INTRODUCTION:
Welcome to the Molecular Subtyping Resource ( MouSR ):
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Introduction-Video Watch it here
This Molecular Subtyping Resource has been developed within the Molecular Pathology Research Group Dunne-Lab as part of the CRUK-funded ACRCelerate colorectal cancer pre-clinical accelerator programme.
The ACRCelerate project will bring together an european-wide consortium of basic and clinical scientists at the forefront of CRC research to interrogate a suite of state-of-the-art preclinical models. The overarching aim of ACRCelerate is to generate robust and reproducible preclinical data to de-risk future clinical trials via patient stratification. Specifically, the various models will be categorised into subtypes based on their gene activity so that treatments can be aimed at particular subgroups of patients. In doing so, the network will be able to accelerate the next generation of stratified trials for CRC through accurate disease subtype positioning. Using this preclinical testing platform, the consortium aims to accelerate the identification and development of new drugs for CRC.
This portal accepts uploads of either; text, csv files. The format of your Transcriptional Data Matrix RNA-seq-Read Count(Both Decimal and Integer numbers) should be similar to this table, with a size limit of up to 30 MB, length no restriction: If your Transcriptional Data Matrix file is not in the same orientation to the table below, you will need to transpose it. A transposing tool can be accessed via this link: Click here
| ID | symbol | KPNt1 | KPNt2 | KPNt3 | KPt1 | KPt2 | KPt3 | APNt1 | APNt2 | APNt3 | ...... |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSMUSG00000000001 | Gnai3 | 2792 | 867 | 3074 | 2096 | 4537 | 5269 | 5635 | 6359 | 9770 | ...... |
| ENSMUSG00000000028 | Cdc45 | 510 | 143 | 131 | 178 | 289 | 316 | 479 | 608 | 1065 | ...... |
| ENSMUSG00000000031 | H19 | 212 | 128 | 796 | 300 | 634 | 598 | 100495 | 2082 | 18134 | ...... |
| ENSMUSG00000000037 | Scml2 | 8 | 0 | 0 | 11 | 1 | 2 | 27 | 122 | 135 | ...... |
| ENSMUSG00000000049 | Apoh | 10 | 26 | 1 | 25 | 27 | 15 | 0 | 23 | 40 | ...... |
| ENSMUSG00000000056 | Narf | 925 | 176 | 730 | 569 | 850 | 830 | 1790 | 1632 | 2115 | ...... |
| ENSMUSG00000000058 | Cav2 | 2195 | 568 | 460 | 1480 | 2586 | 1211 | 303 | 457 | 605 | ... |
| ENSMUSG00000000078 | Klf6 | 3651 | 838 | 2349 | 4263 | 5373 | 5483 | 3156 | 5324 | 6919 | ...... |
| ENSMUSG00000000085 | Scmh1 | 764 | 301 | 491 | 1175 | 1099 | 601 | 1420 | 814 | 1634 | ...... |
| ENSMUSG00000000088 | Cox5a | 2127 | 693 | 1716 | 3127 | 2506 | 3221 | 1333 | 3077 | 4507 | ...... | ENSMUSG00000000093 | Tbx2 | 127 | 31 | 86 | 250 | 269 | 192 | 106 | 206 | 344 | ...... | ENSMUSG00000000094 | Tbx4 | 5 | 0 | 24 | 20 | 42 | 14 | 2028 | 143 | 209 | ...... | ENSMUSG00000000120 | Ngfr | 28 | 9 | 108 | 47 | 96 | 45 | 571 | 71 | 76 | ...... | ENSMUSG00000000125 | Wnt3 | 0 | 0 | 12 | 1 | 2 | 6 | 20 | 99 | 100 | ...... | ENSMUSG00000000126 | Wnt9a | 89 | 5 | 18 | 190 | 171 | 50 | 24 | 25 | 22 | ...... |
| ...... | ...... | ...... | ...... | ...... | ....... | ...... | ...... | ...... | ...... | ...... | ...... |
The format of your Sample Information and Labels Table should be similar to this, If your types are two words, such as Early tumor, you should write them as Early_tumor. Also please note the order of your Sample input should be exact same match with your Transcriptional Data Matrix columns.
