• About the data sets
  • Run MUSIC
    • 0. Load input data
    • 1. Data preprocessing
    • 2. Model building
    • 3. Pick the number of topics
  • Summarize the results
    • Summarize the results under 20 topics to be comparable to GSFA
    • Summarize the results using the optimal number of topics selected by the score

Last updated: 2022-07-25

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slurm setting

sinteractive --partition=broadwl --account=pi-xinhe --mem=30G --time=10:00:00 --cpus-per-task=10

MUSIC website: https://github.com/bm2-lab/MUSIC

Scripts for running the analysis:

cd /project2/xinhe/kevinluo/GSFA/music_analysis/log

sbatch --mem=50G --cpus-per-task=10 ~/projects/GSFA_analysis/code/run_music_LUHMES_data.sbatch

About the data sets

CROP-seq datasets: /project2/xinhe/yifan/Factor_analysis/shared_data/ The data are Seurat objects, with raw gene counts stored in obj@assays$RNA@counts, and cell meta data stored in obj@meta.data. Normalized and scaled data used for GSFA are stored in obj@assays$RNA@scale.data , the rownames of which are the 6k genes used for GSFA.

Load packages

suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(MUSIC))
suppressPackageStartupMessages(library(ComplexHeatmap))
suppressPackageStartupMessages(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 = 13),
                             legend.title = element_text(size = 13),
                             legend.text = element_text(size = 12),
                             panel.grid.minor = element_blank())
)

functions

## Adapted over MUSIC's Diff_topic_distri() function
Empirical_topic_prob_diff <- function(model, perturb_information,
                                      permNum = 10^4, seed = 1000){
  require(reshape2)
  require(dplyr)
  require(ComplexHeatmap)
  options(warn = -1)
  prob_mat <- model@gamma
  row.names(prob_mat) <- model@documents
  topicNum <- ncol(prob_mat)
  topicName <- paste0('Topic_', 1:topicNum)
  colnames(prob_mat) <- topicName
  ko_name <- unique(perturb_information)
  prob_df <- data.frame(prob_mat, 
                        samples = rownames(prob_mat),
                        knockout = perturb_information)
  
  prob_df <- melt(prob_df, id = c('samples', 'knockout'), variable.name = "topic")
  
  summary_df <- prob_df %>%
    group_by(knockout, topic) %>%
    summarise(number = sum(value)) %>%
    ungroup() %>%
    group_by(knockout) %>%
    mutate(cellNum = sum(number)) %>%
    ungroup() %>%
    mutate(ratio = number/cellNum)
  
  summary_df$ctrlNum <- rep(summary_df$cellNum[summary_df$knockout == "CTRL"],
                            length(ko_name))
  summary_df$ctrl_ratio <- rep(summary_df$ratio[summary_df$knockout == "CTRL"],
                               length(ko_name))
  summary_df <- summary_df %>% mutate(diff_index = ratio - ctrl_ratio)
  
  test_df <- data.frame(matrix(nrow = length(ko_name) * topicNum, ncol = 5))
  colnames(test_df) <- c("knockout", "topic", "obs_t_stats", "obs_pval", "empirical_pval")
  k <- 1
  for(i in topicName){
    prob_df.topic <- prob_df[prob_df$topic == i, ]
    ctrl_topic <- prob_df.topic$value[prob_df.topic$knockout == "CTRL"]
    ctrl_topic_z <- (ctrl_topic - mean(ctrl_topic)) / sqrt(var(ctrl_topic))
    for(j in ko_name){
      ko_topic <- prob_df.topic$value[prob_df.topic$knockout == j]
      ko_topic_z <- (ko_topic - mean(ctrl_topic)) / sqrt(var(ctrl_topic))
      test_df$knockout[k] <- j
      test_df$topic[k] <- i
      test <- t.test(ko_topic_z, ctrl_topic_z)
      test_df$obs_t_stats[k] <- test$statistic
      test_df$obs_pval[k] <- test$p.value
      k <- k + 1
    }
  }
  
