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Adjusted unit tests and fixes patch infiltration bug #52

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merged 1 commit into from Mar 7, 2023
Merged

Adjusted unit tests and fixes patch infiltration bug #52

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2 changes: 1 addition & 1 deletion 08-phenotyping.Rmd
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Expand Up @@ -1369,7 +1369,7 @@ Patient1_001_1199 = "unlabeled", Patient1_001_1200 = "unlabeled",
Patient1_001_1201 = "unlabeled"))

expect_equal(table(spe$celltype),
structure(c(Bcell = 1594L, BnTcell = 2675L, CD4 = 3845L, CD8 = 3504L,
structure(c(Bcell = 1595L, BnTcell = 2674L, CD4 = 3845L, CD8 = 3504L,
Myeloid = 8020L, Neutrophil = 681L, Plasma_cell = 3397L, Stroma = 5306L,
Treg = 1515L, Tumor = 16625L, undefined = 632L), dim = 11L, dimnames = structure(list(
c("Bcell", "BnTcell", "CD4", "CD8", "Myeloid", "Neutrophil",
Expand Down
55 changes: 19 additions & 36 deletions 11-spatial_analysis.Rmd
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Expand Up @@ -302,14 +302,8 @@ detected cellular neighborhoods (CN).

```{r, message=FALSE}
library(tidyverse)
for_plot <- colData(spe) %>% as_tibble() %>%
group_by(cn_celltypes, celltype) %>%
summarize(count = n()) %>%
mutate(freq = count / sum(count)) %>%
pivot_wider(id_cols = cn_celltypes, names_from = celltype,
values_from = freq, values_fill = 0) %>%
ungroup() %>%
select(-cn_celltypes)
for_plot <- prop.table(table(spe$cn_celltypes, spe$celltype),
margin = 1)

pheatmap(for_plot, color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
scale = "column")
Expand Down Expand Up @@ -343,14 +337,8 @@ Also here, we can visualize the cell type composition of each cellular
neighborhood.

```{r, message=FALSE}
for_plot <- colData(spe) %>% as_tibble() %>%
group_by(cn_expression, celltype) %>%
summarize(count = n()) %>%
mutate(freq = count / sum(count)) %>%
pivot_wider(id_cols = cn_expression, names_from = celltype,
values_from = freq, values_fill = 0) %>%
ungroup() %>%
select(-cn_expression)
for_plot <- prop.table(table(spe$cn_expression, spe$celltype),
margin = 1)

pheatmap(for_plot, color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
scale = "column")
Expand Down Expand Up @@ -425,14 +413,8 @@ Similar to the example above, we can now observe the cell type
composition per spatial cluster.

```{r lisaClust-3, message=FALSE}
for_plot <- colData(spe) %>% as_tibble() %>%
group_by(lisa_clusters, celltype) %>%
summarize(count = n()) %>%
mutate(freq = count / sum(count)) %>%
pivot_wider(id_cols = lisa_clusters, names_from = celltype,
values_from = freq, values_fill = 0) %>%
ungroup() %>%
select(-lisa_clusters)
for_plot <- prop.table(table(spe$lisa_clusters, spe$celltype),
margin = 1)

pheatmap(for_plot, color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
scale = "column")
Expand Down Expand Up @@ -641,7 +623,7 @@ plotSpatial(spe,
img_id = "sample_id",
node_size_fix = 0.5) +
theme(legend.position = "none") +
scale_color_manual(values = colors())
scale_color_manual(values = rev(colors()))
```

We can now calculate the fraction of T cells within each tumor patch to roughly
Expand All @@ -654,6 +636,7 @@ colData(spe) %>% as_tibble() %>%
summarize(Tcell_count = sum(celltype == "CD8" | celltype == "CD4"),
patch_size = n(),
Tcell_freq = Tcell_count / patch_size) %>%
filter(!is.na(patch_id)) %>%
ggplot() +
geom_point(aes(log10(patch_size), Tcell_freq, color = sample_id)) +
theme_classic()
Expand Down Expand Up @@ -1024,8 +1007,8 @@ expect_equal(spe$aggregatedNeighbors[1000:1100,],
0, 0, 0, 0, 0, 0, 0, 0, 0, 0))))

expect_equal(table(spe$cn_celltypes),
structure(c(`1` = 11968L, `2` = 5268L, `3` = 3361L, `4` = 7820L,
`5` = 10047L, `6` = 9330L), dim = 6L, dimnames = structure(list(
structure(c(`1` = 11968L, `2` = 5268L, `3` = 3360L, `4` = 7820L,
`5` = 10048L, `6` = 9330L), dim = 6L, dimnames = structure(list(
c("1", "2", "3", "4", "5", "6")), names = ""), class = "table"))

