Summary
Publication

Title: Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Authors: Samuel G Rodriques, Robert R Stickels, Aleksandrina Goeva, Carly A Martin, Evan Murray, Charles R Vanderburg, Joshua Welch, Linlin M Chen, Fei Chen, Evan Z Macosko

Date published: 3/29/19

Date added: 5/1/22

Journal: Science

DOI: 10.1126/science.aaw1219

Protocol: Slide-seq

Data source: SCP354

Study design

Species: mouse

Number of samples: 106

Region: Whole brain, hippocampus

Figure: High-resolution RNA capture from tissue by Slide-seq.

Spot-level data

Select sample:

Current selection:

ssREAD sample ID: ST00501

Sample raw name: Cerebellum_Puck_180819_11

Total spots in the plot: 582

Clusters to plot:

Image opacity:

0.4

Point opacity:

1
13612111268575123140246020406080100120140
Number of Spots

Gene expression

Please enter a gene symbol.

Spatially variable genes

Sample:

Log2 fold-change cutoff:

0.5

Adjusted p-value cutoff:

10^-6
10^-5
10^-4
10^-3
0.01
0.05
0.1
1
Foldchange direction:
Gene nameLog fold-changetarget_domainAdjusted p-valuein_group_fractionout_group_fractionin_out_group_ratioin_group_mean_expout_group_mean_exp
Differential expression (DE) / Gene set enrichment

Sample:

Note: The MAST package were used to calculate DEGs on normalized gene expression data (parameter: log2FC > 0.25, adj.p-value < 0.05).

Group:

Cluster:

Log2 fold-change cutoff:

0.5

Adjusted p-value cutoff:

10^-6
10^-5
10^-4
10^-3
0.01
0.05
0.1
1
DE direction:
Gene nameLog fold-changePct.1Pct.2Adjusted p-value
ssREAD is developed by BMBL, it is free and open to all users and there is no login requirement. | 2025