Author: Brian M. Schilder
Most recent update: Jun-21-2021
Intro
When trying to re-analyze single-cell RNA − seq data that has previously been annotated, the same cell-types are not usually labeled in the same way (e.g. “Purkinje cells” vs. “purkinje neurons” vs. “pkj_neurons”). This makes harmonizing data across multiple source quite challenging. One solution is to re-annotate all cells yourself. Alternatively, you can re-use the existing cell-type labels with natural language processing (NLP).
Term frequency–inverse document frequency (tf-idf) is an NLP technique to identify words or phrases that are enriched in one document relative to some other larger set of documents.
In our case, our words are within the non-standardized cell labels and our “documents” are the clusters. The goals is to find words that are enriched in each cluster relative to all the other clusters. This can be thought of as an NLP equivalent of finding gene markers for each cluster.
Quick examples
td-idf annotation
seurat_tfidf will run tf-idf on each cluster and put the results in the enriched_words and tf_idf cols of the meta.data.
pseudo_seurat <- run_tfidf(object = pseudo_seurat,
reduction = "UMAP",
cluster_var = "cluster",
label_var = "celltype") ## Loading required package: SeuratObject
## [1] "+ Extracting data from Seurat object."
## [1] "+ Using reduction: umap"
## Joining, by = "word"
## Joining, by = "cluster"
## Joining, by = "cluster"
head(pseudo_seurat@meta.data)## cluster batch species dataset celltype label
## human.DRONC_human.ASC1 5 DRONC_human human DRONC_human ASC1 ASC1
## human.DRONC_human.ASC2 5 DRONC_human human DRONC_human ASC2 ASC2
## human.DRONC_human.END 9 DRONC_mouse mouse DRONC_mouse END END
## human.DRONC_human.exCA1 0 DRONC_human human DRONC_human exCA1 exCA1
## human.DRONC_human.exCA3 0 DRONC_human human DRONC_human exCA3 exCA3
## human.DRONC_human.exDG 0 DRONC_human human DRONC_human exDG exDG
## nCount_RNA nFeature_RNA RNA_snn_res.0.8 seurat_clusters
## human.DRONC_human.ASC1 237.9283 671 5 5
## human.DRONC_human.ASC2 232.7748 638 5 5
## human.DRONC_human.END 288.9631 616 9 9
## human.DRONC_human.exCA1 245.1858 669 0 0
## human.DRONC_human.exCA3 225.1575 675 0 0
## human.DRONC_human.exDG 237.8823 678 0 0
## UMAP_1 UMAP_2 enriched_words
## human.DRONC_human.ASC1 -0.4796632 0.17629431 asc1; asc2; ast
## human.DRONC_human.ASC2 -0.6386602 -0.05231967 asc1; asc2; ast
## human.DRONC_human.END -7.7066403 -1.84134831 vascular; peric; pericytes
## human.DRONC_human.exCA1 6.2326443 1.51104526 lpn; adpn; ex6a
## human.DRONC_human.exCA3 6.0303471 1.47096417 lpn; adpn; ex6a
## human.DRONC_human.exDG 5.9316036 1.49563257 lpn; adpn; ex6a
## tf_idf
## human.DRONC_human.ASC1 0.0917983119017698
## human.DRONC_human.ASC2 0.0917983119017698
## human.DRONC_human.END 0.154992540041713; 0.10415577696547
## human.DRONC_human.exCA1 0.0644773857405288; 0.06396517525531; 0.0429849238270192
## human.DRONC_human.exCA3 0.0644773857405288; 0.06396517525531; 0.0429849238270192
## human.DRONC_human.exDG 0.0644773857405288; 0.06396517525531; 0.0429849238270192td-idf scatter plot
You can also plot the results in reduced dimensional space (e.g. UMAP). plot_tfidf() will produce a list with three items. - data: The processed data used to create the plot. - tfidf_df: The full per-cluster TF-IDF enrichment results. - plot: The ggplot.
Seurat input
res <- plot_tfidf(object = pseudo_seurat,
label_var = "celltype",
cluster_var = "cluster",
show_plot = T)## [1] "+ Extracting data from Seurat object."
## [1] "+ Using reduction: umap"
## Joining, by = "word"
## Joining, by = "cluster"
## Joining, by = "cluster"
## Warning: Ignoring unknown aesthetics: label
Session Info
utils::sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.4 tidytext_0.3.1 SeuratObject_4.0.2 scNLP_0.1.0
##
## loaded via a namespace (and not attached):
## [1] MatrixGenerics_1.4.0 Biobase_2.52.0
## [3] maps_3.3.0 assertthat_0.2.1
## [5] expm_0.999-6 highr_0.9
## [7] gld_2.6.2 lmom_2.8
## [9] stats4_4.1.0 GenomeInfoDbData_1.2.6
## [11] ggrepel_0.9.1 yaml_2.2.1
## [13] pillar_1.6.1 lattice_0.20-44
## [15] glue_1.4.2 digest_0.6.27
## [17] GenomicRanges_1.44.0 RColorBrewer_1.1-2
## [19] XVector_0.32.0 colorspace_2.0-1
## [21] htmltools_0.5.1.1 Matrix_1.3-4
## [23] pkgconfig_2.0.3 zlibbioc_1.38.0
## [25] purrr_0.3.4 mvtnorm_1.1-2
## [27] scales_1.1.1 rootSolve_1.8.2.1
## [29] tibble_3.1.2 proxy_0.4-26
## [31] generics_0.1.0 farver_2.1.0
## [33] IRanges_2.26.0 ellipsis_0.3.2
## [35] withr_2.4.2 SummarizedExperiment_1.22.0
## [37] BiocGenerics_0.38.0 magrittr_2.0.1
## [39] crayon_1.4.1 evaluate_0.14
## [41] tokenizers_0.2.1 janeaustenr_0.1.5
## [43] fansi_0.5.0 MASS_7.3-54
## [45] SnowballC_0.7.0 class_7.3-19
## [47] tools_4.1.0 data.table_1.14.0
## [49] lifecycle_1.0.0 matrixStats_0.59.0
## [51] stringr_1.4.0 Exact_2.1
## [53] S4Vectors_0.30.0 munsell_0.5.0
## [55] DelayedArray_0.18.0 isoband_0.2.4
## [57] compiler_4.1.0 e1071_1.7-7
## [59] GenomeInfoDb_1.28.0 rlang_0.4.11
## [61] grid_4.1.0 RCurl_1.98-1.3
## [63] dichromat_2.0-0 rstudioapi_0.13
## [65] labeling_0.4.2 bitops_1.0-7
## [67] rmarkdown_2.9 boot_1.3-28
## [69] DescTools_0.99.42 gtable_0.3.0
## [71] DBI_1.1.1 R6_2.5.0
## [73] knitr_1.33 dplyr_1.0.6
## [75] utf8_1.2.1 pals_1.7
## [77] stringi_1.6.2 parallel_4.1.0
## [79] Rcpp_1.0.6 vctrs_0.3.8
## [81] mapproj_1.2.7 tidyselect_1.1.1
## [83] xfun_0.24