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.

FUCCI intensity data

  • Combined intensity data are stored in data/intensity.rds. These include samples that were identified to have a single nuclei .

  • Data generated by combine-intensity-data.R. Combining image analysis output stored in /project2/gilad/fucci-seq/intensities_stats/ into one data.frame and computes summary statistics, including background-corrected RFP and GFP intensity measures.

Sequencing data

  • Raw data from each C1 plate are stored separatley in data/eset/ by experiment (batch) ID.

  • Raw data combining C1 plate are stored in data/eset-raw.rds.

  • Filtered raw data excluding low-quality sequencing samples and genes that are lowly expressed or overly expressed are stored in data/eset-filtered.rds.

Phenotypic data of singleton samples

  • Data file 1: all 1536 samples before filtering (output/data-overview.Rmd/phenotypes_allsamples.txt)
  • Data file 2: 888 samples after filtering (output/data-overview.Rmd/phenotypes_singletonsamples.txt)
  • Data file 3: phenotype labels (output/data-overview.Rmd/phenotypes_labels.txt)
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")

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.

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