  ## Permutation on the perturbation conditions:
  # permNum <- 10^4
  print(paste0("Performing permutation for ", permNum, " rounds."))
  perm_t_stats <- matrix(0, nrow = nrow(test_df), ncol = permNum)
  set.seed(seed)
  for (perm in 1:permNum){
    perm_prob_df <- data.frame(prob_mat, 
                               samples = rownames(prob_mat),
                               knockout = perturb_information[sample(length(perturb_information))])
    perm_prob_df <- melt(perm_prob_df, id = c('samples', 'knockout'), variable.name = "topic")
    k <- 1
    for(i in topicName){
      perm_prob_df.topic <- perm_prob_df[perm_prob_df$topic == i, ]
      ctrl_topic <- perm_prob_df.topic$value[perm_prob_df.topic$knockout == "CTRL"]
      ctrl_topic_z <- (ctrl_topic - mean(ctrl_topic)) / sqrt(var(ctrl_topic))
      for(j in ko_name){
        ko_topic <- perm_prob_df.topic$value[perm_prob_df.topic$knockout == j]
        ko_topic_z <- (ko_topic - mean(ctrl_topic)) / sqrt(var(ctrl_topic))
        test <- t.test(ko_topic_z, ctrl_topic_z)
        perm_t_stats[k, perm] <- test$statistic
        k <- k + 1
      }
    }
    if (perm %% 1000 == 0){
      print(paste0(perm, " rounds finished."))
    }
  }
  ## Compute two-sided empirical p value:
  for (k in 1:nrow(test_df)){
    test_df$empirical_pval[k] <-
      2 * min(mean(perm_t_stats[k, ] <= test_df$obs_t_stats[k]),
              mean(perm_t_stats[k, ] >= test_df$obs_t_stats[k]))
  }
  test_df <- test_df %>%
    mutate(empirical_pval = ifelse(empirical_pval == 0, 1/permNum, empirical_pval)) %>%
    mutate(empirical_pval = ifelse(empirical_pval > 1, 1, empirical_pval))
  
  summary_df <- inner_join(summary_df, test_df, by = c("knockout", "topic"))
  summary_df <- summary_df %>%
    mutate(polar_log10_pval = ifelse(obs_t_stats > 0, -log10(empirical_pval), log10(empirical_pval)))
  return(summary_df)
}

Set directories

data_dir <- "/project2/xinhe/yifan/Factor_analysis/LUHMES/"
res_dir <- "/project2/xinhe/kevinluo/GSFA/music_analysis/LUHMES"
# setwd(res_dir)
dir.create(file.path(res_dir,"/music_output"), recursive = TRUE, showWarnings = FALSE)

Run MUSIC

0. Load input data

feature.names <- data.frame(fread(paste0(data_dir, "GSE142078_raw/GSM4219575_Run1_genes.tsv.gz"),
                                  header = FALSE), stringsAsFactors = FALSE)

# combined_obj <- readRDS("processed_data/seurat_obj.merged_scaled_detect_01.corrected_new.rds")
combined_obj <- readRDS("/project2/xinhe/yifan/Factor_analysis/shared_data/LUHMES_cropseq_data_seurat.rds")
expression_profile <- combined_obj@assays$RNA@counts
rownames(expression_profile) <- feature.names$V2[match(rownames(expression_profile),
                                                       feature.names$V1)]

cat("Dimension of expression profile matrix: \n")
dim(expression_profile)

targets <- names(combined_obj@meta.data)[4:18]
targets[targets == "Nontargeting"] <- "CTRL"
cat("Targets: \n")
print(targets)

perturb_information <- apply(combined_obj@meta.data[4:18], 1,
                             function(x){ targets[which(x > 0)] })
Dimension of expression profile matrix: 
[1] 33694  8708
Targets: 
 [1] "ADNP"   "ARID1B" "ASH1L"  "CHD2"   "CHD8"   "CTNND2" "DYRK1A" "HDAC5" 
 [9] "MECP2"  "MYT1L"  "CTRL"   "POGZ"   "PTEN"   "RELN"   "SETD5"

1. Data preprocessing

crop_seq_list <- Input_preprocess(expression_profile, perturb_information)

crop_seq_qc <- Cell_qc(crop_seq_list$expression_profile,
                       crop_seq_list$perturb_information,
                       species = "Hs", plot = F)

crop_seq_imputation <- Data_imputation(crop_seq_qc$expression_profile,
                                       crop_seq_qc$perturb_information,
                                       cpu_num = 10)
saveRDS(crop_seq_imputation, "music_output/music_imputation.merged.rds")

crop_seq_filtered <- Cell_filtering(crop_seq_imputation$expression_profile,
                                    crop_seq_imputation$perturb_information,
                                    cpu_num = 10)
saveRDS(crop_seq_filtered, "music_output/music_filtered.merged.rds")

2. Model building

Obtain highly dispersion differentially expressed genes.

crop_seq_filtered <- readRDS("music_output/music_filtered.merged.rds")
dim(crop_seq_filtered$expression_profile)
length(crop_seq_filtered$perturb_information)

crop_seq_vargene <- Get_high_varGenes(crop_seq_filtered$expression_profile,
                                      crop_seq_filtered$perturb_information, plot = T)
saveRDS(crop_seq_vargene, "music_output/music_vargene.merged.rds")

crop_seq_vargene <- readRDS("music_output/music_vargene.merged.rds")
dim(crop_seq_vargene$expression_profile)

crop_seq_vargene$expression_profile[1:5,1:5]
length(crop_seq_vargene$perturb_information)

get topics.

