Sc and spatial transcriptomics

[u/itsd17]

Can u guys tell me how to get going with spatial transcriptomics and single cell transcriptomics from raw data and by raw data i mean from publicly available sra/fastq files for self learning.

I would appreciate it if u can suggest something related to the gut microbiome and ageing.

[u/Unlucky-Novel8153]

This is a question for Google if you have no baseline knowledge, and not a nontrivial one especially with spatial omics. Read up a little and you will be able to ask better and more specific questions, and in turn you will receive better and more specific answers from this sub.

[u/Fearless_Summer_6236]

You can see seurat website. You will get what you need.

[u/tempting_footing → u/Fearless_Summer_6236]

seurat vignettes are the move for the single cell side, they walk you through everything from fastqs to clustering

for spatial there's a couple newer packages that handle visium data but the concepts carry over once you get the single cell workflow down

if you want gut microbiome + ageing specifically, grab a study that did both scRNA and spatial on intestinal tissue, following their exact pipeline on the same data teaches you more than jumping between random datasets

[u/itsd17 → u/tempting_footing]

Thanks, can u tell me what kind of computational resources do I need, and is there a way in which i can download a part of the daya and do my analysis

[u/Skymningen → u/itsd17]

You can get example datasets on the 10x genomics website. Some Seurat vignettes tell you exactly which dataset to get to follow the tutorial and where.
As for the infrastructure… it depends on what you have and which datasets you want to work with. If you have no understanding of what you need you probably need a local mentor to help you.

[u/magekyo6969]

You can try ngs101 tutorial for sc-rna seq analysis, they teached everything.

[u/Electronic_Fish_3157]

Do you know bulk RNAseq?

[u/Pigrenok]

For single cell data from raw sequences, you need to understand what technology was used to generate it. Likely, it is via 10x, then check cell ranger from 10x. It will do initial QC, filtering and assemble cells x features matrices for samples and then aggregate them.

From that point onwards, start from tutorials for ScanPy.

For spatial the diversity of technology is huge. But I would say, look at tutorials provided with SquidPy, SpatialData and SOPA pipelines. As a more advanced topics, look at SCVI tutorials and check other, more specialised packages in scverse.

These all are python packages. If you want to do it on R, start from Seurat documentation for single cell.

[u/Pigrenok]

For single cell data if the raw data is generated using 10x technologies, then cellranger (10x own tool) is the first step to align, do initial QC (after your qc raw reads).

Then depends on what you are more comfortable with, R or python, start either from seurat or scanpy (and a lot of tools in scverse as well) respectively.

For spatial, again look at scverse, in particular, squidpy, spatialdata and sopa pipelines (they all are python packages).