results in the DimReduction slot, including:

$Scores (sample scores)
$Eigenvectors (gene contributions)
$percent_var (Percent variance explained per component, up to PC5)

If FindVariableFeatures() was not ran beforehand, RunPCA will calculate HVGs by default with 2000 features. This argument can be overriden directly using the nfeatures argument.

obj <- RunPCA(obj)
#> Calculating principal components from top 2000 HVGs
#> Percent variations:
#> PC1: 85.43 %
#> PC2: 7.07 %
#> PC3: 3.89 %
#> PC4: 2.28 %
#> PC5: 1.33 %
#> PC6: 0 %

# Inspect PCA outputs with the DimReduction() accessor
pca <- DimReduction(obj)
head(pca$Scores)
#>                  PC1       PC2         PC3         PC4          PC5
#> ERR1736465 -12.26994  4.841985  1.95261904  2.98311973  0.770544259
#> ERR1736466 -14.27872 -2.406553 -5.26248029 -0.01697393  0.313289330
#> ERR1736467 -13.35504 -1.694899  3.44737576 -3.03463682 -1.042987566
#> ERR1736468  12.70944 -4.145799  1.47958222  0.20911815  2.703775110
#> ERR1736469  12.51669 -2.370634  0.09842185  2.20330061 -2.739470641
#> ERR1736470  14.67757  5.775900 -1.71551858 -2.34392774 -0.005150492
#>                     PC6
#> ERR1736465 1.950523e-14
#> ERR1736466 1.503658e-14
#> ERR1736467 1.832909e-14
#> ERR1736468 2.047668e-14
#> ERR1736469 2.359593e-14
#> ERR1736470 2.190609e-14
head(pca$Eigenvectors)
#>                 PC1          PC2           PC3          PC4          PC5
#> THY1    0.150266440 -0.022809065  1.105371e-05 -0.006329718 -0.028030018
#> CCL2    0.135706807 -0.025658201  7.037349e-03  0.003838021 -0.040018651
#> SSX1   -0.130106138  0.036993763  1.894730e-02 -0.007723523  0.001874006
#> AKR1C2 -0.116614608  0.003828601  3.034353e-03 -0.019723927 -0.002551927
#> GPX1    0.111588141  0.002460421 -3.352860e-02 -0.039545468 -0.007872470
#> MYL12B  0.005002684  0.178455620 -2.807260e-01  0.269866847 -0.311091450
#>                PC6
#> THY1   -0.24459419
#> CCL2    0.03889988
#> SSX1   -0.09512116
#> AKR1C2 -0.04373226
#> GPX1   -0.01327995
#> MYL12B -0.01492479
head(pca$percent_var)
#>       PC1       PC2       PC3       PC4       PC5       PC6 
#> "85.43 %"  "7.07 %"  "3.89 %"  "2.28 %"  "1.33 %"     "0 %"


# Visualize a scree plot
PlotScree(obj)

Extract and visualize PCA results

PCA results merged with metadata can easily be extracted using ExtractPCA() which enables flexible plotting. For convenience, PlotPCA() allows a quick visualization which can also be further customized with ggplot2.

pcaDF <- ExtractPCA(obj)
head(pcaDF)
#>                  PC1       PC2         PC3         PC4          PC5
#> ERR1736465 -12.26994  4.841985  1.95261904  2.98311973  0.770544259
#> ERR1736466 -14.27872 -2.406553 -5.26248029 -0.01697393  0.313289330
#> ERR1736467 -13.35504 -1.694899  3.44737576 -3.03463682 -1.042987566
#> ERR1736468  12.70944 -4.145799  1.47958222  0.20911815  2.703775110
#> ERR1736469  12.51669 -2.370634  0.09842185  2.20330061 -2.739470641
#> ERR1736470  14.67757  5.775900 -1.71551858 -2.34392774 -0.005150492
#>                     PC6     Sample          Disease Genotype
#> ERR1736465 1.950523e-14 ERR1736465 breast carcinoma       WT
#> ERR1736466 1.503658e-14 ERR1736466 breast carcinoma       WT
#> ERR1736467 1.832909e-14 ERR1736467 breast carcinoma       WT
#> ERR1736468 2.047668e-14 ERR1736468 breast carcinoma Snai1_KO
#> ERR1736469 2.359593e-14 ERR1736469 breast carcinoma Snai1_KO
#> ERR1736470 2.190609e-14 ERR1736470 breast carcinoma Snai1_KO
#>                   CellLine_CellType
#> ERR1736465 HS578T - epithelial cell
#> ERR1736466 HS578T - epithelial cell
#> ERR1736467 HS578T - epithelial cell
#> ERR1736468 HS578T - epithelial cell
#> ERR1736469 HS578T - epithelial cell
#> ERR1736470 HS578T - epithelial cell

