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Schedule - scRNAseq course

Schedule

This course is given at Scilifelab Solna, Rooms Air & Fire, ground floor to the left from the main entrance.

Lecture slides will be provided as links as they get completed.



Monday 27th of January
Time Description Lecturer
09.00 Course introduction Åsa Björklund
09.15 scRNAseq methodologies and ESCG platform Karolina Wallenborg
10.00 Coffee Break
10.30 scRNAseq Quality Control Åsa Björklund
11.15 Data normalization Åsa Björklund
12.10 Lunch
13.15 Intro to exercises Åsa Björklund
13.30 Exercises: Quality control Åsa, Paulo, Anna
15.00 Coffee Break
15.30 Dimensionality reduction Paulo Czarnewski
16.30 Wrap up of todays lectures Åsa & Paulo


Tuesday 28th of January
Time Description Lecturer
09.00 Exercises: Dimensionality reduction Åsa, Paulo, Nikolay, Rui
10.00 Coffee Break
10.30 Batch correction + Data integration Paulo Czarnewski
11.15 Exercises: Data integration Åsa, Paulo, Nikolay, Rui
12.00 Lunch
13.00 Clustering techniques and scRNAseq toolkits Åsa Björklund
13.45 Exercises: Clustering Åsa, Paulo, Nikolay, Rui
14.45 Coffee Break
15.15 Exercises: Clustering (continued) Åsa, Paulo, Nikolay, Rui
16.00 Invited seminar: Ins and outs of UMAP and tSNE Nikoaly Oskolkov
16.30 Wrap up of todays lectures Åsa & Paulo
18.00 ** Course dinner at Shanti - Touch of bengal **


Wednesday 29th of January
Time Description Lecturer
09.00 Differential expression Olga Dethlefsen
09.45 Coffee Break
10.15 Exercises: Differential expression Åsa, Paulo, Olga, Johan
11.15 Trajectory inference analysis Paulo Czarnewski
12.00 Lunch
13.00 Exercises: Trajectory inference Åsa, Paulo, Olga, Johan
14.00 Coffee Break
14.30 Invited seminar: Single-cell multi-omics (Protein + RNA) Johan Reimegård
15.00 Invited seminar: Spatial transcriptomics Lars Borm
15.30 Invited seminar: PanglaoDB Oscar Franzen
16.00 Wrap up of todays lectures Åsa & Paulo
16.15 Course Summary Åsa & Paulo


Thursday 30th of January
Time Description Lecturer
09.00 Bring your own data (until 16.00) Åsa, Paulo, Johan, Rui

You can also download pre-processed datasets from PanglaoDB. Click on view for one dataset and then click on [ RData ] to download the compressed matrix. Put all the files into a single folder and then run the script below to merge them together:

#Set working directory where you downloaded the data
setwd("~/Downloads")

#get the names of all SRA files you downlaoded
SRA_file_list <-  list.files(pattern = "SRA")

#for each file, load the matrix and add it to the list of matrices
file_list <- list()
for(i in SRA_file_list){
  load(i)
  rownames(sm) <- sub( "_.*","",rownames(sm) )
  rownames(sm) <- sub( "[.].*","",rownames(sm) )
  sm <- Matrix::Matrix( rowsum(as.matrix(sm),rownames(sm)) ,sparse = T)
  file_list[[i]] <- sm
}

#get a vector of all genes found in the matrix
all_genes <- unique(unlist(lapply(file_list,function(x) return(rownames(x)))))
all_cells <- unique(unlist(lapply(file_list,function(x) return(colnames(x)))))

#create a empty matrix with all genes and all cells found
combined_data <- Matrix::Matrix(0,nrow = length(all_genes),
                    ncol = length(all_cells),
                    sparse = T,
                    dimnames = list(all_genes,all_cells))

#fill the matrix with the values in each file
for(i in names(file_list)){
  combined_data [rownames(file_list[[i]]),colnames(file_list[[i]])] <- file_list[[i]]
}

#Remove intermediate files
rm(all_genes,all_cells,file_list,sm,i,SRA_file_list)

#This is now your combined data
combined_data


Each day we will have coffee around 10.00 and at 15.00 during the exercises. Lunches will be at Nanna Svarz aroun 12.00 each day.


Icons are provided from [www.svgrepo.com](www.svgrepo.com)