Credit

Data and code below were taken from the DIY.transcriptomics course by Dr. Daniel Beiting

Data

These are the fastq files that come from 1000 peripheral blood mononuclear cells (PBMCs) and is one of the sample datasets provided by 10X Genomics.

Storage space ~ 5Gb

Download here Note: do not uncompress them.

Get the reference sequences from Ensembl (cDNA fasta file for Human) here

(Optional) This file will be generated on the fly but I am anyway pasting the link here (just in case). transcript to gene mapping file

Initial setup and preprocessing (mostly in shell)

1. Create a conda environment (name it sva_demo) and activate it

conda create --name sva_demo

conda activate sva_demo

2. Install the Kallisto package (popular for single-cell analysis)

conda install kallisto

3. Install the kb-python package that consist of some bustools required to perform preprocessing of the dataset

pip install kb-python

More info about kb-python here

4. Use Kallisto to build index from reference sequences

kallisto index -i Homo_sapiens.GRCh38.cdna.all.fa Homo_sapiens.GRCh38.cdna.all.index

kallisto index -i input_fasta output_index

5. Preprocessing scRNA-seq data

 kb count \
 pbmc_1k_v3_S1_mergedLanes_R1.fastq.gz pbmc_1k_v3_S1_mergedLanes_R2.fastq.gz \
 -i Homo_sapiens.GRCh38.cdna.all.index \
 -x 10XV3 \
 -g t2g.txt \
 -t 8 \
 --cellranger

Great, now you are done with the initial setup and preprocessing!

QA and analysis in RStudio

1. You must have R and RStudio installed. If not .....

2. Now open the DIY_scRNAseq script on your system (Rstudio) and simply follow the instructions in it.

Sample input for the ML workflow

ML_input.tsv.gz