How to download your data
When your data has arrived and the preliminary analysis has been performed, you will receive an e-mail from our data team to download your data.
Your data will be sent with our secured Amazon Web Services (AWS) server. There are multiple ways to download:
- Your institute uses AWS as a data portal.
In this case, you can fetch your data directly from our AWS server space to your own AWS server space.
- Your institute is not using AWS
- Download your data by pasting the download link directly in your internet browser
- Download your data via a command-line session of your server cluster
Data files
Data files | 10x Genomics
We send parts of the standard cell ranger output, which consists of the files listed below, as well as a package of files and reports from our own preliminary clustering and differential gene expression analysis that was performed on all your samples that have to be compared.
The following files are always included:
- Html reports – Contains .html files for each sample that contain basic QC metrics and clustering info, as provided by the automated cell ranger software.
- Raw_counts – contains the cellranger output (mapped count tables). These are the raw count matrices with all barcodes.
- Filtered_counts – Same as above, but filtered for empty barcodes (this is used for downstream analaysis).
- Metrics – Contains the QC metrics from the html files in .csv format
- Clustering– Contains a preliminary clustering and differential gene expression analysis we have done with Seurat. You can find the description of the folders and files in our Seurat manual.
- cloupe_file – contains the cloupe files needed to load the data into the 10x Loupe cell browser.
- H5 files – Contain the file structure information as generated by cell ranger.
- Fastq – contains the raw fastq files
Optional:
.bam files – If you want to receive .bam files as resulting from cell ranger, this can be included in the data transfer as well.
Data files | SORT-seq
The diagnostics file contains the QC and diagnostic plots for each plate. Here you can check how well each cell worked (endogenous reads, UMIs and genes per cell) and how well the technical handling of the plate worked (ERCC spike in reads). It also includes a plot to estimate oversequencing, by looking at how many times each molecule was sequenced (by comparing UMI corrected with raw reads). This tells us if enough sequencing depth was assigned to the sample. It also reports the most highly expressed genes and the most variable genes in the library. You can see an example of such a diagnostic plot here.
The Counts folder contains count tables. These come in 3 flavors:
- read counts (raw mapped reads)
- barcode counts (UMI corrected version of 1)
- transcript counts (poisson counting statistics corrected version of 2) This is the file we use as input for downstream analysis since it comes closest to the real situation in the cell
Each of these threesomes of files is then also split into three separate kinds of mapped reads:
- Exonic reads – reads found in exons
- Intronic reads – reads found in introns
- Total reads – combination of both intronic and exonic reads
It is up to you to decide which one you are most interested in. For clustering and downstream analysis, we usually use the transcript counts of the exonic reads.
Fastq contains the raw sequencing FAST files. These typically are a set of 8 files: two reads (Read 1 and Read 2) from each of the four lanes of the Nextseq500. R1 and R2 indicate the read type. L00X indicate the lane. To map, we concatenate all read 1 / read 2 files into two files, and use this as input for mapping with STAR.
The clustering folder contains a preliminary clustering analysis done with Seurat.
Data files | Bulk RNA sequencing
Diagnostics file contains basic QC and diagnostic plots for your samples. It contains the following information:
- Reads per sample – Shows the total number of mapped reads for all samples.
- Genes per sample – Shows the total number of genes detected for all samples.
- Correlation heatmap – This shows a correlation heatmap indicating how similar samples are to each other (Red = similar, blue = different). Samples are clustered by unsupervised clustering based on their similarity.
The Counts folder contains a count table with the expression matrix of your data. Rows are genes and columns are samples.
The *_raw file contains the raw, not normalized reads
Fastq contains the raw sequencing FAST files for all samples.
Data files | VASA-seq
The diagnostics file contains the QC and diagnostic plots for each plate. Here you can check how well each cell worked (endogenous reads, UMIs and genes per cell) and how well the technical handling of the plate worked (ERCC spike in reads). It also includes a plot to estimate oversequencing, by looking at how many times each molecule was sequenced (by comparing UMI corrected with raw reads). This tells us if enough sequencing depth was assigned to the sample. It also reports the most highly expressed genes and the most variable genes in the library. You can see an example of such a diagnostic plot here.
The Counts folder contains count tables. These come in 3 flavors:
- read counts (raw mapped reads)
- barcode counts (UMI corrected version of 1)
- transcript counts (poisson counting statistics corrected version of 2) This is the file we use as input for downstream analysis since it comes closest to the real situation in the cell
Each of these threesomes of files is then also split into three separate kinds of mapped reads:
- Exonic reads – reads found in exons
- Intronic reads – reads found in introns
- Total reads – combination of both intronic and exonic reads
It is up to you to decide which one you are most interested in. For clustering and downstream analysis, we usually use the transcript counts of the exonic reads.
Fastq contains the raw sequencing FAST files. These typically are a set of 8 files: two reads (Read 1 and Read 2) from each of the four lanes of the Nextseq500. R1 and R2 indicate the read type. L00X indicate the lane. To map, we concatenate all read 1 / read 2 files into two files, and use this as input for mapping with STAR.
The clustering folder contains a preliminary clustering analysis done with Seurat.
