So.... if "someone" were absolutely new to RNA-seq, with absolutely no experience in bioinfomatics, but was about to get thrown in the deep end to analyze the differences in cell subtypes (using single cells).... how/where would this person take the first step??? ...and the second....and.... I'm all ears - assume I know nothing (which is not putting it lightly).
In my opinion places like biostars are not suitable for asking such broad questions. People here are happy to help but these questions need some face to face dialogue to understand the problem, what you have in hand, what you can afford learning (or buying).
My 2p: Find a bioinformatician in your institute are somewhere not too far. Grab him/her for coffee and talk about your situation. Ask what you need to learn and what you need access to (a Linux desktop PC might be enough, or a dedicated server? or a cluster?) What you describe seems to be quite standard differential gene expression analysis and you probably don't need a super star in the field, at least to get started. It may even be that what you need is easier than you think...
Then for specific questions biostars is great.
Good luck!
Considering you have =0 NGS/RNA Seq experiment
Step 1 - Learn about Bioinformatics and basic alignment techniques Time = 15-30 days
Step 2 - Learn the basics of Unix, Python, Perl and R these are absolute essentials (But Just the basics). Time - 3-6 months A good book to refer : Haddock, Steven Harold David, and Casey W. Dunn. Practical computing for biologists. No. 57: 004 HAD. Sunderland, MA: Sinauer Associates, 2011.
Step 3 - Start analyzing/manipulating small test data on a more visually interactive platform like galaxy. Time- 1 month
Step 4 - Learn to Use a workstation like Linux based computer with enough NGS tools (Run all your analysis through command line). example- BioLinux . Time - 1 month
Step 5 - Now Start the analysis of real experimental data from your lab/experiments
total Time = 8-10 months
Note that the given timeline is just a guide, actual time will depend on your speed of learning and the time and resources devoted.
Regards.
As always read some (a lot!) of papers/reviews using similar approaches (in your case single-cell RNA-Seq) and look at their methods.
Firstly, you're making a good start to assume nothing and acknowledge that you need help.
Secondly, how much time have you got? If it needs to be done 'yesterday', then forget it and get a (willing) collaborator (like Devon says) who can work with you on the project. Talk to the collaborator and discuss their workflow and decision process, they may even be willing to give you some training if you show you're keen.
If you've got 6+ months, then do as Rajeev suggested. Read up on bioinformatics/programming/linux basics, attend courses, try out a few things yourself on known data and, most importantly, talk through your questions/problems with a local expert. That bit is key, otherwise it's all too easy to fall into bad practices without even knowing it.
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You need a collaborator. No one would try to undertake a wet-lab experiment of this complexity with 0 experience, a bioinformatics analysis of this complexity is no different. There is no possible way for you to become knowledgeable enough in a very short period of time on your own with your current knowledge.
First, just out of curiosity, can you explain how a lab manager makes a descision to have an absolute beginner analyze precious and novel data?
It's hard to say where to start, if you say you know absolutely nothing, but it is also unlikely to be true :) You need to build on your foundation, maybe you know Linux, running scripts, programming languages, statistics, biology, sequencing? Then you will take courses, learn the basics about the technology and what analysis steps are necessary, then read papers and protocols. And allow for a lot of time to understand the pipelines. Some people might want to sell you one-click solutions like CLC bio, they are fine for a beginner, but you still need to understand which of the buttons need to be clicked in which order, and which not and how to interpret the results.
Take a look at RnaSeq workflows from Galaxy.