TSA vs Control (DATA analysis and visualization)¶

Introduction¶

This notebook contains an analyis of a user-submitted RNA-seq dataset. created using BioJupies. For more information on BioJupies, please visit http://biojupies.cloud. If the notebook is not correctly displayed on your browser, please visit our Notebook Troubleshooting Guide.

Table of Contents¶

The notebook is divided into the following sections:

1. Load Dataset - Loads and previews the input dataset in the notebook environment.
2. PCA - Linear dimensionality reduction technique to visualize similarity between samples
3. Library Size Analysis - Analysis of readcount distribution for the samples within the dataset
4. Enrichr Links - Links to enrichment analysis results of the differentially expressed genes via Enrichr
5. Gene Ontology Enrichment Analysis - Identifies Gene Ontology terms which are enriched in the differentially expressed genes
6. Pathway Enrichment Analysis - Identifies biological pathways which are enriched in the differentially expressed genes
7. Transcription Factor Enrichment Analysis - Identifies transcription factors whose targets are enriched in the differentially expressed genes
8. Kinase Enrichment Analysis - Identifies protein kinases whose substrates are enriched in the differentially expressed genes
9. miRNA Enrichment Analysis - Identifies miRNAs whose targets are enriched in the differentially expressed genes
10. L1000CDS² Query - Identifies small molecules which mimic or reverse a given differential gene expression signature
11. L1000FWD Query - Projects signatures on a 2-dimensional visualization of the L1000 signature database

Results¶

1. Load Dataset¶

Here we upload a user-submitted dataset.

Table 1 | RNA-seq expression data. The table displays the first 5 rows of the quantified RNA-seq expression dataset. Rows represent genes, columns represent samples, and values show the number of mapped reads.

Table 2 | Sample metadata. The table displays the metadata associated with the samples in the RNA-seq dataset. Rows represent RNA-seq samples, columns represent metadata categories.

2. PCA¶

Principal Component Analysis (PCA) is a statistical technique used to identify global patterns in high-dimensional datasets. It is commonly used to explore the similarity of biological samples in RNA-seq datasets. To achieve this, gene expression values are transformed into Principal Components (PCs), a set of linearly uncorrelated features which represent the most relevant sources of variance in the data, and subsequently visualized using a scatter plot.

3. Library Size Analysis¶

In order to quantify gene expression in an RNA-seq dataset, reads generated from the sequencing step are mapped to a reference genome and subsequently aggregated into numeric gene counts. Due to experimental variations and random technical noise, samples in an RNA-seq datasets often have variable amounts of the total RNA. Library size analysis calculates and displays the total number of reads mapped for each sample in the RNA-seq dataset, facilitating the identification of outlying samples and the assessment of the overall quality of the data.

4. Enrichr Links¶

Enrichment analysis is a statistical procedure used to identify biological terms which are over-represented in a given gene set. These include signaling pathways, molecular functions, diseases, and a wide variety of other biological terms obtained by integrating prior knowledge of gene function from multiple resources. Enrichr is a web-based application which allows to perform enrichment analysis using a large collection of gene-set libraries and various interactive approaches to display enrichment results.

5. Gene Ontology Enrichment Analysis¶

Gene Ontology (GO) is a major bioinformatics initiative aimed at unifying the representation of gene attributes across all species. It contains a large collection of experimentally validated and predicted associations between genes and biological terms. This information can be leveraged by Enrichr to identify the biological processes, molecular functions and cellular components which are over-represented in the up-regulated and down-regulated genes identified by comparing two groups of samples.

6. Pathway Enrichment Analysis¶

Biological pathways are sequences of interactions between biochemical compounds which play a key role in determining cellular behavior. Databases such as KEGG, Reactome and WikiPathways contain a large number of associations between such pathways and genes. This information can be leveraged by Enrichr to identify the biological pathways which are over-represented in the up-regulated and down-regulated genes identified by comparing two groups of samples.

7. Transcription Factor Enrichment Analysis¶

Transcription Factors (TFs) are proteins involved in the transcriptional regulation of gene expression. Databases such as ChEA and ENCODE contain a large number of associations between TFs and their transcriptional targets. This information can be leveraged by Enrichr to identify the transcription factors whose targets are over-represented in the up-regulated and down-regulated genes identified by comparing two groups of samples.

8. Kinase Enrichment Analysis¶

Protein kinases are enzymes that modify other proteins by chemically adding phosphate groups. Databases such as KEA contain a large number of associations between kinases and their substrates. This information can be leveraged by Enrichr to identify the protein kinases whose substrates are over-represented in the up-regulated and down-regulated genes identified by comparing two groups of samples.

9. miRNA Enrichment Analysis¶

microRNAs (miRNAs) are small non-coding RNA molecules which play a key role in the post-transcriptional regulation of gene expression. Databases such as TargetScan and MiRTarBase contain a large number of associations between miRNAs and their targets. This information can be leveraged by Enrichr to identify the miRNAs whose targets are over-represented in the up-regulated and down-regulated genes identified by comparing two groups of samples.

10. L1000CDS² Query¶

L1000CDS2 is a web-based tool for querying gene expression signatures against signatures created from human cell lines treated with over 20,000 small molecules and drugs for the LINCS project. It is commonly used to identify small molecules which mimic or reverse the effects of a gene expression signature generated from a differential gene expression analysis.

