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:

• Registration and Login

• Data Inputs

• Creating a Study from a ICA Project

• Viewing Results and Navigating in 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.

• Select the samples to include in the analysis

• Click on + Create analysis

Select samples and Create 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

• click on the Run Analysis button

Define Default or Custom analysis

View Default Analysis Results

The analysis status will change to Complete when the analysis has finished.

• Click on the analysis tile to open results

The analysis opens to the analysis task graph.

The task graph shows the analysis pipeline

The first task run on the imported data, Split by feature type, is to split the data into different features: CRISPR Direct Capture(gRNAs) and Gene Expression. This is because having gRNAs included in standard analyses for both clustering and differential expression can lead to unexpected clustering results. The remainder of the pipeline follows the standard scRNA-seq workflow. Additional details for each task are available via the Single Cell walkthrough.

View Summary report

• Double-click the Summary report to open a Data viewer session.

Double-click to view the Summary report

The Summary report shows the Gene expression tab and CRISPR Direct Capture tab from the Perturb-seq samples; these can be toggled at the bottom of the data viewer session.

Gene expression tab

The Gene expression tab 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.

Gene expression data from the Perturb-seq samples

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-perturbation cells are in grey for this specific guide.

CRISPR Direct Capture tab

The CRISPR Direct Capture tab consists of the frequency of the total number of features (guides) in the cells as well as the frequency of each of the top 10 features with highest sum.

CRISPR Direct Capture data from the Perturb-seq samples