# Plot PCA
PlotPCA(
    object = obj,
    x = 1,
    y = 2,
    color_by = "Genotype",
    colors = c("WT" = "steelblue3", "Snai1_KO" = "firebrick3")
)
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the RGenEDA package.
#>   Please report the issue at
#>   <https://github.com/mikemartinez99/RGenEDA/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

Explore Eigen vectors of individual PCs

The individual Eigenvectors (genes) that comprise a particular component of interest can be extracted and their Z-scaled normalized expression visualized as a heatmap annotated by the percent variation explained using extractEigen() and PlotEigenHeatmap(). This can again identify sample-to-sample differences.

pc1_eigen <- extractEigen(
    object = obj,
    component = "PC1"
)
head(pc1_eigen)
#>     Gene  EigenVector      PctVar
#> 1   THY1  0.150266440 2.258000305
#> 2   CCL2  0.135706807 1.841633753
#> 3   SSX1 -0.130106138 1.692760703
#> 4 AKR1C2 -0.116614608 1.359896690
#> 5   GPX1  0.111588141 1.245191327
#> 6 MYL12B  0.005002684 0.002502684


hm2 <- PlotEigenHeatmap(obj,
    pc = "PC1",
    n = 25,
    annotate_by = "Genotype",
    annotate_colors = colorList
)
hm2$heatmap


# GenSave(hm2, "/path/to/EuclidenDistance_Heatmap.png", width = 6, height = 8)

Correlate PCs with metadata

To interpret principal components, individual PCs can be correlated with sample metadata using PlotEigenCorr(). This function computes Pearson correlations and displays them as a heatmap, helping to reveal which metadata features are most associated with major axes of variation. Categorical variables with two levels (such as Genotype) use Pearson correlation; categorical variables with more than two levels (such as batch) are summarized by the unsigned multiple correlation R from a one-way linear model, so results do not depend on how levels are labelled. Variables that cannot be tested, such as columns with a single value or a unique value per sample (here, Disease, CellLine_CellType, and Sample), are dropped. This function returns a list of 4 elements:

$cor_matrix (Pearson correlation values, or multiple correlation R for variables with more than two levels)
$pval_matrix (Associated correlation p-values)
$stars (asterisk representations of p-values)
$plot (Eigencorr plot, as a ggplot2 object, which can be saved with ggsave)

Note: PlotOrdCorr() can be used for microbiome data as it correlates metadata features with NMDS beta values rather than PCs.

ec <- PlotEigenCorr(obj, num_pcs = 5)
ec$plot

Explore DEGs

Differentially expressed genes can now be stored in the RGenEDA object. RGenEDA was designed to directly work with DEG tables derived from DESeq2 and contain the column names “baseMean”, “log2FoldChange” and “padj”. DEG tables from other packages can be used with some minor dataframe manipulation.

Note: multiple DEG assays can be appended to the DEGs slot by passing an assay name in the SetDEGs() command (for example, raw DESeq2 results and Shrunk DESeq2 results.)

To get a quick diagnosis of the differential expression results, a histogram of un-corrected pvalues can be plotted using PlotPValHist().

res <- results(dds) |>
    as.data.frame()

# Set a new DEG assay
obj <- SetDEGs(
    object = obj,
    deg_table = res,
    assay = "unfiltered"
)

# P value histogram
PlotPValHist(obj,
    assay = "unfiltered",
    alpha = 0.05
)

To quickly summarize the DEG results with different thresholds, run SummarizeDEGs(). This function can take multiple assays if needed and outputs results in a tabular format.

DEG assays can be filtered and directly saved as a new assay in the DEGs slot using FilterDEGs()

# Summarize DEGs
SummarizeDEGs(obj,
    alpha = 0.05,
    lfc1 = 1,
    lfc2 = 2
)
#>                                 unfiltered
#> padj<=0.05 & log2FC >=1 (Up)           656
#> padj<=0.05 & log2FC <=-1 (Down)        542
#> padj<=0.05 & log2FC >=2 (Up)           228
#> padj<=0.05 & log2FC <=-2 (Down)        181

# Filter the DEG assay and save as a new one
obj <- FilterDEGs(
    object = obj,
    assay = "unfiltered",
    alpha = 0.05,
    l2fc = 1,
    saveAssay = "padj05_lfc1"
)

# Grab results with accessor function
nrow(DEGs(object = obj, assay = "unfiltered"))
#> [1] 11247
nrow(DEGs(object = obj, assay = "padj05_lfc1"))
#> [1] 1198

Basic DEG visualizations plots such as MA plots and volcano plots can be plotted using PlotMA() and PlotVolcano() “Num” and “Den” arguments refer to numerator level and denominator level (reference level of experiment, in our case, “WT”)

PlotMA(obj,
    assay = "unfiltered",
    alpha = 0.05,
    l2fc = 1
)
#> Scale for size is already present.
#> Adding another scale for size, which will replace the existing scale.
#> Warning: Removed 5 rows containing missing values or values outside the scale range
#> (`geom_point()`).

