Access data

Last updated: 2020-01-23

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Knit directory: peco-paper/

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Here are some useful links for how we processed our data.

Data filtering and normalization

For imaging data:

First, we processed FUCCI and DAPI images and computed background-corrected sum intensity for each cell image.
Second, we corrected for C1 batch effects in FUCCI intensity data.

For scRNA-seq data:

First, we assessed potential C1 batch effects in sequencing depth and read to molecule conversion rate.
Second, we identified high quality samples using sample quality metrics, such as total number of mapped reads, ERCC percentages, etc.
Third, we excluded over-expressed and lowly-expressed genes.
Finally, we used Principal Component Analysis to examined potential sources of gene expression variation.

To create the final dataset for analysis:

We combined scRNA-seq with image intensity data.

Downloading the data files

You have two main options for downloading the data files. First, you can manually download the individual files by clicking on the links on this page or navigating to the files in the fucci-seq GitHub repository. This is the recommended strategy if you only need a few data files.

Second, you can install git-lfs. To handle large files, we used Git Large File Storage (LFS). This means that the files that you download with git clone are only plain text files that contain identifiers for the files saved on GitHub’s servers. If you want to download all of the data files at once, you can do this with after you install git-lfs.

To install git-lfs, follow their instructions to download, install, and setup (git lfs install). Alternatively, if you use conda, you can install git-lfs with conda install -c conda-forge git-lfs. Once installed, you can download the latest version of the data files with git lfs pull.

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
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 [5] rprojroot_1.3-2 R6_2.4.0        backports_1.1.2 git2r_0.26.1   
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[13] promises_1.0.1  whisker_0.3-2   rmarkdown_1.10  tools_3.5.1    
[17] stringr_1.3.1   glue_1.3.0      httpuv_1.4.5    yaml_2.2.0     
[21] compiler_3.5.1  htmltools_0.3.6 knitr_1.20

Access data

John Blischak, Joyce Hsiao

Data filtering and normalization

Downloading the data files