Data mapping
When performing a mapping assembly, the generated data will be ‘mapped’ against the reference genome. The genetic information of your species of interest is stored in the reference genome, e.g., cell types and positions of genes on the chromosome. During this procedure, the reference genome is scanned by the algorithm for the perfect spot to map a read. When the algorithm finds a match between your reads and the reference genome, this additional biological information within your data will be saved. This provides you with information about the presence or absence of transcripts of specific genes within your data.
The mapping is most efficient when the mapping software indexes the genome. Two widely used methods to perform the mapping procedure are Spliced Transcripts Alignment to a Reference (STAR) and Burrows-Wheeler Aligner (BWA). The big difference between these two methods is the extra information from STAR about the spliced and unspliced transcripts in your data.
Database of genomes
Here, you can find an overview of the genomes we currently have available. Is your species not listed here? Please provide us with the genome so we can map your data. On top of this list, we work with project-specific genomes. You can upload your sequence during sample submission
| Trivial name | Genome |
|---|---|
| Mosquito species | Anopheles_gambiae |
| Arabidopsis | Arabidopsis_thaliana_TAIR10 |
| C. elegans | Caenorhabditis_elegans.WBcel235 |
| Dog | CanFam2011) – ERCC |
| Chimpanzee | Chimpanzee (PanTro) |
| Single cell green alga | Chlamydomonas_reinhardtii |
| Fruit fly | Drosophila Dm6 |
| Oral bacterium | Fusobacterium_nucleatum |
| Chicken | Gallus gallus |
| Hamster | CHO_CriGri-PICR_refseq_LONMF |
| Human | Human hg38 |
| Mouse | Mouse mm10 + mito |
| Protozoan parasite | Plasmodium_falciparum |
| Rabbit | OryCun2.0 |
| Rainbow trout | oncorhynchus_mykiss |
| Rat | Rattus norvegicus |
| Salmon | Salmo_salar.ICSASG_v2.105 |
| Wild boar/pig | Sus scrofa |
| Frog | Xenopus 9.1 |
| Zebrafish | Zebrafish zv9/zv11 |
Preliminary data analysis
Which steps are taken during our standard data analysis?
For our preliminary analysis, we follow the steps as indicated in the figure below. This workflow can roughly be divided into three components: pre-processing, dimensionality reduction, and clustering.
Pre-processing
Quality Control
Pre-processing starts with quality control, where several quality metrics are visualized. These metrics include the number of genes per cell, the number of UMIs per cell, the percentage of mitochondrial genes per cell and the percentage of ribosomal genes per cell.
Filtering
A cutoff value is selected to filter for cells of adequate quality. For our preliminary analysis, we filter cells on the number of UMIs. The selection of this cutoff is relatively arbitrary, so we recommend re-evaluating this for your own analysis. Cells can also be filtered on other metrics, such as the percentage of mitochondrial genes per cell.
Normalization
For normalization and further steps, we use functionalities provided by the Seurat package. Using the default normalization method, gene expression values for each cell are normalized by the total transcript counts and scaled. The outcome is log-transformed.
Determining variable features
The genes that are considered most informative for the variability in the data are selected by applying variance-stabilizing transformation. The genes with the highest variance-to-mean ratio are selected. The 2,000 most variable genes are used for further analysis. These are the genes that show the largest differences in expression between cells.
Scaling
To improve comparison between genes and prevent highly expressed genes dominating the analysis, the relative gene expression abundances between cells is corrected by a linear transformation. During this step, the gene counts are scaled to have 0 mean expression across cells and 1 variance across cells.
Dimensionality reduction
Principal Component Analysis
The dimensionality of the dataset is reduced by principal component analysis (PCA). To put if briefly, PCA simplifies further analysis by creating components that reflect the variation in the dataset.
Selecting principal components
The principal components that explain most of the variance are used for clustering and UMAP/tSNE embedding. The number of principal components to include is determined in an automated fashion. We recommend re-evaluating the selected number of PCs for your own analysis.
Creating UMAP and tSNE
For visualization, the cells are embedded in a two-dimensional space by UMAP or tSNE. These non-linear algorithms try to capture the underlying manifold of the data and place similar cells close to each other. Cells that are grouped together during clustering are expected to co-localize on UMAP and tSNE. In our analysis, the UMAP and tSNE embeddings are used to visualize clusters, libraries, gene expression, and quality metrics.
The most relevant parameter is the number of principal components used.
Clustering
Determining clusters
The cells are clustered into subpopulations to find biological meaningful trends in the single-cell data. They are grouped together based on how alike their gene expression profiles are. Relevant parameters are the resolution and the number of principal components used. The same number is used as for the creation of the UMAP and tSNE.
Differential expression analysis between clusters
To find the genes that mark the difference between clusters, differential expression analysis is performed. In this step, the p values are computed for differentially expressed genes between cell clusters. The enriched genes characterize the cluster. The used statistical test is the Wilcoxon rank-sum test, in which genes are ranked by their difference in expression between clusters. Other relevant parameters are:
- Whether to analyze only upregulated expression in the selected cluster with regards to the rest of the clusters, or both up- and downregulated cells.
- The minimum percentage that a gene is expressed in either the selected cluster or the remaining clusters.
- The threshold for how many log-fold difference the genes need to have to be included in the results.
- The threshold for the p value that a gene needs to have to be included.