| Group | Samples | Group-ID |
|---|---|---|
| KPN_tumour | KPNt1 | KPNt1 |
| KPN_tumour | KPNt2 | KPNt2 |
| KPN_tumour | KPNt3 | KPNt3 |
| KP_tumour | KPt1 | KPt1 |
| KP_tumour | KPt2 | KPt2 |
| KP_tumour | KPt3 | KPt3 |
| APN_tumour | APNt1 | APNt1 | APN_tumour | APNt2 | APNt2 | APN_tumour | APNt3 | APNt3 |
| ...... | ...... | ...... |
Source of inspirations:
DEApp
Input Data: Please Upload your Data
Data Input-Video Watch it here
Please note In Read count (Decimal Numbers) option, the Entire Differential Gene Expressions section is not available in the current version of the App.
Input 1: Transcriptional Data Matrix
Input 2: Sample Information and Labels
Input Details
Input Information Summary
Everything looks correct?
PCA 3D Analysis
PCA 3D-Video Watch it here
Plotly Modebar control: From left to right
1)Download plot as a selected format.
2) Zoom on selection. Click and hold your mouse button to select a region to zoom on.
3)Image panning.
4)Rotates the plot around its middle point in three-dimensional space.
5)Rotates the plot around its middle point while constraining one axis slightly.
6)Reset axis to the original axis ranges.
7)Reset to the last saved axis ranges.
8)Show closest data on hover -- this function is always on.
PCA 2D Analysis
Different Filtering Options
PCA 2D-Video Watch it here
PCA :A dimensionality-reduction method to assess quality and clustering characteristics of data sets. We used the prcomp function in the R stats package to perform PCA analysis.
PCA ReferencesPut plot option on PC1&PC2 first:
PCA Analysis 2D Plot
MDS 2D Analysis
Different Filtering Options
MDS 2D-Video Watch it here
MDS: Multidimensional Scaling is a dimension-reduction technique designed to project high dimensional data down to 2 dimensions while preserving relative distances between observations. We used the cmdscale function in the R stats package to perform MDS analysis.
MDS ReferencesMDS Analysis 2D Plot
ssGSEA Analysis
Analysis Filtering Criteria
Please pick a related Species according to your already Uploaded Transcriptional Data Matrix.
If your Uploaded data is Read count (Integer Numbers) and you didn't run the Differential Gene Expression Analysis yet, first you need to run Differential Gene Expression Analysis (Heatmap section) to get your data normalized then come back to run this analysis.
ssGSEA: The modification of standard gene set enrichment analysis (GSEA) specifically for single sample classification (ssGSEA) is performed using the GSVA package (version 1.32.0)
GSVAThe R package msigdbr (version 7.1.1) is used to retrieve mouse Hallmark and biological processes (GO_BP) gene sets and applied to samples.
msigdbrEnrichment score from ssGSEA demonstrate the degree to which the genes in a particular gene set are co-ordinately up- or down-regulated within a sample
Enrichment scoreAnalysis Results
ssGSEA Hallmark Plot
Gene Set Enrichment Analysis (GSEA) Filtering
Please Choose Two Groups
Please Note for GeneOntology option, Only the first 50 pathways having highest ES will be displayed here.
Download-Genelists
GSEA: Gene set enrichment analysis (GSEA) is a computational method, used to determine whether a priori defined set of genes display significant differences between two biological phenotypes. This is performed using the fgsea package (version 1.16.0) and enrichplot(version 1.10.0)
GSEAGSEA-Video Watch it here
Gene Set Enrichment Analysis Plot
CRIS-Classifier
Analysis Filtering Criteria
If your Uploaded data is a Read count (Integer Numbers) and you didn't run the Differential Gene Expression Analysis yet, first you need to run Differential Gene Expression Analysis (Heatmap section) to get your data normalized then come back to run this analysis.
Warning, you should have at least 20 samples to get reliable result using current classifer method.
CRIS-Classifier for Human option: This method performs CRC intrinsic subtypes (CRIS) classification based on nearest template prediction (NTP) method and CRIS template used in CMScaller R package.
