Square Bin-Gene

Analytical Report
A02677B5_DAPI_Demo_Realign
Alerts
Metrics
Value
Alert
Note

- Summary
  Gene Protein
- Square Bin
  Gene Protein
- Cell Bin
  Gene Protein
- Image
- Analysis

Tissue Square Bin Statistics

###### Bin Size

The size of Bin which is the unit of aggregated DNBs in a squared region (i.e. Bin 50 = 50 * 50 DNBs)

###### Mean Reads (per bin)

Mean number of sequenced reads divided by the number of bins under tissue coverage

###### Median Reads (per bin)

Median number of sequenced reads divided by the number of bins under tissue coverage (pick the middle value after sorting)

###### Mean Gene Type (per bin)

Mean number of unique gene types divided by the number of bins under tissue coverage

###### Median Gene Type (per bin)

Median number of unique gene types divided by the number of bins under tissue coverage

###### Mean MID (per bin)

Mean number of MIDs divided by the number of bins under tissue coverage

###### Median MID (per bin)

Median number of MIDs divided by the number of bins under tissue coverage
Bin Size
Mean Reads
(per bin)
Median Reads
(per bin)
Mean Gene Type
(per bin)
Median Gene Type
(per bin)
Mean MID
(per bin)
Median MID
(per bin)
20
5,380
5,555
563
585
1,219
1,267
50
33,083
34,582
2,395
2,516
7,497
7,895
200
490,606
535,439
9,453
10,158
111,161
122,252

- Bin 20
- Bin 50

Distribution
Violin plots show the distribution of deduplicated MID count and gene types in each bin
Clustering
(left) Clustering spots (bin20) under tissue covered with the Leiden algorithm
(right) UMAP projections of spots (bin20) colored by automated clustering. Same color is assigned to spots that are within shorter distance and with similar gene expression profile
Tissue Plot with Spots (bin20)
UMAP Projection of Spots (bin20)
Top Markers by Cluster
The goal of the differential expression analysis is to identify markers that are more highly expressed in a cluster than the rest of the sample. For each marker, a differential expression test was run between each cluster and the remaining sample. An estimate of the log2 ratio of expression in a cluster to that in other coordinates is Log2 fold-change (L2FC). A value of 1.0 denotes a 2-fold increase in expression within the relevant cluster. Based on a negative binomial test, the p-value indicates the expression difference's statistical significance. The Benjamini-Hochberg method has been used to correct the p-value for multiple testing. Additionally, the top N features by L2FC for each cluster were kept after features in this table were filtered by (Mean UMI counts > 1.0). Grayed-out features have an adjusted p-value >= 0.10 or an L2FC < 0. N (ranges from 1 to 50) is the number of top features displayed per cluster, which is set to limit the amount of table entries displayed to 10,000. N=%10,000/K^2 where K is the number of clusters. Click on a column to sort by that value, or search a gene of interest.
Distribution
Violin plots show the distribution of deduplicated MID count and gene types in each bin
Clustering
(left) Clustering spots (bin50) under tissue covered with the Leiden algorithm
(right) UMAP projections of spots (bin50) colored by automated clustering. Same color is assigned to spots that are within shorter distance and with similar gene expression profile
Tissue Plot with Spots (bin50)
UMAP Projection of Spots (bin50)
Top Markers by Cluster
The goal of the differential expression analysis is to identify markers that are more highly expressed in a cluster than the rest of the sample. For each marker, a differential expression test was run between each cluster and the remaining sample. An estimate of the log2 ratio of expression in a cluster to that in other coordinates is Log2 fold-change (L2FC). A value of 1.0 denotes a 2-fold increase in expression within the relevant cluster. Based on a negative binomial test, the p-value indicates the expression difference's statistical significance. The Benjamini-Hochberg method has been used to correct the p-value for multiple testing. Additionally, the top N features by L2FC for each cluster were kept after features in this table were filtered by (Mean UMI counts > 1.0). Grayed-out features have an adjusted p-value >= 0.10 or an L2FC < 0. N (ranges from 1 to 50) is the number of top features displayed per cluster, which is set to limit the amount of table entries displayed to 10,000. N=%10,000/K^2 where K is the number of clusters. Click on a column to sort by that value, or search a gene of interest.