Table 1.

Column 1 shows the reference. Column 2 shows the organism. Column 3 shows the single-cell protocol used. Column 4 shows the number of cells (samples) included in the study. Column 5 shows the number of genes included in the data uploaded to the public repository with varying gene annotations used. Column 6 indicates the units in which the values were reported. Column 7 shows the level of confounding between biological condition and batch effect quantified using the standardized Pearson contingency coefficient as a measure of association. The percentage ranges from 0no confounding to 100completely confounded

Study	Organism	scRNA-seq protocol	Number of cells	Number of genes	Processed data available	Confounding ()
Deng and others (2014)	Mouse	SMART-Seq	286	22 958	RPKM	96.6‡
Guo and others (2015)	Human	Tang and others (2009)	154	23 394	FPKM	82.1
Kowalczyk and others (2015)	Mouse	SMART-Seq	533	8422	TPM	84.8
Kumar and others (2014)	Mouse	SMART-Seq	361	22 443	TPM	97.1
Patel and others (2014)	Human	SMART-Seq	430	5948	TPM	98.9
Treutlein and others (2014)	Mouse	SMART-Seq	198	23 745	FPKM	92.8
Shalek and others (2014)	Mouse	SMART-Seq	383	27 723	TPM	100
Trapnell and others (2014)	Human	SMART-Seq	306	47 192	FPKM	100
Burns and others (2015)	Mouse	SMART-Seq	249	26 585	TPM	NA
Leng and others (2015)	Human	SMART-Seq	458	19 804	TPM	NA
Jaitin and others (2014)	Mouse	MARS-Seq	4466	20 190	UMI	NA
Macosko and others (2015)	Mouse	Drop-Seq	49 300	16 961	UMI	NA
Satija and others (2015)	Zebrafish	SMART-Seq	1152	13 902	UMI	NA
Zeisel and others (2015)	Mouse	STRT-Seq	3004	19 972	UMI	NA
Zheng and others (2017)	Human	Chromium Single Cell	20 000	27 998	UMI	NA
		(10X Genomics)

^‡The main purpose of this study was to investigate monoallelic gene expression in mouse embryos, but here we consider the different developmental stages (oocyte to blastocyst) as the biological condition as an example