## Get_topics() can take up to a few hours to finish, 
## depending on the size of data
system.time(
  topic_1 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 5))
saveRDS(topic_1, "music_output/music_merged_5_topics.rds")

system.time(
  topic_2 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 10))
saveRDS(topic_2, "music_output/music_merged_10_topics.rds")

system.time(
  topic_3 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 15))
saveRDS(topic_3, "music_output/music_merged_15_topics.rds")

system.time(
  topic_4 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 20))
saveRDS(topic_4, "music_output/music_merged_20_topics.rds")

## try fewer numbers of topics
system.time(
  topic_5 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 4))
saveRDS(topic_5, "music_output/music_merged_4_topics.rds")

system.time(
  topic_6 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 6))
saveRDS(topic_6, "music_output/music_merged_6_topics.rds")

system.time(
  topic_7 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 7))
saveRDS(topic_7, "music_output/music_merged_7_topics.rds")

system.time(
  topic_8 <- Get_topics(crop_seq_vargene$expression_profile,
                        crop_seq_vargene$perturb_information,
                        topic_number = 3))
saveRDS(topic_8, "music_output/music_merged_3_topics.rds")

3. Pick the number of topics

topic_1 <- readRDS("music_output/music_merged_4_topics.rds")
topic_2 <- readRDS("music_output/music_merged_5_topics.rds")
topic_3 <- readRDS("music_output/music_merged_6_topics.rds")
topic_4 <- readRDS("music_output/music_merged_10_topics.rds")
topic_5 <- readRDS("music_output/music_merged_15_topics.rds")
topic_6 <- readRDS("music_output/music_merged_20_topics.rds")

topic_model_list <- list()
topic_model_list$models <- list()
topic_model_list$perturb_information <- topic_1$perturb_information
topic_model_list$models[[1]] <- topic_1$models[[1]]
topic_model_list$models[[2]] <- topic_2$models[[1]]
topic_model_list$models[[3]] <- topic_3$models[[1]]
topic_model_list$models[[4]] <- topic_4$models[[1]]
topic_model_list$models[[5]] <- topic_5$models[[1]]
topic_model_list$models[[6]] <- topic_6$models[[1]]

optimalModel <- Select_topic_number(topic_model_list$models,
                                    plot = T,
                                    plot_path = "music_output/select_topic_number_4to6to20.pdf")

Summarize the results

Summarize the results under 20 topics to be comparable to GSFA

Gene ontology annotations for top topics

topic_res <- readRDS("music_output/music_merged_20_topics.rds")

topic_func <- Topic_func_anno(topic_res$models[[1]], species = "Hs")
saveRDS(topic_func, "music_output/topic_func.rds")
topic_func <- readRDS("music_output/topic_func.rds")
# pdf("music_output/music_merged_20_topics_GO_annotations.pdf",
#     width = 14, height = 12)
ggplot(topic_func$topic_annotation_result) +
  geom_point(aes(x = Cluster, y = Description,
                 size = Count, color = -log10(qvalue))) +
  scale_color_gradientn(colors = c("blue", "red")) +
  theme_bw() +
  theme(axis.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1))
# dev.off()

Perturbation effect prioritizing

# calculate topic distribution for each cell.
distri_diff <- Diff_topic_distri(topic_res$models[[1]],
                                 topic_res$perturb_information,
                                 plot = T)
saveRDS(distri_diff, "music_output/distri_diff.rds")

distri_diff <- readRDS("music_output/distri_diff.rds")
t_D_diff_matrix <- dcast(distri_diff %>% dplyr::select(knockout, variable, t_D_diff),
                         knockout ~ variable)
rownames(t_D_diff_matrix) <- t_D_diff_matrix$knockout
t_D_diff_matrix$knockout <- NULL
# pdf("music_output/music_merged_20_topics_TPD_heatmap.pdf", width = 12, height = 8)
Heatmap(t_D_diff_matrix,
        name = "Topic probability difference (vs ctrl)",
        cluster_rows = T, cluster_columns = T,
        column_names_rot = 45,
        heatmap_legend_param = list(title_gp = gpar(fontsize = 12, fontface = "bold")))
# dev.off()

Overall perturbation effect ranking list.