Or a volcano plot by specifying numerator and denominator (denominator is your comparison reference level).

PlotVolcano(obj,
    assay = "unfiltered",
    alpha = 0.05,
    l2fc = 1,
    den = "WT",
    num = "Snai1_KO",
    title = "Example Volcano"
)

To get a glance at everything at once, we can plot a differential expression dashboard with DEDashboard()

#> Scale for size is already present.
#> Adding another scale for size, which will replace the existing scale.
#> Warning: Removed 5 rows containing missing values or values outside the scale range
#> (`geom_point()`).

To explore whether a differentially expressed gene (DEG) is also a highly variable gene (HVG), the intersect of these two vectors can be taken to obtain highly variable DEGs (hvDEGs), which can be useful for applications such as gene-set variation analysis (GSVA). To obtain this, the FindHVDEGs() function can be used. The direction of fold change to intersect with HVGs can be either “positive”, or “negative”, which returns a vector of genes, or “both” which contains a list with $positive, $negative, and $both slots.

FindHVDEGs(obj,
    assay = "padj05_lfc1",
    direction = "negative"
)
#> 332 -log2FC hvDEGs found.
#>   [1] "CAMKK1"       "ACSM3"        "TMEM98"       "DLX6"         "MAP3K9"      
#>   [6] "PRSS21"       "FUZ"          "CEP68"        "ERCC1"        "BID"         
#>  [11] "ABCC2"        "DEF6"         "LRRC7"        "VCAN"         "SPATA7"      
#>  [16] "CAPG"         "MPC1"         "GUCY1B1"      "EYA2"         "ANKRD44"     
#>  [21] "PRKCQ"        "MTHFD2"       "TRO"          "NAV3"         "MAST4"       
#>  [26] "GAL"          "CLTCL1"       "DGCR2"        "MGAT4A"       "VASH1"       
#>  [31] "ST6GAL1"      "ZNF532"       "TUBE1"        "MLH1"         "ARHGEF1"     
#>  [36] "SLC1A3"       "OPHN1"        "CARMIL1"      "IGSF9B"       "CXCL2"       
#>  [41] "MMP2"         "TESC"         "DLL3"         "DSP"          "SYDE2"       
#>  [46] "PCSK5"        "TRMT2A"       "SMARCB1"      "PPM1F"        "SRRD"        
#>  [51] "SEPTIN3"      "SYNGR1"       "PCK2"         "BMP7"         "ISM1"        
#>  [56] "TRIB3"        "FERMT1"       "ZNF516"       "KLHL4"        "CPPED1"      
#>  [61] "FZD3"         "JPH1"         "GDAP1"        "TUBB4A"       "FCGRT"       
#>  [66] "NOVA2"        "OLFM2"        "TIMM50"       "DMAC2"        "CARD8"       
#>  [71] "TMEM59L"      "SCN1B"        "CADM4"        "PTN"          "GIMAP2"      
#>  [76] "TRIM14"       "KANK1"        "COL1A1"       "CLEC2B"       "PARP11"      
#>  [81] "VDR"          "ADGRD1"       "PHC1"         "SOBP"         "PHACTR2"     
#>  [86] "SEMA5A"       "GHR"          "PCDHB5"       "PCDHB6"       "CDH6"        
#>  [91] "SLC12A7"      "EHHADH"       "PODXL2"       "ARHGEF26"     "EFEMP1"      
#>  [96] "IL18R1"       "ID2"          "NOL10"        "SLC25A12"     "SDC1"        
#> [101] "ORC4"         "PAPPA2"       "RHOU"         "CTH"          "AGMAT"       
#> [106] "ADGRL2"       "IFT46"        "MREG"         "UBE3D"        "TGIF2"       
#> [111] "ABCG2"        "LGALSL"       "BCL11A"       "PCDHB12"      "RDH10"       
#> [116] "PDZRN3"       "KIAA1549"     "BAZ2B"        "PFKFB2"       "DAW1"        
#> [121] "PTGIS"        "SNAI1"        "ZNF576"       "BMP4"         "EML2"        
#> [126] "TMX4"         "ID1"          "SSX1"         "GNAZ"         "PODXL"       
