GSFA Results on LUHMES CROP-seq Data
Kaixuan Luo
Last updated: 2022-08-01
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Knit directory: GSFA_analysis/
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| html | 1aa83d4 | kevinlkx | 2022-08-01 | Build site. |
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GSFA results
The processed dataset consists of 8708 neural progenitor cells that belong to one of the 15 perturbation conditions (CRISPR knock-down of 14 neurodevelopmental genes, and negative control). Top 6000 genes ranked by deviance statistics were kept. And GSFA was performed on the data with 20 factors specified.
Load necessary packages and data
library(data.table)
library(Matrix)
library(tidyverse)
library(ggplot2)
theme_set(theme_bw() + theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
panel.grid.minor = element_blank())
)
library(gridExtra)
library(ComplexHeatmap)
library(kableExtra)
library(WebGestaltR)
source("code/plotting_functions.R")The first thing we need is the output of GSFA fit_gsfa_multivar() run. The lighter version containing just the posterior mean estimates and LFSR of perturbation-gene effects is enough.
data_folder <- "/project2/xinhe/yifan/Factor_analysis/LUHMES/"
fit <- readRDS(paste0(data_folder,
"gsfa_output_detect_01/use_negctrl/All.gibbs_obj_k20.svd_negctrl.seed_14314.light.rds"))
gibbs_PM <- fit$posterior_means
lfsr_mat <- fit$lfsr[, -ncol(fit$lfsr)]
total_effect <- fit$total_effect[, -ncol(fit$total_effect)]
KO_names <- colnames(lfsr_mat)We also need the cell by perturbation matrix which was used as input \(G\) for GSFA.
metadata <- readRDS(paste0(data_folder, "processed_data/merged_metadata.rds"))
G_mat <- metadata[, 4:18]Finally, we load the mapping from gene name to ENSEMBL ID for all 6k genes used in GSFA, as well as selected neuronal marker genes. This is specific to this study and analysis.
feature.names <- data.frame(fread(paste0(data_folder, "GSE142078_raw/GSM4219576_Run2_genes.tsv.gz"),
header = FALSE), stringsAsFactors = FALSE)
genes_df <- feature.names[match(rownames(lfsr_mat), feature.names$V1), ]
names(genes_df) <- c("ID", "Name")
interest_df <- readRDS(paste0(data_folder, "processed_data/selected_neuronal_markers.rds"))Factor ~ Perturbation Association
Perturbation effects on factors
First of all, we look at the estimated effects of gene perturbations on factors inferred by GSFA.
We found that targeting of 7 genes, ADNP, ARID1B, ASH1L, CHD2, DYRK1A, PTEN, and SETD5, has significant effects (PIP > 0.95) on at least 1 of the 20 inferred factors.
All targets and factors (Figure S6A):
dotplot_beta_PIP(t(gibbs_PM$Gamma_pm), t(gibbs_PM$beta_pm),
marker_names = KO_names,
reorder_markers = c(KO_names[KO_names!="Nontargeting"], "Nontargeting"),
inverse_factors = F) +
coord_flip()
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
Here is a closer look at the estimated effects of selected perturbations on selected factors (Figure 5A):
targets <- c("ADNP", "ARID1B", "ASH1L", "CHD2", "DYRK1A", "PTEN", "SETD5")
complexplot_perturbation_factor(gibbs_PM$Gamma_pm[-nrow(gibbs_PM$Gamma_pm), ],
gibbs_PM$beta_pm[-nrow(gibbs_PM$beta_pm), ],
marker_names = KO_names,
reorder_markers = targets,
reorder_factors = c(6, 9, 15))
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
Factor-perturbation association p values
We can also assess the correlations between each pair of perturbation and inferred factor.
