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Source  : dfc6f2a
Branch  : main
Author  : Andrew Ghazi <[email protected]>
Time    : 2024-10-02 13:26:05 +0000
Message : Merge pull request #47 from ccb-hms/exercise_design

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actions-user committed Oct 2, 2024
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2 changes: 1 addition & 1 deletion cell_type_annotation.md
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Expand Up @@ -1978,7 +1978,7 @@ Use `BiocParallel` and the `BPPARAM` argument! This example will set it to use f
``` r
library(BiocParallel)

my_bpparam = MulticoreParam(workers = 4)
my_bpparam <- MulticoreParam(workers = 4)

res2 <- SingleR(test = sce.mat,
ref = ref.mat,
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8 changes: 5 additions & 3 deletions eda_qc.md
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Expand Up @@ -85,8 +85,8 @@ bcrank <- barcodeRanks(counts(sce))
# Only showing unique points for plotting speed.
uniq <- !duplicated(bcrank$rank)

line_df = data.frame(cutoff = names(metadata(bcrank)),
value = unlist(metadata(bcrank)))
line_df <- data.frame(cutoff = names(metadata(bcrank)),
value = unlist(metadata(bcrank)))

ggplot(bcrank[uniq,], aes(rank, total)) +
geom_point() +
Expand All @@ -107,6 +107,8 @@ A simple approach would be to apply a threshold on the total count to only retai

::: callout
Depending on your data source, identifying and discarding empty droplets may not be necessary. Some academic institutions have research cores dedicated to single cell work that perform the sample preparation and sequencing. Many of these cores will also perform empty droplet filtering and other initial QC steps. If the sequencing outputs were provided to you by someone else, make sure to communicate with them about what pre-processing steps have been performed, if any.

<!-- TODO: cite official 10x CellRanger docs -->
:::

:::: challenge
Expand Down Expand Up @@ -973,7 +975,7 @@ e.out <- emptyDrops(counts(sce))
sce <- sce[,which(e.out$FDR <= 0.001)]

# Thankfully the data come with gene symbols, which we can use to identify mitochondrial genes:
is.mito = grepl("^MT-", rowData(sce)$Symbol)
is.mito <- grepl("^MT-", rowData(sce)$Symbol)

# QC metrics ----
df <- perCellQCMetrics(sce, subsets = list(Mito = is.mito))
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2 changes: 1 addition & 1 deletion hca.md
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Expand Up @@ -250,7 +250,7 @@ For the sake of demonstration, we'll focus this small subset of samples:


``` r
sample_subset = metadata |>
sample_subset <- metadata |>
filter(
ethnicity == "African" &
grepl("10x", assay) &
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6 changes: 3 additions & 3 deletions intro-sce.md
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Expand Up @@ -318,9 +318,9 @@ The `SingleCellExperiment` constructor function can be used to create a new `Sin


``` r
mat = matrix(runif(30), ncol = 5)
mat <- matrix(runif(30), ncol = 5)

my_sce = SingleCellExperiment(assays = list(logcounts = mat))
my_sce <- SingleCellExperiment(assays = list(logcounts = mat))

my_sce$my_col_info = runif(5)

Expand Down Expand Up @@ -359,7 +359,7 @@ sce <- WTChimeraData(samples = 5)

sce6 <- WTChimeraData(samples = 6)

combined_sce = cbind(sce, sce6)
combined_sce <- cbind(sce, sce6)

combined_sce
```
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82 changes: 42 additions & 40 deletions large_data.md
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Expand Up @@ -438,22 +438,21 @@ table(exact = colLabels(sce), approx = clusters)

``` output
approx
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 90 0 0 0 4 0 0 0 1 0 0 0 0 0 0
2 0 143 0 0 0 0 0 0 0 0 0 0 0 0 1
3 0 0 77 0 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 341 0 0 0 0 0 0 0 0 0 0 0
5 0 0 0 0 388 0 0 0 0 1 0 1 0 0 0
6 0 0 0 0 0 208 1 0 0 0 1 0 0 0 0
7 0 0 0 0 0 1 244 0 0 1 0 0 0 0 0
8 0 0 0 0 1 0 0 91 0 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 113 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0 153 0 0 0 0
12 0 0 0 0 2 0 0 0 0 0 0 218 0 0 0
13 0 0 0 0 0 0 0 0 0 0 0 0 146 0 0
14 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0
15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 56
exact 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 90 0 0 0 0 0 0 0 1 0 0 0 0 0
2 0 143 0 1 0 0 0 0 0 0 0 0 0 0
3 0 0 77 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 397 0 0 0 0 0 0 0 0 0 0
5 0 0 0 0 393 0 0 2 0 0 0 5 0 0
6 0 0 0 0 0 204 6 0 0 0 1 0 0 0
7 0 0 0 0 0 0 245 0 0 1 0 0 0 0
8 0 0 0 0 1 0 0 93 0 0 0 0 0 0
9 1 0 0 0 1 0 0 0 106 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 116 2 1 0 0
11 0 0 0 0 0 0 0 0 0 2 139 0 6 0
12 0 0 0 0 1 0 0 0 0 0 0 210 0 0
13 0 0 0 0 0 0 0 0 0 0 0 0 146 0
14 0 0 0 0 0 0 0 0 0 0 0 0 0 20
```