11. L1000FWD Query¶

L1000FWD is a web-based tool for querying gene expression signatures against signatures created from human cell lines treated with over 20,000 small molecules and drugs for the LINCS project.

Methods¶

Data¶

Data Source¶

Dataset was user-submitted, compressed in an HDF5 data package, and uploaded to Google Cloud.

Data Normalization¶

logCPM¶

Raw counts were normalized to log10-Counts Per Million (logCPM) by dividing each column by the total sum of its counts, multiplying it by 10⁶, followed by the application of a log10-transform.

Signature Generation¶

The gene expression signature was generated by comparing gene expression levels between the control group and the experimental group using the limma R package (Ritchie et al., Nucleic Acids Research 2015), available on Bioconductor: http://bioconductor.org/packages/release/bioc/html/limma.html.

PCA¶

Principal Component Analysis was performed using the PCA function from in the sklearn Python module. Prior to performing PCA, the raw gene counts were normalized using the logCPM method, filtered by selecting the 2500 genes with most variable expression, and finally transformed using the Z-score method.

Library Size Analysis¶

Read counts were calculated by performing the sum for each column in the raw gene count matrix. Total counts were subsequently divided by 10⁶ and displayed as million reads.

Enrichr Links¶

The up-regulated and down-regulated gene sets were generated by extracting the 500 genes with the respectively highest and lowest values from the gene expression signature. The gene sets were subsequently submitted to Enrichr (Kuleshov et al., 2016), which is freely available at http://amp.pharm.mssm.edu/Enrichr/, using the gene set upload API. For more information on the methods used to generate the signature, see the Differential Gene Expression section.

Gene Ontology Enrichment Analysis¶

Enrichment results were generated by analyzing the up-regulated and down-regulated gene sets using Enrichr. The following libraries were used for the analysis: GO_Biological_Process_2018, GO_Molecular_Function_2018, GO_Cellular_Component_2018. Significant terms are determined by using a cut-off of p-value<0.1 after applying Benjamini-Hochberg correction. For more information on the methods used to perform the enrichment analysis, see the Enrichr section.

Pathway Enrichment Analysis¶

Enrichment results were generated by analyzing the up-regulated and down-regulated gene sets using Enrichr. The following libraries were used for the analysis: KEGG_2016, Reactome_2016, WikiPathways_2016. Significant terms are determined by using a cut-off of p-value<0.1 after applying Benjamini-Hochberg correction. For more information on the methods used to perform the enrichment analysis, see the Enrichr section.

Transcription Factor Enrichment Analysis¶

Enrichment results were generated by analyzing the up-regulated and down-regulated gene sets using Enrichr. The following libraries were used for the analysis: ChEA_2016, ENCODE_TF_ChIP-seq_2015, ARCHS4_TFs_Coexp. Significant results are determined by using a cut-off of p-value<0.1 after applying Benjamini-Hochberg correction. For more information on the methods used to perform the enrichment analysis, see the Enrichr section.

Kinase Enrichment Analysis¶

Enrichment results were generated by analyzing the up-regulated and down-regulated gene sets using Enrichr. The following libraries were used for the analysis: KEA_2015, ARCHS4_Kinases_Coexp. Significant results are determined by using a cut-off of p-value<0.1 after applying Benjamini-Hochberg correction. For more information on the methods used to perform the enrichment analysis, see the Enrichr section.

miRNA Enrichment Analysis¶

Enrichment results were generated by analyzing the up-regulated and down-regulated gene sets using Enrichr. The following libraries were used for the analysis: TargetScan_microRNA_2017, miRTarBase_2017. Significant results are determined by using a cut-off of p-value<0.1 after applying Benjamini-Hochberg correction. For more information on the methods used to perform the enrichment analysis, see the Enrichr section.

L1000CDS² Query¶

The L1000CDS2 analysis (Duan et al., 2016) was performed by submitting the top 2000 genes in the gene expression signature to the L1000CDS2 signature search API. For more information on the methods used to generate the signature, see the Differential Gene Expression section.

L1000FWD Query¶

The L1000FWD analysis (Wang et al., 2018) was performed by submitting the top 2000 genes in the gene expression signature to the L1000FWD signature search API. For more information on the methods used to generate the signature, see the Differential Gene Expression section.

References¶

Duan, Q., Reid, S.P., Clark, N.R., Wang, Z., Fernandez, N.F., Rouillard, A.D., Readhead, B., Tritsch, S.R., Hodos, R., Hafner, M., et al. (2016). L1000CDS2: LINCS L1000 characteristic direction signatures search engine. Npj Systems Biology and Applications 2. doi: https://doi.org/10.1038/npjsba.2016.15

Kuleshov, M.V., Jones, M.R., Rouillard, A.D., Fernandez, N.F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S.L., Jagodnik, K.M., Lachmann, A., et al. (2016). Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research 44, W90ÐW97. doi: https://dx.doi.org/10.1093/nar/gkw377

Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 2, 559Ð572. doi: https://doi.org/10.1080/14786440109462720

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47–e47. doi: https://doi.org/10.1093/nar/gkv007

Wang, Z., Lachmann, A., Keenan, A.B., and Ma’ayan, A. (2018). L1000FWD: fireworks visualization of drug-induced transcriptomic signatures. Bioinformatics. doi: https://doi.org/10.1093/bioinformatics/bty060