The distribution of correlation p values show significant signals.
gibbs_res_tb <- make_gibbs_res_tb(gibbs_PM, G_mat, compute_pve = F)
heatmap_matrix <- gibbs_res_tb %>% select(starts_with("pval"))
rownames(heatmap_matrix) <- 1:nrow(heatmap_matrix)
colnames(heatmap_matrix) <- colnames(G_mat)
summ_pvalues(unlist(heatmap_matrix),
title_text = "GSFA\n(15 Targets x 20 Factors)")
| Version | Author | Date |
|---|---|---|
| 1aa83d4 | kevinlkx | 2022-08-01 |
Factor Interpretation
Correlation within factors
Since the GSFA model does not enforce orthogonality among factors, we first inspect the pairwise correlation within them to see if there is any redundancy. As we can see below, the inferred factors are mostly independent of each other.
plot_pairwise.corr_heatmap(input_mat_1 = gibbs_PM$Z_pm,
corr_type = "pearson",
name_1 = "Pairwise correlation within factors (Z)",
label_size = 10)
| Version | Author | Date |
|---|---|---|
| 1aa83d4 | kevinlkx | 2022-08-01 |
plot_pairwise.corr_heatmap(input_mat_1 = (gibbs_PM$F_pm > 0.95) * 1,
corr_type = "jaccard",
name_1 = "Pairwise correlation within \nbinarized gene loadings (F_pm > 0.95)",
label_size = 10)
Gene loading in factors
To understand these latent factors, we inspect the loadings (weights) of several marker genes for neuron maturation and differentiation in them.
| protein_name | gene_name | type | gene_ID |
|---|---|---|---|
| TP53 | TP53 | Cell proliferation | ENSG00000141510 |
| CDK4 | CDK4 | Cell proliferation | ENSG00000135446 |
| Nestin | NES | Neural progenitor cell | ENSG00000132688 |
| STMN2 | STMN2 | Mature neuron | ENSG00000104435 |
| MAP2 | MAP2 | Mature neuron | ENSG00000078018 |
| DPYSL3 | DPYSL3 | Mature neuron | ENSG00000113657 |
| MAP1B | MAP1B | Mature neuron | ENSG00000131711 |
| CRABP2 | CRABP2 | Mature neuron | ENSG00000143320 |
| NEFL | NEFL | Mature neuron | ENSG00000277586 |
| ZEB2 | ZEB2 | Mature neuron | ENSG00000169554 |
| ITM2C | ITM2C | Negative regulation of neuron projection | ENSG00000135916 |
| CNTN2 | CNTN2 | Negative regulation of neuron projection | ENSG00000184144 |
| DRAXIN | DRAXIN | Negative regulation of neuron projection | ENSG00000162490 |
| HDAC2 | HDAC2 | Negative regulation of neuron projection | ENSG00000196591 |
We visualize both the gene PIPs (dot size) and gene weights (dot color) in all factors (Figure S6B):
complexplot_gene_factor(genes_df, interest_df, gibbs_PM$F_pm, gibbs_PM$W_pm)
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
A closer look at some factors that are associated with perturbations (Figure 5C):
complexplot_gene_factor(genes_df, interest_df, gibbs_PM$F_pm, gibbs_PM$W_pm,
reorder_factors = c(6, 9, 15))
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
GO enrichment analysis in factors
To further characterize these latent factors, we perform GO (gene ontology) enrichment analysis of genes loaded on the factors using WebGestalt.
Foreground genes: genes w/ non-zero loadings in each factor (gene PIP > 0.95);
Background genes: all 6000 genes used in GSFA;
Statistical test: hypergeometric test (over-representation test);
Gene sets: GO Slim "Biological Process" (non-redundant).
