From 8b3e092f207a6403668e8b9fcd3371d14fa39bfe Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Fri, 4 Oct 2024 17:48:36 +0000
Subject: [PATCH] markdown source builds

Auto-generated via {sandpaper}
Source  : 62aa52b0bac743acad8ad048126e8634bf9e43ca
Branch  : main
Author  : Andrew Ghazi <6763470+andrewGhazi@users.noreply.github.com>
Time    : 2024-10-04 17:07:47 +0000
Message : Merge pull request #53 from ccb-hms/eval_meta_ex

eval exercises
---
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 ...-19_b08fde96fe5bd5a4a590d08d794c4cb2.RData | Bin 2781 -> 0 bytes
 hca.md                                        |  68 +++++++++++++++
 large_data.md                                 |  79 +++++++++---------
 md5sum.txt                                    |   4 +-
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similarity index 100%
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diff --git a/hca.md b/hca.md
index f9398c9..2c2048e 100644
--- a/hca.md
+++ b/hca.md
@@ -369,6 +369,23 @@ metadata |>
     count(tissue) |>
     arrange(-n)
 ```
+
+``` output
+# A tibble: 33 × 2
+   tissue                          n
+   <chr>                       <int>
+ 1 cortex of kidney            36940
+ 2 kidney                      23549
+ 3 lung parenchyma             16719
+ 4 renal medulla                7729
+ 5 respiratory airway           7153
+ 6 blood                        4248
+ 7 bone marrow                  4113
+ 8 heart left ventricle         1454
+ 9 transition zone of prostate  1140
+10 lung                         1137
+# ℹ 23 more rows
+```
 :::::::::::::::::::::::
 
 :::::::::::::::::::::::::::::::::::::::::::::
@@ -390,6 +407,13 @@ metadata |>
     arrange(-n) |> 
     head(n = 1)
 ```
+
+``` output
+# A tibble: 1 × 3
+  tissue           cell_type                              n
+  <chr>            <chr>                              <int>
+1 cortex of kidney epithelial cell of proximal tubule 29986
+```
 :::::::::::::::::::::::
 
 :::::::::::::::::::::::::::::::::::::::::::::
@@ -408,12 +432,56 @@ Use `count` to summarise.
 metadata |>
     count(tissue) |>
     arrange(-n)
+```
 
+``` output
+# A tibble: 33 × 2
+   tissue                          n
+   <chr>                       <int>
+ 1 cortex of kidney            36940
+ 2 kidney                      23549
+ 3 lung parenchyma             16719
+ 4 renal medulla                7729
+ 5 respiratory airway           7153
+ 6 blood                        4248
+ 7 bone marrow                  4113
+ 8 heart left ventricle         1454
+ 9 transition zone of prostate  1140
+10 lung                         1137
+# ℹ 23 more rows
+```
+
+``` r
 metadata |>
     count(tissue_harmonised) |>
     arrange(-n)
 ```
 
+``` output
+# A tibble: 19 × 2
+   tissue_harmonised     n
+   <chr>             <int>
+ 1 kidney            68851
+ 2 lung              25737
+ 3 blood              4248
+ 4 bone               4113
+ 5 heart              1454
+ 6 lymph node         1210
+ 7 prostate           1156
+ 8 intestine large     816
+ 9 liver               793
+10 thymus              753
+11 intestine small     530
+12 eye                 437
+13 intestine           360
+14 esophagus           334
+15 nose                290
+16 vasculature         143
+17 brain                97
+18 adrenal gland        20
+19 axilla               13
+```
+
 For example you can see that `tissue_harmonised` merges the `cortex of kidney`
 and `kidney` groups in `tissue`.
 
diff --git a/large_data.md b/large_data.md
index b59e334..6e2ae0c 100644
--- a/large_data.md
+++ b/large_data.md
@@ -438,21 +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
-   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
+exact   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15
+   1   90   0   0   0   1   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  75   0   2   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  56
+   5    0   0   0   0 392   0   0   1   0   1   0   0   0   0   0
+   6    0   0   0   0   0  79 131   0   0   0   0   0   0   0   0
+   7    0   0   0   0   0 245   0   0   0   1   0   0   0   0   0
+   8    0   0   0   0   0   0   0  95   0   0   0   0   0   0   0
+   9    1   0   0   0   2   0   0   0 106   0   0   0   0   0   0
+   10   0   0   0   0   0   0   0   0   0 105   0   0   0   0   0
+   11   0   0   0   0   0   0   1   0   0   5 147   0   0   0   0
+   12   0   0   0   0   1   0   0   0   0  23   0 199   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
 ```
 
 The similarity of the two clusterings can be quantified by calculating the pairwise Rand index: 
@@ -565,11 +565,11 @@ accurate, though RSVD is much faster for file-backed matrices.
 
 :::: challenge
 
-The uncertainty from approximation error is sometimes psychologically
-objectionable. "Why can't my computer just give me the right answer?" One way to
-alleviate this feeling is to quantify the approximation error on a small test
-set like the sce we have here. Using the `ExactParam()` class, visualize the
-error in PC1 coordinates compared to the RSVD results.
+The uncertainty from approximation error is sometimes aggravating. "Why can't my
+computer just give me the right answer?" One way to alleviate this feeling is to
+quantify the approximation error on a small test set like the sce we have here.
+Using the `ExactParam()` class, visualize the error in PC1 coordinates compared
+to the RSVD results.
 
 ::: solution
 This code block calculates the exact PCA coordinates. Another thing to note: PC vectors are only identified up to a sign flip. We can see that the RSVD PC1 vector points in the 
@@ -964,47 +964,44 @@ function for writing to HDF5 from the *[HDF5Array](https://bioconductor.org/pack
 wt_out <- tempfile(fileext = ".h5")
 
 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)
 ```
 
-``` 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
+``` 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
 ```
 
 ``` r
 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)
 ```
 
-``` error
-Error in eval(expr, envir, enclos): object 'wt_counts' not found
+``` output
+1520366960 bytes
 ```
 
 ``` r
 object.size(oom_wt)
 ```
 
-``` error
-Error in eval(expr, envir, enclos): object 'oom_wt' not found
+``` output
+2488 bytes
 ```
 
 :::::::::::::::::::::::
diff --git a/md5sum.txt b/md5sum.txt
index 44ccce2..ca19511 100644
--- a/md5sum.txt
+++ b/md5sum.txt
@@ -8,8 +8,8 @@
 "episodes/eda_qc.Rmd" "d1a9a8a578fff2b0e9566ec613da388d" "site/built/eda_qc.md" "2024-10-03"
 "episodes/cell_type_annotation.Rmd" "8fab6e0cbb60d6fe6a67a0004c5ce5ab" "site/built/cell_type_annotation.md" "2024-10-02"
 "episodes/multi-sample.Rmd" "6d47bd1941b7a83000873da8d836fba0" "site/built/multi-sample.md" "2024-10-04"
-"episodes/large_data.Rmd" "f19fa53e9e63d4cb8fe0f6ab61c8fc3a" "site/built/large_data.md" "2024-10-02"
-"episodes/hca.Rmd" "247c19cda5e903d2eb1a5dd547d95506" "site/built/hca.md" "2024-10-03"
+"episodes/large_data.Rmd" "ba9660bc24664972f540bbe5fdc34d08" "site/built/large_data.md" "2024-10-04"
+"episodes/hca.Rmd" "1577de2f4a851c7e99e3a8459230d6f1" "site/built/hca.md" "2024-10-04"
 "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"