This course provides insights into the foundations and principles of multi-omics data analysis and modeling,. The course is aimed specifically for students with experience in Python programming, eager to apply and broaden their skills in single-cell genomics and multi-omics setting. This is a four day course, consisting of lectures in the morning and practicals in the afternoon.
- Day 1: will provide foundations in multi omics technologies and analysis
- Day 2: will focus on basics of machine and supervised analysis of single-cell data,
- Day 3: will focus on unsupervised exploration of multi-omics bulk and single-cell assay
- Day 4: will introduce spatial multi-omics analytics and integration.
- Experience in Python programming, and the use of bash.
- Access to a Linux/Mac/Windows OS machine for practicals
- Day 1: Please download the folder and run the first notebook to download the datasets. (this is cloned from here.)
- Day 2: data
- Day 3: TBD
- Day 4: data
- Day 1: environment-day1
- Day 2: environment-day2
- Day 3: environment-day3
- Day 4: environment-day4
In case you don't manage to set everything up on your own computer, we have prepared a Linux virtual machine that has all the packages you need. This is intended purely as a last resort, since it adds quite a bit of RAM and CPU overhead. You can download it here. The image is in VirtualBox format, but you can also use it with QEMU.
To run QEMU on an Intel/AMD host, use qemu-system-x86_64 -enable-kvm -m 8G -cpu host -vga qxl -hda vm.vdi which runs the VM with 8 GB RAM and 1 CPU.
Add -smp 2 to give it 2 cores.
On a Windows host, replace -enable-kvm with -enable-hax.
QEMU can also emulate x86 hardware on Apple Silicon, but in that case no hardware virtualization is available, so it will be very slow. Try something like:
qemu-system-x86_64 -m 8G -cpu max -hda vm.vdi -vga qxl