Chiara Schiller
@chiaraschiller.bsky.social
PhD at Schapiro lab in Heidelberg | Spatial biology | Bioinformatics | Tissue organization
6/ Thank you
This work was led by @chiaraschiller.bsky.social w help from @kbestak.bsky.social and supervision from @miguelib.bsky.social, @tanevski.bsky.social and @denisschapiro.bsky.social
This work was led by @chiaraschiller.bsky.social w help from @kbestak.bsky.social and supervision from @miguelib.bsky.social, @tanevski.bsky.social and @denisschapiro.bsky.social
April 15, 2025 at 3:50 PM
6/ Thank you
This work was led by @chiaraschiller.bsky.social w help from @kbestak.bsky.social and supervision from @miguelib.bsky.social, @tanevski.bsky.social and @denisschapiro.bsky.social
This work was led by @chiaraschiller.bsky.social w help from @kbestak.bsky.social and supervision from @miguelib.bsky.social, @tanevski.bsky.social and @denisschapiro.bsky.social
5/ Real data validation
In myocardial infarction tissues, COZI uniquely detects:
- Early neutrophil infiltration
- Monocyte targeting of stressed cardiomyocytes
- Spatial infiltration gradients
In myocardial infarction tissues, COZI uniquely detects:
- Early neutrophil infiltration
- Monocyte targeting of stressed cardiomyocytes
- Spatial infiltration gradients
April 15, 2025 at 3:50 PM
5/ Real data validation
In myocardial infarction tissues, COZI uniquely detects:
- Early neutrophil infiltration
- Monocyte targeting of stressed cardiomyocytes
- Spatial infiltration gradients
In myocardial infarction tissues, COZI uniquely detects:
- Early neutrophil infiltration
- Monocyte targeting of stressed cardiomyocytes
- Spatial infiltration gradients
4/ One step in the right direction: COZI
COZI (Conditional Z-score) combines advantageous method features by normalizing neighbor counts based on context and providing a z-score. It captures directional preferences and performs robustly across conditions, cell types, and neighborhood structures.
COZI (Conditional Z-score) combines advantageous method features by normalizing neighbor counts based on context and providing a z-score. It captures directional preferences and performs robustly across conditions, cell types, and neighborhood structures.
April 15, 2025 at 3:50 PM
4/ One step in the right direction: COZI
COZI (Conditional Z-score) combines advantageous method features by normalizing neighbor counts based on context and providing a z-score. It captures directional preferences and performs robustly across conditions, cell types, and neighborhood structures.
COZI (Conditional Z-score) combines advantageous method features by normalizing neighbor counts based on context and providing a z-score. It captures directional preferences and performs robustly across conditions, cell types, and neighborhood structures.
3/ The Comparison
Using simulated tissues with known patterns, we show that existing methods often:
- Miss directionality of NEP
- Struggle with low-abundance cell types
- Lack sensitivity to detect subtle NEP changes
Using simulated tissues with known patterns, we show that existing methods often:
- Miss directionality of NEP
- Struggle with low-abundance cell types
- Lack sensitivity to detect subtle NEP changes
April 15, 2025 at 3:50 PM
3/ The Comparison
Using simulated tissues with known patterns, we show that existing methods often:
- Miss directionality of NEP
- Struggle with low-abundance cell types
- Lack sensitivity to detect subtle NEP changes
Using simulated tissues with known patterns, we show that existing methods often:
- Miss directionality of NEP
- Struggle with low-abundance cell types
- Lack sensitivity to detect subtle NEP changes
2/ The problem
Cell-cell spatial relationships are key to tissue function, but comparing NEP across datasets or conditions is tricky. Existing methods differ in how they define neighborhoods, count neighbors, and score NEP—leading to inconsistent results. We identified the common building blocks.
Cell-cell spatial relationships are key to tissue function, but comparing NEP across datasets or conditions is tricky. Existing methods differ in how they define neighborhoods, count neighbors, and score NEP—leading to inconsistent results. We identified the common building blocks.
April 15, 2025 at 3:50 PM
2/ The problem
Cell-cell spatial relationships are key to tissue function, but comparing NEP across datasets or conditions is tricky. Existing methods differ in how they define neighborhoods, count neighbors, and score NEP—leading to inconsistent results. We identified the common building blocks.
Cell-cell spatial relationships are key to tissue function, but comparing NEP across datasets or conditions is tricky. Existing methods differ in how they define neighborhoods, count neighbors, and score NEP—leading to inconsistent results. We identified the common building blocks.
Reposted by Chiara Schiller
Quentin Blampey, Lotte Pollaris, @clarencemah.me Laurens Lehner @chiaraschiller.bsky.social @miguelib.bsky.social
We worked on easing the learning curve of the SpatialData framework by improving the documentation and APIs. We prepared new beginner-friendly notebooks and introduced a new more...
We worked on easing the learning curve of the SpatialData framework by improving the documentation and APIs. We prepared new beginner-friendly notebooks and introduced a new more...
January 20, 2025 at 5:29 PM
Quentin Blampey, Lotte Pollaris, @clarencemah.me Laurens Lehner @chiaraschiller.bsky.social @miguelib.bsky.social
We worked on easing the learning curve of the SpatialData framework by improving the documentation and APIs. We prepared new beginner-friendly notebooks and introduced a new more...
We worked on easing the learning curve of the SpatialData framework by improving the documentation and APIs. We prepared new beginner-friendly notebooks and introduced a new more...
Reposted by Chiara Schiller
@chiaraschiller.bsky.social - Poster 149
December 12, 2024 at 2:58 PM
@chiaraschiller.bsky.social - Poster 149