#> [131] "CHAC1"        "KIF1A"        "AFDN"         "ZNF227"       "THOC6"       
#> [136] "PER2"         "TTC9"         "PSAT1"        "MSI1"         "ACVR1B"      
#> [141] "OS9"          "MAP7"         "NHSL1"        "NIBAN1"       "EDNRB"       
#> [146] "GATA4"        "CTSV"         "KIAA0319"     "FLOT1"        "SULF1"       
#> [151] "FXYD6"        "GPAT3"        "BMPR1B"       "LEF1"         "CLSTN3"      
#> [156] "INHBE"        "RAB15"        "STON2"        "NUDT7"        "CDH13"       
#> [161] "GAREM1"       "TTYH2"        "IGFBP4"       "GEMIN7"       "WTIP"        
#> [166] "CRABP2"       "ETNK2"        "ALDH1L1"      "OTULINL"      "TNFAIP8"     
#> [171] "FAXC"         "SDK1"         "ZNF214"       "NCAM1"        "FEZ1"        
#> [176] "MPP7"         "CDH8"         "IL18"         "AKR1C2"       "TDO2"        
#> [181] "SPOCK1"       "PRDM8"        "BMP6"         "PTPRD"        "KCTD15"      
#> [186] "ANKFN1"       "KIF5A"        "TSPAN7"       "CD109"        "MMP14"       
#> [191] "ARSL"         "TMSB15A"      "TSPAN33"      "ZNF208"       "FAM171A2"    
#> [196] "PDPN"         "CADM3"        "EIF4E3"       "RPL22L1"      "HHIP"        
#> [201] "TBX20"        "STEAP1"       "ELAPOR2"      "ZNF704"       "CHMP4C"      
#> [206] "ADCY1"        "SLC16A9"      "PKNOX2"       "JCAD"         "JAM3"        
#> [211] "STXBP4"       "PPFIBP2"      "CYB5R2"       "PRTG"         "RIMKLB"      
#> [216] "NNMT"         "GPRC5B"       "OR51B5"       "OR51I1"       "CYP2S1"      
#> [221] "NXN"          "DDIT4"        "COA6"         "ZNF30"        "STXBP6"      
#> [226] "CXCL8"        "ZFPM2"        "SMAD1"        "KRT8"         "HOXB9"       
#> [231] "CHD7"         "LURAP1"       "DLK2"         "LRRC34"       "CYP4F11"     
#> [236] "TLN2"         "ZNF556"       "CALB2"        "SLCO2A1"      "BRSK2"       
#> [241] "NUPR1"        "OR51B6"       "EID2"         "EID2B"        "SYNE3"       
#> [246] "NCKAP5"       "MAGEF1"       "SOX12"        "HNRNPA0"      "AP3S1"       
#> [251] "ZNF223"       "ARL14"        "NLRP11"       "F2R"          "ZNF322"      
#> [256] "OR52D1"       "FDX1P1"       "PLCB1"        "CADM1"        "OR51B4"      
#> [261] "TMEM121B"     "SCN5A"        "THAP7"        "OR51M1"       "SLC24A3"     
#> [266] "MRPL40"       "PRAME"        "KCNQ5"        "KRT10"        "ZNF397"      
#> [271] "AKR1C1"       "ZKSCAN4"      "ZNF70"        "PABIR1"       "OR51I2"      
#> [276] "FAM78B"       "ZFP92"        "AKR1C3"       "PPP1R26"      "POTEF"       
#> [281] "TCF4"         "ZNF512B"      "MVB12B"       "ZSCAN26"      "STMN3"       
#> [286] "COL13A1"      "TUBA3C"       "MDM4"         "FAM169A"      "ZNF830"      
#> [291] "SCAMP5"       "ZNF521"       "SFMBT2"       "DZIP3"        "OR2L2"       
#> [296] "GPANK1"       "MRPS18B"      "PRR3"         "TMEM200C"     "GPSM3"       
#> [301] "EMP2"         "RTL10"        "RABGAP1L-IT1" "RFPL4A"       "SNHG14"      
#> [306] "APCDD1L-DT"   "YWHAEP1"      "RNASEH1-AS1"  "ZSCAN31"      "FOXD2-AS1"   
#> [311] "POU5F1P4"     "PRKCQ-AS1"    "GUSBP2"       "PEG10"        "N4BP2L2"     
#> [316] "LINC00504"    "RBBP4P1"      "CHCHD10"      "PCDHB17P"     "PECAM1"      
#> [321] "GNAO1-AS1"    "SH3GL1P1"     "LIN37"        "NCBP2AS2"     "CAHM"        
#> [326] "ZNF595"       "FAM106A"      "H2BC3"        "H2AC16"       "H4C1"        
#> [331] "OR51B2"       "H3C3"

