Perturb-seq
Illumina Connected Multiomics
Illumina Connected Multiomics (ICM) is available for further tertiary analysis of Illumina Single Cell Transcriptomics Perturb-seq data and other multiomic data.
Getting Started
Refer to the following links to the ICM user guide to get started with ICM:
Demo Data
Demo data that can be used to follow along with this walkthrough is found in the Connected Multiomics Demo Data repository. The dataset will be provided shortly at /Multiomics-Demo-Data/Perturb-seq. Each sample will need the following files as input (when specifying sample1 as the sample id):
sample1.scRNA.filtered.matrix.mtx.gz
sample1.scRNA.filtered.barcodes.tsv.gz
sample1.scRNA.filtered.features.tsv.gz
sample1.scRNA.feature_barcode_reference.csv
sample1.scRNA.positive_cell_guide_assignments.csv
Default Single Cell Perturb-seq Analysis
The default Perturb-seq analysis runs a pre-defined pipeline on each sample and presents analysis results in visualizations in a Data viewer.
Creating a Default Analysis
After adding data to a study, follow the following steps to create a Default analysis.
Click on + New Analysis
In the pop-up window, provide a name for the analysis
select Default: Illumina Single Cell Transcriptomics Perturb-seq from the dropdown as the Analysis Type
choose a sample group to be included in the analysis (all samples option is selected by default)
click on the Run Analysis button
View Default Analysis Results
When the analysis status is Complete, click on the analysis tile to open results. The analysis opens into a Data viewer that consists of one UMAP colored by cluster IDs, a biomarker table, a cell composition pie chart and the distributions of Total count and Expressed genes within different clusters.
The plots can be configured by selecting the Configure option from the toolbox on the left within each plot. Here is one example that converts the above UMAP to a feature plot by recoloring single cells with a ‘feature’: one of the gRNAs (PDCD10_4). In the feature plot, all cells in red carry the perturbation of the gRNA, while all the non-pertubation cells are in grey fot this specific guide.
View Default Analysis Pipeline
To view the default analysis pipeline, click on the Analysis name on breadcrumb on top of the Data viewer to go to Analyses page. The default analysis pipeline looks like this:
The first task(Split by feature type) running on the imported data is to split the data into different features: CRISPR Direct Capture(gRNAs) and Gene Expression. Because having gRNAs included in standard analyses for both clustering and differential expression can lead to unexpected clustering results. The rest of the pipeline is the same as regular scRNA-seq where users can read more details about the tasks after clicking the link.
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