expect_equal(table(spe$cn_expression),
Expand All @@ -1034,22 +1017,22 @@ expect_equal(table(spe$cn_expression),
c("1", "2", "3", "4", "5", "6")), names = ""), class = "table"))

expect_equal(table(spe$lisa_clusters),
structure(c(`1` = 11939L, `2` = 4928L, `3` = 2729L, `4` = 4528L,
`5` = 16574L, `6` = 7096L), dim = 6L, dimnames = structure(list(
structure(c(`1` = 11941L, `2` = 4928L, `3` = 2729L, `4` = 4527L,
`5` = 16574L, `6` = 7095L), dim = 6L, dimnames = structure(list(
c("1", "2", "3", "4", "5", "6")), names = ""), class = "table"))

expect_equal(table(spe$spatial_context),
structure(c(`1` = 6009L, `1_2` = 20L, `1_2_3_5` = 1L, `1_2_3_5_6` = 3L,
structure(c(`1` = 6009L, `1_2` = 20L, `1_2_3_5` = 1L, `1_2_3_5_6` = 2L,
`1_2_3_6` = 3L, `1_2_4` = 4L, `1_2_4_5` = 3L, `1_2_4_5_6` = 18L,
`1_2_4_6` = 199L, `1_2_5` = 26L, `1_2_5_6` = 207L, `1_2_6` = 1514L,
`1_3` = 3L, `1_3_4_5_6` = 8L, `1_3_4_6` = 7L, `1_3_5_6` = 71L,
`1_3_6` = 328L, `1_4_5` = 5L, `1_4_5_6` = 479L, `1_4_6` = 1121L,
`1_3` = 3L, `1_3_4_5_6` = 8L, `1_3_4_6` = 7L, `1_3_5_6` = 72L,
`1_3_6` = 329L, `1_4_5` = 5L, `1_4_5_6` = 479L, `1_4_6` = 1121L,
`1_5` = 40L, `1_5_6` = 2060L, `1_6` = 7806L, `2` = 1646L, `2_3` = 227L,
`2_3_4` = 92L, `2_3_4_5` = 59L, `2_3_5` = 183L, `2_3_5_6` = 38L,
`2_3_6` = 11L, `2_4` = 2669L, `2_4_5` = 906L, `2_4_5_6` = 131L,
`2_4_6` = 780L, `2_5` = 512L, `2_5_6` = 294L, `2_6` = 761L, `3` = 1587L,
`2_4_6` = 780L, `2_5` = 512L, `2_5_6` = 294L, `2_6` = 761L, `3` = 1586L,
`3_4` = 193L, `3_4_5` = 556L, `3_4_5_6` = 49L, `3_4_6` = 8L,
`3_5` = 1884L, `3_5_6` = 321L, `3_6` = 272L, `4` = 1602L, `4_5` = 4914L,
`3_5` = 1885L, `3_5_6` = 321L, `3_6` = 271L, `4` = 1602L, `4_5` = 4914L,
`4_5_6` = 1546L, `4_6` = 1484L, `5` = 3013L, `5_6` = 1424L, `6` = 697L
), dim = 52L, dimnames = structure(list(c("1", "1_2", "1_2_3_5",
"1_2_3_5_6", "1_2_3_6", "1_2_4", "1_2_4_5", "1_2_4_5_6", "1_2_4_6",
Expand All @@ -1065,8 +1048,8 @@ expect_equal(table(spe$spatial_context_filtered),
`1_2_6` = 1514L, `1_4_5_6` = 479L, `1_4_6` = 1121L, `1_5_6` = 2060L,
`1_6` = 7806L, `2` = 1646L, `2_3_5` = 183L, `2_4` = 2669L, `2_4_5` = 906L,
`2_4_5_6` = 131L, `2_4_6` = 780L, `2_5` = 512L, `2_5_6` = 294L,
`2_6` = 761L, `3` = 1587L, `3_4` = 193L, `3_4_5` = 556L, `3_5` = 1884L,
`3_5_6` = 321L, `3_6` = 272L, `4` = 1602L, `4_5` = 4914L, `4_5_6` = 1546L,
`2_6` = 761L, `3` = 1586L, `3_4` = 193L, `3_4_5` = 556L, `3_5` = 1885L,
`3_5_6` = 321L, `3_6` = 271L, `4` = 1602L, `4_5` = 4914L, `4_5_6` = 1546L,
`4_6` = 1484L, `5` = 3013L, `5_6` = 1424L), dim = 29L, dimnames = structure(list(
c("1", "1_2_4_6", "1_2_5_6", "1_2_6", "1_4_5_6", "1_4_6",
"1_5_6", "1_6", "2", "2_3_5", "2_4", "2_4_5", "2_4_5_6",
Expand Down