Can I perform Expression Analysis on this 10x dataset?

Question

10x Genomics: 20k Human PBMCs is the dataset.

Description of the dataset:

Inputs/Libraries Human peripheral blood mononuclear cells (PBMCs) of a healthy male donor aged 30-35 were obtained by 10x Genomics from AllCells.

Gene expression and V(D)J libraries were generated from ~33,000 cells (18,470 cells recovered) as described in the Chromium Next GEM Single Cell 5' HT Reagent Kits v2 User Guide (CG000421) using the Chromium and sequenced on an Illumina NovaSeq 6000 to a read depth of approximately 25,000 mean reads per cell for Gene Expression, 15,000 mean reads per cell for TCR Amplified, and 25,000 mean reads per cell for BCR Amplified libraries.

Paired-end, dual indexing

Read 1: 26 cycles (16 bp barcode, 10 bp UMI)
i5 index: 10 cycles (sample index)
i7 index: 10 cycles (sample index)
Read 2: 90 cycles (transcript)

This is the RPubs Link for the Single Cell Analysis work I've performed on it.

Can someone confirm that I can do both Single-Cell RNA Seq Analysis AND Differential Gene Expression Analysis?

I know that Expression Analysis comes from having a control and a treatment group. I honestly don't know anything about bioinformatics but I'm diving into the fire to get a solid understanding once I've coded everything.

I followed this video and want to follow this other video.

Thank you guys.

Answer 1

Typically what this PMBC RNA-seq data set would be used for is for a control against individuals who would exhibit a pronounced cellular immune response. For example, following a COVID19 infection the PMBCs would be have lots of T-cell CD4+ and CD8+ and are essential for controlling the infection. Thus it is a really good approach to understand the reasons for different clinical outcomes, such chronic infection and severe infections.

I would suspect a cancer patient would show a pronounced shift in cellular immune response, but I'm not certain. Anyway, by comparing a control data set against a COVID19 patient a better understanding of the cellular immune response can be obtained and a differential gene expression analysis would be an important inroad to assess the T-cell up-regulation. DEG would show very pronounced differences between a healthy individual and a patient.

Having a hypothesis about the expected results from DEG is important, because interpreting DEG output is otherwise difficult.

With regards your question, a DEG analysis IMO would be a good analysis and is easy to perform, however there only appears to be a single sample. A DEG analysis in context is control X vs patient Y. The sample you have appears to be a control sample so you would need a second sample. Performing DEG is easy, but do stress that interpreting DEG isn't easy because everything is relative. Thus what is sought usually in PMBC comparisons healthy vs patient is evidence of T-cell CD8+/CD4+ up regulation, any indication about which possibly which HLA type is active (I dunno whether RNAseq will do this - good if it can) and information on the signalling cascade. Basically as much information on the cellular immune response that can be gleaned via RNAseq without performing laborious and difficult immunological experiments requiring specialist apparatus.

There are investigators here with more experience than I in eukaryotic RNAseq analysis, so there input would be helpful.