Pinned Repositories
advent_of_code
advent_of_code_2023
Solutions to advent of code 2023 in R
awesome-advent-of-code
A collection of awesome resources related to the yearly Advent of Code challenge.
extractoR
libroScrapeR
RNA-seq_data_analysis_course_for_biologists_may_2018
The goal of the course is to introduce basic RNA-seq data analysis, starting from experiment design and raw files to functional data analysis that can be performed using PC only. In brief, the content of the course will cover: general knowledge on RNA-seq library preparation (which kit/technology should I use and why? how many replicates? how deep should I sequence my samples?), quality check and filtering of the reads (including adapter trimming, rRNA removal), read alignment/quasi-mapping (can I perform read mapping on my PC without losing its specificity and efficacy?), performing gene counts (assigning reads to provided gene annotation), data normalization (which normalization types should I apply to my data?), differential expression data analysis, basic data visualization, functional analysis (GO terms, pathway analysis).
rosalind
solving rosalind tasks in R
ScExploreR
R shiny app visualizing single cell RNA-seq data reflecting the transcriptome profiles of nearly 50 000 cells building the embryonic zebrafish heart at 48 and 72 hpf.
Single-cell-RNA-seq-data-analysis-2018
Event page: https://www.csc.fi/web/training/-/single-cell-rnaseq-r-2018
kanahia's Repositories
kanahia/libroScrapeR
kanahia/advent_of_code
kanahia/advent_of_code_2023
Solutions to advent of code 2023 in R
kanahia/awesome-advent-of-code
A collection of awesome resources related to the yearly Advent of Code challenge.
kanahia/extractoR
kanahia/RNA-seq_data_analysis_course_for_biologists_may_2018
The goal of the course is to introduce basic RNA-seq data analysis, starting from experiment design and raw files to functional data analysis that can be performed using PC only. In brief, the content of the course will cover: general knowledge on RNA-seq library preparation (which kit/technology should I use and why? how many replicates? how deep should I sequence my samples?), quality check and filtering of the reads (including adapter trimming, rRNA removal), read alignment/quasi-mapping (can I perform read mapping on my PC without losing its specificity and efficacy?), performing gene counts (assigning reads to provided gene annotation), data normalization (which normalization types should I apply to my data?), differential expression data analysis, basic data visualization, functional analysis (GO terms, pathway analysis).
kanahia/rosalind
solving rosalind tasks in R
kanahia/ScExploreR
R shiny app visualizing single cell RNA-seq data reflecting the transcriptome profiles of nearly 50 000 cells building the embryonic zebrafish heart at 48 and 72 hpf.
kanahia/Single-cell-RNA-seq-data-analysis-2018
Event page: https://www.csc.fi/web/training/-/single-cell-rnaseq-r-2018