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PeteHaitch merged 2 commits into from
Oct 10, 2025
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Update hca-bone-marrow.Rmd #7

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Update hca-bone-marrow.Rmd
PeteHaitch Oct 7, 2025
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PeteHaitch Oct 10, 2025
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4 changes: 2 additions & 2 deletions DESCRIPTION
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
Package: OSCA.workflows
Title: OSCA Workflows
Version: 1.17.2
Date: 2025-09-29
Version: 1.17.3
Date: 2025-10-10
Authors@R: c(
person('Robert', 'Amezquita', role = 'aut'),
person('Aaron', 'Lun', role = 'aut', email="infinite.monkeys.with.keyboards@gmail.com"),
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15 changes: 11 additions & 4 deletions inst/book/hca-bone-marrow.Rmd
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Expand Up @@ -106,7 +106,7 @@ for (i in colnames(blocked.stats)) {

## Data integration

Here we use multiple cores, randomized SVD and approximate nearest-neighbor detection to speed up this step.
Here we use multiple cores, randomized SVD^[The randomized SVD may give slightly different results on different systems, so the MNN-corrected values may themselves vary across systems.] and approximate nearest-neighbor detection to speed up this step.

```{r integration}
library(batchelor)
Expand Down Expand Up @@ -189,15 +189,22 @@ markers.bone <- findMarkers(sce.bone, block = sce.bone$Donor,
direction = 'up', lfc = 1, BPPARAM=bpp)
```

We visualize the top markers for a randomly chosen cluster using a heatmap in Figure \@ref(fig:unref-hca-bone-heatmap).
We visualize the top markers for a randomly chosen cluster^[The exact cluster chosen varies across systems due to the MNN-corrected values themselves varying across systems.] using a heatmap in Figure \@ref(fig:unref-hca-bone-heatmap).
The presence of upregulated genes like _LYZ_, _S100A8_ and _VCAN_ is consistent with a monocyte identity for this cluster.

```{r, echo=FALSE}
cluster.choice <- "2"
# NOTE: The exact cluster varies across systems due to the MNN-corrected values
# themselves varying across systems. This bit of code aims to pick the
# cluster with the intended 'monocyte' identity.
cluster.choice <- which.max(
tapply(
colMeans(logcounts(sce.bone)[c("LYZ", "VCAN", "S100A8", "CTSS"), ]),
colLabels(sce.bone),
median))
```

```{r unref-hca-bone-heatmap, fig.cap=sprintf("Heatmap of log~2~-fold changes for the top marker genes (rows) of cluster %s compared to all other clusters (columns).", cluster.choice)}
top.markers <- markers.bone[["2"]]
top.markers <- markers.bone[[cluster.choice]]
best <- top.markers[top.markers$Top <= 10,]
lfcs <- getMarkerEffects(best)

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