Getting started with harmony
2024-04-14
Last updated: 2024-04-14
Checks: 7 0
Knit directory: muse/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200712) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 95fa6fd. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rproj.user/
Ignored: r_packages_4.3.0/
Ignored: r_packages_4.3.2/
Ignored: r_packages_4.3.3/
Untracked files:
Untracked: analysis/breast_cancer.Rmd
Untracked: code/multiz100way/
Untracked: data/lung_bcell.rds
Untracked: data/pbmc3k.csv
Untracked: data/pbmc3k.csv.gz
Untracked: data/pbmc3k/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/harmony.Rmd) and HTML
(docs/harmony.html) files. If you’ve configured a remote
Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 95fa6fd | Dave Tang | 2024-04-14 | Create some plots |
| html | 9abb7b6 | Dave Tang | 2024-04-14 | Build site. |
| Rmd | 72ffea9 | Dave Tang | 2024-04-14 | Getting started with harmony |
Quickstart
Follow the quickstart tutorial
install.packages("harmony")Load {harmony}.
library("harmony")Loading required package: RcpppackageVersion("harmony")[1] '1.2.0'Data
We library normalized the cells, log transformed the counts, and scaled the genes. Then we performed PCA and kept the top 20 PCs. The PCA embeddings and meta data are available as part of this package.
data(cell_lines)
V <- cell_lines$scaled_pcs
meta_data <- cell_lines$meta_data
str(cell_lines)List of 2
$ meta_data : tibble [2,370 × 5] (S3: tbl_df/tbl/data.frame)
..$ cell_id : chr [1:2370] "half_GTACGAACCACCAA" "t293_AGGTCATGCACTTT" "half_ATAGTTGACTTCTA" "half_GAGCGGCTTGCTTT" ...
..$ dataset : chr [1:2370] "half" "t293" "half" "half" ...
..$ nGene : int [1:2370] 1508 4009 3545 2450 2388 3762 3792 4089 3374 3023 ...
..$ percent_mito: num [1:2370] 0.0148 0.0232 0.0153 0.017 0.0601 ...
..$ cell_type : chr [1:2370] "jurkat" "t293" "jurkat" "jurkat" ...
..- attr(*, ".internal.selfref")=<externalptr>
$ scaled_pcs:Classes 'data.table' and 'data.frame': 2370 obs. of 20 variables:
..$ X1 : num [1:2370] 0.00281 -0.01167 0.00933 0.00634 0.00855 ...
..$ X2 : num [1:2370] -0.00145 0.000877 -0.006972 -0.002518 0.007087 ...
..$ X3 : num [1:2370] -0.00639 0.000897 -0.002599 -0.00439 -0.002254 ...
..$ X4 : num [1:2370] 0.000282 0.001324 0.001882 0.000274 0.001679 ...
..$ X5 : num [1:2370] 0.00144 -0.00329 -0.0038 -0.0025 0.00455 ...
..$ X6 : num [1:2370] 0.000752 0.001303 -0.000347 0.000435 0.0003 ...
..$ X7 : num [1:2370] -0.00283 -0.00198 -0.00157 0.00136 -0.0016 ...
..$ X8 : num [1:2370] -0.000653 0.001625 -0.003272 -0.00263 -0.000263 ...
..$ X9 : num [1:2370] 0.001411 -0.000913 -0.001031 -0.001876 0.001389 ...
..$ X10: num [1:2370] -0.000417 -0.000175 -0.001623 -0.000425 0.000391 ...
..$ X11: num [1:2370] 0.001652 -0.000034 0.001241 -0.000458 -0.001444 ...
..$ X12: num [1:2370] 6.71e-05 3.76e-04 -7.61e-04 -6.52e-04 -2.44e-03 ...
..$ X13: num [1:2370] 0.000542 0.000219 -0.001502 -0.002067 -0.000907 ...
..$ X14: num [1:2370] 0.001223 0.001688 -0.000279 -0.000927 -0.000135 ...
