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The framework of IOBR
Building upon the existing functions of IOBR, IOBR2 introduces additional analysis and visualization
capabilities, with its comprehensive implementation and functionalities thoroughly detailed in the tutorial
(https://iobr.github.io/book/) with a complete analysis pipeline [6]. The current version, IOBR 2.0, encompasses
six functional modules: 1) Transcriptome data prepare module (pre-procession of transcriptome data, as well
as pertinent batch statistical analyses); 2) TME deconvolution and signature estimation module (estimation of
signature scores and identification of phenotype relevant signatures, along with decoding immune contexture);
3) TME interaction module
(clustering TME characteristics and analyzing receptor-lig
and interactions); 4)
Genome and TME interaction module (analysis of signature associated mutations) ; 5) TME data visualization
and Statistical analysis module (visual representation and statistical examination of TME data); 6) TME
modeling module (fast model construction and the assessment of model performance).
Transcriptome data prepare
Data preparation
In line with the preprocessing workflow of transcriptomic data, we have integrated a variety of functionalities
into the IOBR2. IOBR2 supports users in retaining genes based on the maximum or average values of
repeated gene expressions. Additionally, we have developed an annotation function for annotating expression
matrices. The annotation files in IOBR include anno_hug133plus2, anno_rnaseq, and anno_illumina,
corresponding to annotations for HG-U133 Plus 2.0 microarray probes, RNAseq annotation data, and Illumina
microarray probes, respectively.
In IOBR2, we have established a function for differentially expressed genes (DEGs) analysis between two
groups. This function supports two analytical methods, limma [12] and Desq2 [13]. The limma employs a linear
model to assess changes in gene expression, correcting for multiple testing differences using an empirical
Bayesian method. Originally designed for microarray data, its utility has been extended to small-sample
RNA-seq data analysis. Desq2, specifically designed for RNA-seq data analysis, uses a negative binomial
distribution to model gene expression data, applying either the Wald test or likelihood ratio test to each gene to
detect expression differences. Users can choose the appropriate method based on their data type and
research needs. Additionally, IOBR2 supports DEG analysis for more than 2 groups. It leverages the Seurat R
package to identify significant markers across multiple groups within the dataset [14]. The methods available
for comparison include bootstrap, delong, and venkatraman, offering a range of options for comprehensive
analysis.
Users can also rapidly convert gene expression count data into Transcripts Per Million (TPM) value. During
the annotation and conversion processes, additional operations such as merging annotation data with the
expression matrices, removing unnecessary columns, transforming rows and columns, and handling duplicates
based on the specified method can be simultaneously implemented. For sequencing data from different
sources or batches, users can use IOBR to examine batch effects in the data and perform batch correction.
Furthermore, we have built a filtering function rapid analysis of gene expression data and identification of
outliers in the dataset.
TME deconvolution and signature estimation
Signature Estimation
To enhance the characterization of the TME in cancer cells and to deepen our understanding of tumor
immunity and its functional states, we have developed an estimation function an estimation function for
user-generated signatures or 323 reported signatures enrolled in IOBR (Supplementary Table S1). The
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The copyright holder for thisthis version posted January 15, 2024. ; https://doi.org/10.1101/2024.01.13.575484doi: bioRxiv preprint