LiMCA is a new single-cell sequencing method that can simultaneously profile the 3D genome structure and transcriptome in individual cells. On transcriptome side, LiMCA provides full-length mRNA read coverage for thousands of genes. This capacity is crucial for research across various domains, such as olfactory receptor expression. On 3D genome side, LiMCA can accurately identify distinct cell types and reconstruct high-resolution 3D models.
This repository contains data, scripts, pipelines used for LiMCA analysis.
We followed the standard Smart-seq2 processing workflow documented in the Human Cell Atlas (HCA) Data Portal (https://data.humancellatlas.org/pipelines/smart-seq2-workflow).
We can calculate single-cell haplotypic gene expression using phASER. A simple bash script, RNA_phaser_ase.sh, is provided.
✔ For analysis of scHi-C data, we encapsulated hickit and dip-c commands to creat the bash pipelines, LiMCA_for_diploid.sh and LiMCA_for_olfactory.sh. See the documentations of hickit and dip-c for detailed, step-by-step instructions.
LiMCA_for_diploid.sh
Usage: LiMCA_for_diploid.sh
Options:
-g: genome.fa
-f: R1.fq
-r: R2.fq
-t: N, threads
-v: SNP FILE
-s: GENOME ASSEMBLY
-h: help information
Action:
-A: Reads mapping
-P: Contacts parsing, haplotype imputation, and scA/B values
-S: Reconstruction of 3D genome structures, structure filtering
-D: Typical Dip-C analysis
-R: Spatial analysis of actively expressed genes in individual cells
(visualization of actively expressed genes, calculation of spatial relationship between actively expressed genes)
LiMCA_for_olfactory.sh
Usage: LiMCA_for_olfactory.sh
Options:
-g: genome.fa
-f: R1.fq
-r: R2.fq
-t: N, threads
-s: GENOME ASSEMBLY
-v: SNP FILE
-h: help information
Action:
-A: Reads mapping
-P: Contacts parsing, haplotype imputation
-S: Reconstruction of 3D genome structures, structure filtering
-C: Contact analysis of Greek islands and OR genes for olfactory neurons
-D: Similar to -C, but 3D structure analysis for olfactory neurons
See also:
GEO README (ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121791/suppl/GSE121791%5F00README%2Emd%2Etxt)