CRIS-Classifier for Mouse option: In order to do CRC intrinsic subtypes (CRIS) classification in mouse, the human CRIS template, embedded in CMScalller, was converted to mouse orthologues using biomaRt, R package, and NTP method was applied to call CRIS subtypes in mouse tissues.
biomaRt / CMScaller / NTP / CRISCMS-Classifier
Analysis Filtering Criteria
If your Uploaded data is Read count (Integer Numbers) and you didn't run the Differential Gene Expression Analysis yet, first you need to run Differential Gene Expression Analysis (Heatmap section) to get your data normalized then come back to run this analysis.
Warning, you should have at least 20 samples to get reliable result using current classifer method.
CMS-Classifier for Human option: This method performs Consensus Molecular Subtypes (CMS) classification in human based on nearest template prediction (NTP) method and CMS template used in CMScaller R package.
CMS-Classifier for Mouse option: In order to do CMS classification in mouse, the human CMS template, embedded in CMScalller, is converted to mouse orthologues using biomaRt, R package, and intersected mouse genes across CMS subtypes were removed. Then, NTP method was applied to call CMS in mouse tissues.
biomaRt / CMScaller / NTPGene Expression Levels
Gene Expression Tables
Gene Expression Plot
CountPlot: click on a gene of interest from the table above. (The Table is only for this option)
Differential Gene Expression Analysis
Differential Categories comparison Analysis
Please select Maximum 5 groups for analysis in each Differential Category A and B
Please DO Not compare 2 same groups
Differential gene expression analysis: DESeq2 package (version 1.24.0) was used to identify genes which are differentially expressed between the two groups selected by user.
DESeq2Heatmap: The heatmaply package (version 1.1.1) is used to visualize the differentially expressed genes.
heatmaplyHeatmap-Video Watch it here
By clicking Create Plot button, the Heatmap plot will be created using default values as below. Then you can change these values and press it again to Update plot.
Top Significant Genes Heatmap
Plotly Modebar control: From left to right
1)Download plot as a selected format.
2) Zoom on selection. Click and hold you mouse button to select a region to zoom on.
3)Image panning.
4)Zoom IN.
5)Zoom OUT.
6)Reset axis to the original axis ranges.
7)Toggle spike lines on and off. Shows lines on a graph indicating the exact x-axis and y-axis.
Volcano Analysis
The Volcano plot is created using default values as below using Plotly. You can change these values and press Update plot.
Afterwards, you can pick any gene names (max=10), choose selected Genes option and then new plot will be created.
Volcano-Video Watch it here
Download Selected GenePlot
Plotly Modebar control: From left to right
1)Download plot as a selected format.
2) Zoom on selection. Click and hold you mouse button to select a region to zoom on.
3)Image panning.
4)Zoom IN.
5)Zoom OUT.
6)Reset axis to the original axis ranges.
7)Toggle spike lines on and off. Shows lines on a graph indicating the exact x-axis and y-axis.
Selected Gene List Heatmap Plot
Heatmap Selected Gene-Video Watch it here
Please Pick one option for your plots Format:
Please Click on Minimum Two Genes For Clustering:
Without-Thresholds:This Table is created without applying any Thresholds.
Default-Thresholds:This Table is created using Default values in previous section.
Selected Genes List Heatmap
Plotly Modebar control: From left to right
1)Download plot as a selected format.
2) Zoom on selection. Click and hold you mouse button to select a region to zoom on.
3)Image panning.
4)Zoom IN.
5)Zoom OUT.
6)Reset axis to the original axis ranges.
7)Toggle spike lines on and off. Shows lines on a graph indicating the exact x-axis and y-axis.
Estimate The Abundance of Microenvironment Cell Populations In Mouse/Human Tissue
mMcp-Video Watch it here
If your Uploaded data is Read count (Integer Numbers) and you didn't run the Differential Gene Expression Analysis yet, first you need to run Differential Gene Expression Analysis (Heatmap section) to get your data normalized then come back to run this analysis.
mMCP-counter: This function estimates the quantity of several immune and stromal cell populations from heterogeneous transcriptomic data, which has been modified for use specifically murine samples. mMCP-counter
MCP-counter: This function estimates the quantity of several immune and stromal cell populations from absolute abundance cell populations in heterogeneous transcriptomic data, which has been proposed for use specifically with human samples.
MCP-counterDownload