Data overview

Data availability

• This document lists the datasets analyzed in the study.

• We stored all datasets as expressionSets (require Biobase package).

• We provided data in TXT format on the Gilad lab website for the 11,093 genes analyzed in the study and for each of the 888 samples: molecule count, sample phenotpyes, gene information, phenotype label descriptiond and FUCCI intensity data.

Data structure

We collected two types of data for each single cell sample: single-cell RNA-seq using C1 plates and FUCCI image intensity data.

• Raw RNA-seq data: data/eset-raw.rds

• Filtered RNA-seq data: data/eset-filtered.rds

• FUCCI intensity data: data/intensity.rds

• FUCCI intensity data adjusted for batch effect: output/images-normalize-anova.Rmd/pdata.adj.rds

• Final data combining filtered intensity and RNA-seq, including 11093 genes and 888 samples: data/eset-final.rds

Code used to generate data from data/eset-raw.rds to data/eset-final.rds is stored in code/output-raw-2-final.R.

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.

How to access expressionSets

We store feature-level (gene) read count and molecule count in expressionSet (data/eset) objects, which also contain sample metadata (e.g., assigned indivdual ID, cDNA concentraion) and quality filtering criteria (e.g., number of reads mapped to FUCCI transgenes, ERCC conversion rate). Data from different C1 plates are stored in separate eset objects:

To combine eset objects from the different C1 plates:

eset <- Reduce(combine, Map(readRDS, Sys.glob("data/eset/*.rds")))

To access data stored in expressionSet:

• exprs(eset): access count data, 20,421 features by 1,536 single cell samples.

• pData(eset): access sample metadata. Returns data.frame of 1,536 samples by 43 labels. Use varMetadata(phenoData(eset)) to view label descriptions.

• fData(eset): access feature metadata. Returns data.frame of 20,421 features by 6 labels. Use varMetadata(featureData(eset)) to view label descriptions.

• varMetadata(phenoData(eset)): view the sample metadata labels.

• varMetadata(featureData(eset)): view the feature (gene) metadata labels.

Additional data information

library(Biobase)

eset_raw <- readRDS("../data/eset-raw.rds")
df <- data.frame(sample_id=rownames(pData(eset_raw)), pData(eset_raw), stringsAsFactors = F)
write.table(df, quote=F, sep="\t", 
            row.names = F, col.names = T,
            file = "../output/data-overview.Rmd/phenotypes_allsamples.txt")


eset_final <- readRDS("../data/eset-final.rds")
df <- data.frame(sample_id=rownames(pData(eset_final)), pData(eset_final), stringsAsFactors = F)
write.table(df, quote=F, sep="\t", 
            row.names = F, col.names = T,
            file = "../output/data-overview.Rmd/phenotypes_singletonsamples.txt")

labels <- data.frame(var_names=rownames(varMetadata(eset_raw)),
                       labels=varMetadata(eset_raw)$labeDescription, stringsAsFactors = F)
labels <- rbind(labels,
                data.frame(var_names=rownames(varMetadata(eset_final)),
                       labels=varMetadata(eset_final)$labelDescription, stringsAsFactors = F)[45:54,])
write.table(labels, quote=F,
            sep="\t", row.names = F, col.names = T,
            file = "../output/data-overview.Rmd/phenotypes_labels.txt")

# testing reading files
library(data.table)
df_all <- fread(file = "../output/data-overview.Rmd/phenotypes_allsamples.txt")
df_singles <- fread(file = "../output/data-overview.Rmd/phenotypes_singletonsamples.txt")
df_labels <- fread(file = "../output/data-overview.Rmd/phenotypes_labels.txt")

eset_final <- readRDS("../data/eset-final.rds")

df <- data.frame(sample_id=rownames(pData(eset_raw)), pData(eset_raw), stringsAsFactors = F)

write.table(df, quote=F, sep="\t", quote=F,
            row.names = F, col.names = T,
            file = "../output/data-overview.Rmd/phenotypes_allsamples.txt")

write.table(data.frame(var_names=rownames(varMetadata(eset_raw)),
                       labels=varMetadata(eset_raw)$labeDescription, stringsAsFactors = F), 
            quote=F,
            sep="\t", row.names = F, col.names = T,
            file = "../output/data-overview.Rmd/phenotypes_allsamples_labels.txt")

# testing reading files
library(data.table)
df_test <- fread(file = "../output/data-overview.Rmd/phenotypes_allsamples.txt")
df_labels_test <- fread(file = "../output/data-overview.Rmd/phenotypes_allsamples_labels.txt")

Session information

sessionInfo()

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):
 [1] workflowr_1.1.1   Rcpp_1.0.0        digest_0.6.18    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] magrittr_1.5      git2r_0.23.0      evaluate_0.12    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20