## The "WebGestaltR" tool needs Internet connection.
enrich_db <- "geneontology_Biological_Process_noRedundant"
PIP_mat <- gibbs_PM$F_pm
enrich_res_by_factor <- list()
for (i in 1:ncol(PIP_mat)){
enrich_res_by_factor[[i]] <-
WebGestaltR::WebGestaltR(enrichMethod = "ORA",
organism = "hsapiens",
enrichDatabase = enrich_db,
interestGene = genes_df[PIP_mat[, i] > 0.95, ]$ID,
interestGeneType = "ensembl_gene_id",
referenceGene = genes_df$ID,
referenceGeneType = "ensembl_gene_id",
isOutput = F)
}Several GO “biological process” terms related to neuronal development are enriched in factors 4, 9, and 16 (Figure 5D):
factor_indx <- 6
terms_of_interest <- c("regulation of ion transmembrane transport",
"regulation of trans-synaptic signaling",
"axon development",
"regulation of neuron projection development")
barplot_top_enrich_terms(enrich_res_by_factor[[factor_indx]],
terms_of_interest = terms_of_interest,
str_wrap_length = 35, pval_max = 8, FC_max = 6) +
labs(title = paste0("Factor ", factor_indx),
x = "Fold of enrichment")
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
factor_indx <- 9
terms_of_interest <- c("actin filament organization",
"cell fate commitment",
"axon development",
"regulation of cell morphogenesis")
barplot_top_enrich_terms(enrich_res_by_factor[[factor_indx]],
terms_of_interest = terms_of_interest,
str_wrap_length = 35, pval_max = 8, FC_max = 6) +
labs(title = paste0("Factor ", factor_indx),
x = "Fold of enrichment")
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
factor_indx <- 15
terms_of_interest <- c("developmental growth involved in morphogenesis",
"axon development")
barplot_top_enrich_terms(enrich_res_by_factor[[factor_indx]],
terms_of_interest = terms_of_interest,
str_wrap_length = 35, pval_max = 8, FC_max = 6) +
labs(title = paste0("Factor ", factor_indx),
x = "Fold of enrichment")
| Version | Author | Date |
|---|---|---|
| 6900708 | kevinlkx | 2022-08-01 |
Session Information
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
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
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] lattice_0.20-45 WebGestaltR_0.4.4 kableExtra_1.3.4
[4] ComplexHeatmap_2.6.2 gridExtra_2.3 forcats_0.5.1
[7] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
[10] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
[13] ggplot2_3.3.5 tidyverse_1.3.1 Matrix_1.4-1
[16] data.table_1.14.2
loaded via a namespace (and not attached):
[1] matrixStats_0.61.0 fs_1.5.2 lubridate_1.8.0
[4] doParallel_1.0.17 webshot_0.5.2 RColorBrewer_1.1-3
[7] httr_1.4.2 rprojroot_2.0.2 doRNG_1.8.2
[10] tools_4.0.4 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.2
[16] BiocGenerics_0.36.1 colorspace_2.0-3 GetoptLong_1.0.5
[19] withr_2.5.0 tidyselect_1.1.2 compiler_4.0.4
[22] git2r_0.30.1 cli_3.2.0 rvest_1.0.2
[25] Cairo_1.5-15 xml2_1.3.3 labeling_0.4.2
[28] sass_0.4.1 scales_1.2.0 apcluster_1.4.10
[31] systemfonts_1.0.4 digest_0.6.29 R.utils_2.11.0
[34] svglite_2.0.0 rmarkdown_2.13 pkgconfig_2.0.3
