ExpressAnalystR

library(ExpressAnalystR)

#boolean FALSE indicates local mode (vs web mode);
Init.Data(FALSE);

# Set analysis type to single gene expression matrix
SetAnalType("onedata");

1.2 Read data table

dataName <- "endotoxin_data.txt";
dataSets <- ReadTabExpressData(dataName);

1.3 Annotate gene IDs to Entrez

For this step it is imortant to please select correct organism, data type (array or RNA-seq), id type and gene-level summary (mean, median, sum). For gene-level summary, microarray can use mean or median while RNA-seq needs to be sum.

dataSets <- PerformDataAnnot(dataName, "hsa", "array", "refseq", "mean");

Take a look at the mapped dataset by reading the dataset’s qs file using readDataset(fileName) function.

dataSet <- readDataset(dataName)
print(head(dataSet$data.anot[c(1:5),]))

Check diagnostics plots to look at overall data distribution, sample separation.

PlotDataBox(dataName, "qc_boxplot_", 72, "png");
PlotDataPCA(dataName, "qc_pca_", 72, "png");

Check your working directory for png images named qc_boxplot_dpi72.png and qc_pca_dpi72.png, open them.

Box plot shows that the expression distribution of samples are between around -4 to 12.5. This shows that the data has already been normalized.
PCA plot shows sample separation both between absent and present, and also, low and high. Depending of your experimental design, try to see if the samples are separated by the metadata of interest, it can also be used to see whether there are potentially mislabed sample.

1.4 Perform data filtering and normalization

No normalization need to be performed, PCA plot from previous step shows that the dataset is already normalized. Filter by variance (lower 15% removed) Filter by relative abundance (lower 4 percentile of average expression signal) Filter by count not applied (only for RNASeq data) Filter unannotated genes TRUE

dataSets <- PerformNormalization("dataName", "none", 15, 4,"true","false");

1.5 Prepare differential expression (DE) analysis

Selected metadata of interest, in this case we are interested in investigating the effect of presence of Estrogen Receptor (ER) vs absence. We are not setting secondary factor and blocking factor. After selecting metadata, compute design matrix and select DE analysis algorithm by running SetupDesignMatrix function. For microarray data, only limma can be used.

dataSets <- SetSelectedMetaInfo(dataName,"Treatment", "NA", F);
dataSets <- SetupDesignMatrix(dataName, "limma");

1.6 Perform DE analysis and check DE results

Fold change is log2 transformed. Adjusted P-value using False Discovery Rate (FDR) method.

dataSets <- PerformDEAnal(dataName, "custom", "control vs. LPS", "NA", "intonly");
dataSet <- readDataset(dataName);
print(head(dataSet$comp.res));

1.7 Visualize gene expression pattern of individual gene

PlotSelectedGene(dataName,"Gene_5111_","5111", dpi=72, format="png");

Check the resulting png image (Gene_5111_dpi72.png) in your working directory.

2.1 Load ExpressAnalystR library and initialize R objects

library(ExpressAnalystR)

#boolean FALSE indicates local mode (vs web mode);
Init.Data(FALSE);

# Set analysis type to meta-analysis
SetAnalType("metadata");

2.2 Read and process individual dataset

#Read dataset text file
dataSets <- ReadOmicsData("E-GEOD-25713.txt");
dataSets <- ReadOmicsData("E-GEOD-59276.txt");
dataSets <- ReadOmicsData("GSE69588.txt");

ReadMetaData("helminth_meta.csv");

dataSets <- SanityCheckData("E-GEOD-25713.txt");

#Map gene id to entrez id
dataSets <- AnnotateGeneData("E-GEOD-25713.txt", "mmu", "mean","entrez");

Visually inspect dataset using box plot (qc_boxplot_0_dpi72.png) and pca plot (qc_pca_0_dpi72.png).

PlotDataProfile("E-GEOD-25713.txt", "raw", "qc_boxplot_0_", "qc_pca_0_");

#Remove variables with more than 50% missing data
dataSets <- RemoveMissingPercent("E-GEOD-25713.txt", 0.5);

#Replace missing value with minimum values across dataset
dataSets <- ImputeMissingVar("E-GEOD-25713.txt", "min");

#Replace missing value with minimum values across dataset
dataSets <- FilteringData("E-GEOD-25713.txt","pct","0", "0");
dataSets <- NormalizeDataMetaMode("E-GEOD-25713.txt", "NA");

#process the other two datasets
dataSets <- SanityCheckData("E-GEOD-59276.txt");
dataSets <- AnnotateGeneData("E-GEOD-59276.txt", "mmu","mean", "entrez");
dataSets <- RemoveMissingPercent("E-GEOD-59276.txt", 0.5);
dataSets <- ImputeMissingVar("E-GEOD-59276.txt", "min");
dataSets <- FilteringData("E-GEOD-59276.txt","pct","0", "0");
dataSets <- NormalizeDataMetaMode("E-GEOD-59276.txt", "NA");

dataSets <- SanityCheckData("GSE69588.txt");
dataSets <- AnnotateGeneData("GSE69588.txt", "mmu","mean", "entrez");
dataSets <- RemoveMissingPercent("GSE69588.txt", 0.5);
dataSets <- ImputeMissingVar("GSE69588.txt", "min");
dataSets <- FilteringData("GSE69588.txt","pct","0", "0");
dataSets <- NormalizeDataMetaMode("GSE69588.txt", "NA");

2.3 Perform data integrity check (compatibility)

CheckMetaDataIntegrity();

2.4 Check diagnostic plot and perform batch correction

PlotMetaPCA("qc_meta_pca_","72", "png", "");

There is clear signs of batch effect. The samples from same dataset are largely clustered together. To remove the batch effect, we need to run comBat batch correction algorithm

#Apply batch effect correction
dataSets <- PerformBatchCorrection();

#Check the result 
PlotMetaPCA("qc_meta_pca_afterBatch_","72", "png", "");

Here is the result after batch correction.

#### 2.5 Perform DE analysis

CovariateScatter.Anal("E-GEOD-25713.txt", "E-GEOD-25713.txt_covariate_0_.png", "png", "Condition", "CONTROL", "NA" , 0.05,"fdr", "anova")
CovariateScatter.Anal("E-GEOD-59276.txt", "E-GEOD-59276.txt_covariate_0_.png", "png", "Condition", "CONTROL", "NA" , 0.05,"fdr", "anova")
CovariateScatter.Anal("GSE69588.txt", "GSE69588.txt_covariate_0_.png", "png", "Condition", "CONTROL", "NA" , 0.05,"fdr", "anova")

2.5 Perform statistical meta-analysis using combine p-values method

PerformPvalCombination("fisher", 0.05)

2.6 View result tables

analSet <- readSet(analSet, "analSet");
print(head(analSet$meta.mat));
#
#       CombinedTstat CombinedPval
#16854         89.093   2.7728e-14
#246256        99.964   2.7728e-14
#105855        94.751   2.7728e-14
#19241        105.030   2.7728e-14
#319169        94.339   2.7728e-14
#16819        100.880   2.7728e-14

For a more detailed table containing additionally log fold change and p-values of features for individual dataset, please check this csv file meta_sig_genes_metap.csv, it is also generated in your working directory.