scRNA-seq Differential Expression
Introduction
This dataset consists of gene expression data of human pancreatic islet cells of both healthy and diabetic donors. The aim of the study is to discover differences in gene expression between healthy and diabetic cells.
Dataset description
Organism: Homo sapiens
Instrument: Illumina HiSeq 2500
Library construction: SMARTer v1
Layout: Single-end 75 pb
Number of cells: 1,068
Publication
Lawlor N, George J, Bolisetty M, et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Research. 2017 Feb;27(2):208-222. DOI: 10.1101/gr.212720.116. PMID: 27864352; PMCID: PMC5287227.
Bioinformatic Analysis
1.- scRNA-Seq Clustering
Application
Input
Parameters
Input Type: Count Table File
Count Table File: counts_islets.txt
Column Separator: TAB
NA Values: Assume Zero Values
Minimum Cells: 4
Set Minimum Counts Cutoff: false
Set Maximum Counts Cutoff: true
Maximum Counts: 5000000
Set Minimum Features Cutoff: false
Set Maximum Features Cutoff: false
Filter by % of Mitochondrial Genes: false
Multi Sample Analysis: true
Experimental Design File: exp_design_islets.txt
Condition: disease
N. Dimensions for Integration: 20
K Anchor: 5
K Filter: 200
K Score: 30
K Weight: 100
Normalize Data: true
Normalization Method: Regularized Negative Binomial Regression
High Variable Features: 3000
Scale Data: false
Center Data: true
Regress Out Mitochondrial Genes: false
Regress Out Cell Cycle Genes: false
Principal Components: 50
Define Dimensions by: Manual
Number of Dimensions: 12
k-value: 20
Resolution: 0.8
Point's Minimum Distance: 0.3
Point's Spread: 1.0
Execution Time
~ 10 min
Output
2.- scRNA-Seq Differential Expression
Application
Input
Parameters
Filtering Mode: Counts Per Million
CPM Filter: 0.0
Cells Reaching CPM Filter: 1
Normalization Method: TMM with Zero Pairing
Biological Replicates: individual
Diffexp Option: Simple Design
Primary Factor: clusters
Primary Contrast Conditions: cluster_1,cluster_9,cluster_2,cluster_3,cluster_6,cluster_5,cluster_4,cluster_8,cluster_7
Primary Reference Conditions: cluster_1,cluster_9,cluster_2,cluster_3,cluster_6,cluster_5,cluster_4,cluster_8,cluster_7
Blocking Factor: disease
Test Contrasts Separately: true
Select a Statistical Test: GLM (Likelihood Ratio Test)
Robust: true