[37] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[40] fastmap_1.1.0 rlang_1.0.2 GlobalOptions_0.1.2
[43] readxl_1.4.0 rstudioapi_0.13 farver_2.1.0
[46] shape_1.4.6 jquerylib_0.1.4 generics_0.1.2
[49] jsonlite_1.8.0 R.oo_1.24.0 magrittr_2.0.3
[52] Rcpp_1.0.9 munsell_0.5.0 S4Vectors_0.28.1
[55] fansi_1.0.3 R.methodsS3_1.8.1 lifecycle_1.0.1
[58] stringi_1.7.6 whisker_0.4 yaml_2.3.5
[61] plyr_1.8.6 parallel_4.0.4 promises_1.2.0.1
[64] crayon_1.5.1 haven_2.5.0 pander_0.6.5
[67] circlize_0.4.14 hms_1.1.1 knitr_1.38
[70] pillar_1.7.0 igraph_1.2.11 rjson_0.2.21
[73] rngtools_1.5.2 reshape2_1.4.4 codetools_0.2-18
[76] stats4_4.0.4 reprex_2.0.1 glue_1.6.2
[79] evaluate_0.15 modelr_0.1.8 foreach_1.5.2
[82] png_0.1-7 vctrs_0.4.1 tzdb_0.3.0
[85] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[88] clue_0.3-60 assertthat_0.2.1 xfun_0.30
[91] broom_0.8.0 later_1.3.0 viridisLite_0.4.0
[94] iterators_1.0.14 IRanges_2.24.1 cluster_2.1.2
[97] workflowr_1.7.0 ellipsis_0.3.2
sessionInfo()R version 4.0.4 (2021-02-15) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
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
attached base packages: [1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages: [1] lattice_0.20-45 WebGestaltR_0.4.4 kableExtra_1.3.4
[4] ComplexHeatmap_2.6.2 gridExtra_2.3 forcats_0.5.1
[7] stringr_1.4.0 dplyr_1.0.8 purrr_0.3.4
[10] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6
[13] ggplot2_3.3.5 tidyverse_1.3.1 Matrix_1.4-1
[16] data.table_1.14.2
loaded via a namespace (and not attached): [1] matrixStats_0.61.0 fs_1.5.2 lubridate_1.8.0
[4] doParallel_1.0.17 webshot_0.5.2 RColorBrewer_1.1-3 [7] httr_1.4.2 rprojroot_2.0.2 doRNG_1.8.2
[10] tools_4.0.4 backports_1.4.1 bslib_0.3.1
[13] utf8_1.2.2 R6_2.5.1 DBI_1.1.2
[16] BiocGenerics_0.36.1 colorspace_2.0-3 GetoptLong_1.0.5
[19] withr_2.5.0 tidyselect_1.1.2 compiler_4.0.4
[22] git2r_0.30.1 cli_3.2.0 rvest_1.0.2
[25] Cairo_1.5-15 xml2_1.3.3 labeling_0.4.2
[28] sass_0.4.1 scales_1.2.0 apcluster_1.4.10
[31] systemfonts_1.0.4 digest_0.6.29 R.utils_2.11.0
[34] svglite_2.0.0 rmarkdown_2.13 pkgconfig_2.0.3
[37] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[40] fastmap_1.1.0 rlang_1.0.2 GlobalOptions_0.1.2 [43] readxl_1.4.0 rstudioapi_0.13 farver_2.1.0
[46] shape_1.4.6 jquerylib_0.1.4 generics_0.1.2
[49] jsonlite_1.8.0 R.oo_1.24.0 magrittr_2.0.3
[52] Rcpp_1.0.9 munsell_0.5.0 S4Vectors_0.28.1
[55] fansi_1.0.3 R.methodsS3_1.8.1 lifecycle_1.0.1
[58] stringi_1.7.6 whisker_0.4 yaml_2.3.5
[61] plyr_1.8.6 parallel_4.0.4 promises_1.2.0.1
[64] crayon_1.5.1 haven_2.5.0 pander_0.6.5
[67] circlize_0.4.14 hms_1.1.1 knitr_1.38
[70] pillar_1.7.0 igraph_1.2.11 rjson_0.2.21
[73] rngtools_1.5.2 reshape2_1.4.4 codetools_0.2-18
[76] stats4_4.0.4 reprex_2.0.1 glue_1.6.2
[79] evaluate_0.15 modelr_0.1.8 foreach_1.5.2
[82] png_0.1-7 vctrs_0.4.1 tzdb_0.3.0
[85] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[88] clue_0.3-60 assertthat_0.2.1 xfun_0.30
[91] broom_0.8.0 later_1.3.0 viridisLite_0.4.0
[94] iterators_1.0.14 IRanges_2.24.1 cluster_2.1.2
[97] workflowr_1.7.0 ellipsis_0.3.2