..$ X15: num [1:2370] 0.002081 0.000386 -0.001141 0.001114 0.001015 ...
..$ X16: num [1:2370] 1.87e-03 -1.50e-03 5.99e-04 -1.98e-05 -1.25e-03 ...
..$ X17: num [1:2370] 0.000429 0.000259 0.001224 -0.001069 -0.001165 ...
..$ X18: num [1:2370] 0.00115 -0.00106 0.00145 0.00028 0.00111 ...
..$ X19: num [1:2370] -1.09e-03 4.11e-04 7.41e-05 9.33e-04 -1.76e-04 ...
..$ X20: num [1:2370] 0.000265 -0.00171 -0.000662 0.000365 0.000477 ...
..- attr(*, ".internal.selfref")=<externalptr> Cell types.
table(cell_lines$meta_data$cell_type)
jurkat t293
1266 1104 Dataset.
table(cell_lines$meta_data$dataset)
half jurkat t293
846 824 700 Analysis
Initially, the cells cluster by both dataset (left) and cell type
(right). The quickstart guide uses the do_scatter()
function, which is missing.
We can simply plot the first two PCs using {ggplot2}.
Plot PC1 versus PC2.
my_df <- data.frame(PC1 = V$X1, PC2 = V$X2, dataset = meta_data$dataset, cell_type = meta_data$cell_type)
ggplot(my_df, aes(PC1, PC2, colour = dataset)) +
geom_point() +
theme_minimal() +
ggtitle("Before harmony") -> p1
ggplot(my_df, aes(PC1, PC2, colour = cell_type)) +
geom_point() +
theme_minimal() -> p2
p1 + p2
Let’s run Harmony to remove the influence of dataset-of-origin from the cell embeddings.
harmony_embeddings <- harmony::RunHarmony(
V, meta_data, 'dataset', verbose=FALSE
)
my_df2 <- data.frame(PC1 = harmony_embeddings[, 1], PC2 = harmony_embeddings[, 2], dataset = meta_data$dataset, cell_type = meta_data$cell_type)
ggplot(my_df2, aes(PC1, PC2, colour = dataset)) +
geom_point() +
theme_minimal() +
ggtitle("After harmony") -> p1
ggplot(my_df2, aes(PC1, PC2, colour = cell_type)) +
geom_point() +
theme_minimal() -> p2
p1 + p2
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] harmony_1.2.0 Rcpp_1.0.12 patchwork_1.2.0 lubridate_1.9.3
[5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.0
[13] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.4 xfun_0.43 bslib_0.7.0
[4] processx_3.8.4 lattice_0.22-5 callr_3.7.6
[7] tzdb_0.4.0 vctrs_0.6.5 tools_4.3.3
[10] ps_1.7.6 generics_0.1.3 fansi_1.0.6
[13] highr_0.10 pkgconfig_2.0.3 Matrix_1.6-5
[16] lifecycle_1.0.4 compiler_4.3.3 farver_2.1.1
[19] git2r_0.33.0 munsell_0.5.1 RhpcBLASctl_0.23-42
[22] getPass_0.2-4 codetools_0.2-19 httpuv_1.6.15
[25] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.8
[28] later_1.3.2 pillar_1.9.0 jquerylib_0.1.4
[31] whisker_0.4.1 cachem_1.0.8 tidyselect_1.2.1
[34] digest_0.6.35 stringi_1.8.3 labeling_0.4.3
[37] cowplot_1.1.3 rprojroot_2.0.4 fastmap_1.1.1
[40] grid_4.3.3 colorspace_2.1-0 cli_3.6.2
[43] magrittr_2.0.3 utf8_1.2.4 withr_3.0.0
[46] scales_1.3.0 promises_1.3.0 timechange_0.3.0
[49] rmarkdown_2.26 httr_1.4.7 hms_1.1.3
[52] evaluate_0.23 knitr_1.46 rlang_1.1.3
[55] glue_1.7.0 rstudioapi_0.16.0 jsonlite_1.8.8
[58] R6_2.5.1 fs_1.6.3