The similarity of the two clusterings can be quantified by calculating the pairwise Rand index:
Expand Down Expand Up @@ -633,8 +632,8 @@ From there we can visualize the error with a histogram:


``` r
error = reducedDim(r.out, "PCA")[,"PC1"] -
reducedDim(e.out, "PCA")[,"PC1"]
error <- reducedDim(r.out, "PCA")[,"PC1"] -
reducedDim(e.out, "PCA")[,"PC1"]

data.frame(approx_error = error) |>
ggplot(aes(approx_error)) +
Expand Down Expand Up @@ -962,47 +961,50 @@ function for writing to HDF5 from the *[HDF5Array](https://bioconductor.org/pack


``` r
wt_out = tempfile(fileext = ".h5")
wt_out <- tempfile(fileext = ".h5")

wt_counts = counts(WTChimeraData())
wt_counts <- counts(WTChimeraData())
```

``` error
Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'counts': failed to load resource
name: EH2973
title: WT chimera processed counts (sample 9)
reason: 1 resources failed to download
```

``` r
writeHDF5Array(wt_counts,
name = "wt_counts",
file = wt_out)
```

``` output
<29453 x 30703> sparse HDF5Matrix object of type "double":
cell_1 cell_2 cell_3 ... cell_30702 cell_30703
ENSMUSG00000051951 0 0 0 . 0 0
ENSMUSG00000089699 0 0 0 . 0 0
ENSMUSG00000102343 0 0 0 . 0 0
ENSMUSG00000025900 0 0 0 . 0 0
ENSMUSG00000025902 0 0 0 . 0 0
... . . . . . .
ENSMUSG00000095041 0 1 2 . 0 0
ENSMUSG00000063897 0 0 0 . 0 0
ENSMUSG00000096730 0 0 0 . 0 0
ENSMUSG00000095742 0 0 0 . 0 0
tomato-td 1 0 1 . 0 0
``` error
Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'is_sparse': object 'wt_counts' not found
```

``` r
oom_wt = HDF5Array(wt_out, "wt_counts")
oom_wt <- HDF5Array(wt_out, "wt_counts")
```

``` error
Error in file_path_as_absolute(path): file '/tmp/Rtmp6Q9gmL/file1e892f562866.h5' does not exist
```

``` r
object.size(wt_counts)
```

``` output
1520366960 bytes
``` error
Error in eval(expr, envir, enclos): object 'wt_counts' not found
```

``` r
object.size(oom_wt)
```

``` output
2488 bytes
``` error
Error in eval(expr, envir, enclos): object 'oom_wt' not found
```

:::::::::::::::::::::::
Expand All @@ -1028,7 +1030,7 @@ Use the function `system.time` to obtain the runtime of each job.


``` r
sce.brain = logNormCounts(sce.brain)
sce.brain <- logNormCounts(sce.brain)

system.time({i.out <- runPCA(sce.brain,
ncomponents = 20,
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10 changes: 5 additions & 5 deletions md5sum.txt
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Expand Up @@ -4,12 +4,12 @@
"config.yaml" "b0d664d3d6abdd0e98b16282e1c03107" "site/built/config.yaml" "2024-09-24"
"index.md" "495939ddd3f110be3bbcd49b60f4a7ce" "site/built/index.md" "2024-09-24"
"links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2024-09-24"
"episodes/intro-sce.Rmd" "2e2c4be36a8f7c2d803ca58200ee1e6d" "site/built/intro-sce.md" "2024-09-24"
"episodes/eda_qc.Rmd" "17151682c663ca6f41832a562e5cdc6d" "site/built/eda_qc.md" "2024-09-24"
"episodes/cell_type_annotation.Rmd" "dc23fda097f772bec1b7172277298221" "site/built/cell_type_annotation.md" "2024-09-30"
"episodes/intro-sce.Rmd" "709fc538c9872b9494fa37f1059ea4a0" "site/built/intro-sce.md" "2024-10-02"
"episodes/eda_qc.Rmd" "b4800ddfe2d5deb5047311658f254e6d" "site/built/eda_qc.md" "2024-10-02"
"episodes/cell_type_annotation.Rmd" "5bd585c6e4c6fc09a7443ce4da35899f" "site/built/cell_type_annotation.md" "2024-10-02"
"episodes/multi-sample.Rmd" "4711a38fd8b29961424215dd17fb7528" "site/built/multi-sample.md" "2024-09-30"
"episodes/large_data.Rmd" "b9710492c6792ea435778c4e42f27e02" "site/built/large_data.md" "2024-09-24"
"episodes/hca.Rmd" "20f753a47fcae8ed5d0631fbc582f549" "site/built/hca.md" "2024-09-30"
"episodes/large_data.Rmd" "f19fa53e9e63d4cb8fe0f6ab61c8fc3a" "site/built/large_data.md" "2024-10-02"
"episodes/hca.Rmd" "3f2af9dc9e53fd617512a37db87f20a7" "site/built/hca.md" "2024-10-02"
"instructors/instructor-notes.md" "205339793f625a1844a768dea8e4a9c8" "site/built/instructor-notes.md" "2024-09-24"
"learners/reference.md" "40fc1d0be2412d2d9d434a5bc84e4de8" "site/built/reference.md" "2024-09-24"
"learners/setup.md" "25772142a26fe3c0cebbe650f5683269" "site/built/setup.md" "2024-09-24"
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