Session information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ggtext_0.2.0                lubridate_1.9.5            
#>  [3] forcats_1.0.1               stringr_1.6.0              
#>  [5] dplyr_1.2.1                 purrr_1.2.2                
#>  [7] readr_2.2.0                 tidyr_1.3.2                
#>  [9] tibble_3.3.1                ggplot2_4.0.3              
#> [11] tidyverse_2.0.0             pasilla_1.41.0             
#> [13] DEXSeq_1.59.1               AnnotationDbi_1.75.2       
#> [15] DESeq2_1.53.6               SummarizedExperiment_1.43.0
#> [17] GenomicRanges_1.65.4        Seqinfo_1.3.2              
#> [19] IRanges_2.47.5              S4Vectors_0.51.10          
#> [21] MatrixGenerics_1.25.0       matrixStats_1.5.0          
#> [23] Biobase_2.73.2              BiocGenerics_0.59.12       
#> [25] generics_0.1.4              BiocParallel_1.47.0        
#> [27] RGenEDA_0.99.0              rmarkdown_2.32             
#> 
#> loaded via a namespace (and not attached):
#>  [1] DBI_1.3.0            bitops_1.1-0         httr2_1.3.0         
#>  [4] permute_0.9-10       biomaRt_2.69.5       rlang_1.3.0         
#>  [7] magrittr_2.0.5       otel_0.2.0           compiler_4.6.1      
#> [10] RSQLite_3.53.3       mgcv_1.9-4           png_0.1-9           
#> [13] vctrs_0.7.3          pkgconfig_2.0.3      crayon_1.5.3        
#> [16] fastmap_1.2.0        dbplyr_2.6.0         XVector_0.53.0      
#> [19] labeling_0.4.3       Rsamtools_2.29.0     tzdb_0.5.0          
#> [22] bit_4.6.0            xfun_0.62            cachem_1.1.0        
#> [25] jsonlite_2.0.0       progress_1.2.3       blob_1.3.0          
#> [28] DelayedArray_0.39.8  parallel_4.6.1       prettyunits_1.2.0   
#> [31] cluster_2.1.8.3      R6_2.6.1             bslib_0.12.0        
#> [34] stringi_1.8.9        RColorBrewer_1.1-3   genefilter_1.95.0   
#> [37] jquerylib_0.1.4      Rcpp_1.1.2           knitr_1.52          
#> [40] BiocBaseUtils_1.15.1 timechange_0.4.0     Matrix_1.7-6        
#> [43] splines_4.6.1        tidyselect_1.2.1     abind_1.4-8         
#> [46] yaml_2.3.12          vegan_2.7-6          codetools_0.2-20    
#> [49] hwriter_1.3.2.1      curl_8.1.0           lattice_0.23-1      
#> [52] withr_3.0.3          KEGGREST_1.53.6      S7_0.2.2            
#> [55] evaluate_1.0.5       survival_3.8-12      BiocFileCache_3.3.0 
#> [58] xml2_1.6.0           Biostrings_2.81.9    pillar_1.11.1       
#> [61] filelock_1.0.3       hms_1.1.4            scales_1.4.0        
#> [64] xtable_1.8-8         glue_1.8.1           pheatmap_1.0.13     
#> [67] maketools_1.3.2      tools_4.6.1          sys_3.4.3           
#> [70] annotate_1.91.0      locfit_1.5-9.12      XML_3.99-0.25       
#> [73] buildtools_1.0.0     grid_4.6.1           nlme_3.1-171        
#> [76] patchwork_1.3.2      cli_3.6.6            S4Arrays_1.13.2     
#> [79] gtable_0.3.6         sass_0.4.10          digest_0.6.39       
#> [82] SparseArray_1.13.4   ggrepel_0.9.8        geneplotter_1.91.0  
#> [85] farver_2.1.2         memoise_2.0.1        htmltools_0.5.9     
#> [88] lifecycle_1.0.5      httr_1.4.9           statmod_1.5.2       
#> [91] gridtext_0.1.6       bit64_4.8.6          MASS_7.3-66

- Overview
- Session information

RGenEDA Tutorial