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SINGLE-CELL-RECITER

Single-cell topics modeling using deep learning and multi-omics enrichment analysis.

SINGLE-CELL-RECITER

Abstract

In this project, we will apply the amortized Latent Dirichlet Allocation (LDA) model to scRNA. Initially developed in the natural language processing field, LDA is a method of modeling topics using a topic matrix. We can apply the method to single-cell biology by treating each cell as a document and each gene expression count as a word. After training the model, we will plot the topics over a UMAP of the reference set and inspect them for characteristic gene sets. Multiomics enrichment analysis will be performed further using ranked gene/word lists of each cell cluster/document.

Table of Contents

Background

Natural language processing introduced Latent Dirichlet Allocation (LDA) as a topic modeling method. We can apply the method to single-cell biology by treating each cell as a document and each gene expression as a word. We can apply the method to single-cell biology by treating each cell as a document and each gene expression as a word.

Workflow

SINGLE-CELL-RECITER

  • Pancheva A, Wheadon H, Rogers S, Otto TD. Using topic modeling to detect cellular crosstalk in scRNA-seq. PLoS computational biology. 2022 Apr 8;18(4):e1009975.

Data

SRA Run Selector

Run BioProject BioSample LibraryLayout Organism Sample Name source_name tissue
SRR11832836 PRJNA603103 SAMN13919403 PAIRED Homo sapiens GSM4284223 Skin cSCC
SRR11832837 PRJNA603103 SAMN13919401 PAIRED Homo sapiens GSM4284224 Skin Normal Skin
SRR11832838 PRJNA603103 SAMN13919399 PAIRED Homo sapiens GSM4284225 Skin cSCC
SRR11832839 PRJNA603103 SAMN13919396 PAIRED Homo sapiens GSM4284226 Skin Normal Skin
SRR11832840 PRJNA603103 SAMN13919395 PAIRED Homo sapiens GSM4284227 Skin cSCC
SRR11832841 PRJNA603103 SAMN13919394 PAIRED Homo sapiens GSM4284228 Skin Normal Skin
SRR11832842 PRJNA603103 SAMN13919393 PAIRED Homo sapiens GSM4284229 Skin cSCC
SRR11832843 PRJNA603103 SAMN13919392 PAIRED Homo sapiens GSM4284230 Skin Normal Skin
SRR11832844 PRJNA603103 SAMN13919391 PAIRED Homo sapiens GSM4284231 Skin cSCC
SRR11832845 PRJNA603103 SAMN13919390 PAIRED Homo sapiens GSM4284232 Skin cSCC
SRR11832846 PRJNA603103 SAMN13919389 PAIRED Homo sapiens GSM4284233 Skin Normal Skin
SRR11832847 PRJNA603103 SAMN13919388 PAIRED Homo sapiens GSM4284234 Skin cSCC
SRR11832848 PRJNA603103 SAMN13919387 PAIRED Homo sapiens GSM4284235 Skin Normal Skin
SRR11832850 PRJNA603103 SAMN13919385 PAIRED Homo sapiens GSM4284237 Skin Normal Skin
SRR11832851 PRJNA603103 SAMN13919384 PAIRED Homo sapiens GSM4284238 Skin cSCC
SRR11832852 PRJNA603103 SAMN13919383 PAIRED Homo sapiens GSM4284239 Skin Normal Skin
SRR11832853 PRJNA603103 SAMN13919382 PAIRED Homo sapiens GSM4284240 Skin cSCC
SRR11832854 PRJNA603103 SAMN13919381 PAIRED Homo sapiens GSM4284241 Skin Normal Skin
SRR11832855 PRJNA603103 SAMN13919380 PAIRED Homo sapiens GSM4284242 Skin cSCC
SRR11832856 PRJNA603103 SAMN13919379 PAIRED Homo sapiens GSM4284243 Skin Normal Skin
SRR11832857 PRJNA603103 SAMN13919378 PAIRED Homo sapiens GSM4284244 Skin cSCC
SRR11832858 PRJNA603103 SAMN13919377 PAIRED Homo sapiens GSM4284245 Skin Normal Skin
SRR11832859 PRJNA603103 SAMN13919434 PAIRED Homo sapiens GSM4284246 Skin cSCC
SRR11832860 PRJNA603103 SAMN13919433 PAIRED Homo sapiens GSM4284247 Skin Normal Skin
SRR11832849 PRJNA603103 SAMN13919386 PAIRED Homo sapiens GSM4284236 Skin cSCC

Usage

👩‍💻 Installation

:octocat: Git

To fetch source code, change in to directory of your choice and run:

git clone -b main \
    git@github.com:u-brite/Single-cell-reciter.git

Requirements

OS:

Tested on:
Description: 🎩 Red Hat Enterprise Linux Server release 7.9 (Maipo)
Release: 7.9
Codename: Maipo

Tools:

  • Anaconda3
    • Tested with version: conda 4.11.0
  • Cellranger
    • cellranger-5.0.1
  • bamtofastq v1.3.5

Create and activate 🐍 conda environment

❗ Optional: Depends on project. ❗

Change in to root directory and run the commands below:

# create conda environment. Needed only the first time.
conda env create -f Hackin_Omics.yml

# if you need to update existing environment
conda env update --file Hackin_Omics.yml

# activate conda environment
conda activate Hackin_Omics

Steps to run

Step 1

Use the public scRNA data or your own dataset. Various public datasets are available from a variety of sources, including NCBI GEO.

Step 2

Using the Cell Ranger is a set of analysis pipelines to align reads, and generate feature-barcode matrices.

Example:

#!/bin/bash
#
#SBATCH --job-name=CellRanger_P1
#SBATCH --output=CellRanger_P1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=20
#SBATCH --partition=largemem
#SBATCH --time=12:00:00
#SBATCH --mem-per-cpu=10000

module load CellRanger/5.0.1
cellranger -V

time cellranger count --id=P1_1 \
--fastqs=P1_Fastaqs \
--sample=SRR11832837,SRR11832839 \
--transcriptome=refdata-gex-GRCh38-2020-A

Once all the Cell ranger analysis is done move all *.h5 file to one folder as per the need.

Note: Assuming HCP is being used for the analysis.

Step 3

As described in the scRNA_Topic_Modeling.ipynb load the *.h5 file and perform the topic modeling analysis.

Output from this step includes -

Results/
|-- Rank_by_topic.csv
|-- SCC.h5ad

Step 4

Using the each topic in the matrix from the previous step perfom the multiomics gene ranked encriment analyis as discribed in the NOTEBOOK Topic_Ranked_Enrichment_Analysis.ipynb.

Step 5

(Future work) Using the topic in the matrix from the step 3 and perform co-expression analysis. see Notebook: 5_co_expression_analysis.ipynb

Results

LDA topics

Team Members

Name Email Role
👋 Nilesh Kumar (Ph.D. Candidate) nileshkr@uab.edu Team Leader
🤚 Dr. Virginie Grosboillot virginie.grosboillot@alumni.ethz.ch Team Member
✋ Hammad Ali Hassan hammadali50@live.com Team Member

Mentor

Dr. Shahid Mukhtar Associate Professor, Department of Biology Co-Director, Genetics and Genomic Sciences Undergraduate Program Faculty, Department of Surgery Scientist, Nutrition Obesity Research Center UAB | The University of Alabama at Birmingham Campbell Hall 369 | 1300 Univ. Blvd. | Birmingham, AL, 35294-1170