distri_diff <- readRDS(file.path(res_dir, "music_output/distri_diff.rds"))

rank_overall_result <- Rank_overall(distri_diff)
print(rank_overall_result)
# saveRDS(rank_overall_result, "music_output/rank_overall_result.rds")
   perturbation ranking    Score off_target
1         ASH1L       1 85.91013       none
2         SETD5       2 79.56861       none
3        ARID1B       3 77.59034       none
4          CHD8       4 70.16751       none
5          PTEN       5 66.48328       none
6          POGZ       6 64.12727       none
7         MECP2       7 62.10662       none
8          RELN       8 59.26017       none
9          CHD2       9 55.85265       none
10        MYT1L      10 39.67237       none
11         ADNP      11 39.06289       none
12       DYRK1A      12 38.98911       none
13        HDAC5      13 38.48395       none

topic-specific ranking list.

rank_topic_specific_result <- Rank_specific(distri_diff)
head(rank_topic_specific_result, 10)
# saveRDS(rank_topic_specific_result, "music_output/rank_topic_specific_result.rds")
    topic perturbation ranking
1  Topic1       ARID1B       1
2  Topic1         CHD8       2
3  Topic1        SETD5       3
4  Topic1        MECP2       4
5  Topic1         POGZ       5
6  Topic1         PTEN       6
7  Topic1        HDAC5       7
8  Topic1       DYRK1A       8
9  Topic1         ADNP       9
10 Topic1         RELN      10

perturbation correlation.

perturb_cor <- Correlation_perturbation(distri_diff,
                                        cutoff = 0.5, gene = "all", plot = T,
                                        plot_path = file.path(res_dir, "music_output/correlation_network_20_topics.pdf"))

head(perturb_cor, 10)
# saveRDS(perturb_cor, "music_output/perturb_cor.rds")
   Perturbation_1 Perturbation_2 Correlation
2          ARID1B           ADNP -0.36642339
3           ASH1L           ADNP  0.60467107
16          ASH1L         ARID1B  0.12772609
30           CHD2          ASH1L  0.91852307
4            CHD2           ADNP  0.81036468
17           CHD2         ARID1B  0.03762586
18           CHD8         ARID1B  0.89232727
5            CHD8           ADNP -0.26907399
31           CHD8          ASH1L  0.25264119
44           CHD8           CHD2  0.13836576

Adaptation to the code to generate calibrated empirical TPD scores

summary_df <- Empirical_topic_prob_diff(topic_res$models[[1]],
                                        topic_res$perturb_information)
saveRDS(summary_df, "music_output/music_merged_20_topics_ttest_summary.rds")

summary_df <- readRDS("music_output/music_merged_20_topics_ttest_summary.rds")

log10_pval_mat <- dcast(summary_df %>% dplyr::select(knockout, topic, polar_log10_pval),
                        knockout ~ topic)
rownames(log10_pval_mat) <- log10_pval_mat$knockout
log10_pval_mat$knockout <- NULL
pdf("music_output/music_merged_20_topics_empirical_tstats_heatmap.pdf",
    width = 12, height = 8)
ht <- Heatmap(log10_pval_mat,
              name = "Polarized empirical t-test -log10(p-value)\n(KO vs ctrl cell topic probs)",
              col = circlize::colorRamp2(breaks = c(-4, 0, 4), colors = c("blue", "grey90", "red")),
              cluster_rows = T, cluster_columns = T,
              column_names_rot = 45,
              heatmap_legend_param = list(title_gp = gpar(fontsize = 12,
                                                          fontface = "bold")))
draw(ht)
dev.off()

Summarize the results using the optimal number of topics selected by the score

Pick optimal number of topics

topic_1 <- readRDS("music_output/music_merged_4_topics.rds")
topic_2 <- readRDS("music_output/music_merged_5_topics.rds")
topic_3 <- readRDS("music_output/music_merged_6_topics.rds")
topic_4 <- readRDS("music_output/music_merged_7_topics.rds")

topic_model_list <- list()
topic_model_list$models <- list()
topic_model_list$perturb_information <- topic_1$perturb_information
topic_model_list$models[[1]] <- topic_1$models[[1]]
topic_model_list$models[[2]] <- topic_2$models[[1]]
topic_model_list$models[[3]] <- topic_3$models[[1]]
topic_model_list$models[[4]] <- topic_4$models[[1]]

optimalModel <- Select_topic_number(topic_model_list$models,
                                    plot = T,
                                    plot_path = "music_output/select_topic_number_4to7.pdf")

optimalModel
saveRDS(optimalModel, "music_output/optimalModel_4_topics.rds")

Gene ontology annotations for top topics

topic_func <- Topic_func_anno(optimalModel, species = "Hs", plot_path = "music_output/topic_annotation_GO_4_topics.pdf")
saveRDS(topic_func, "music_output/topic_func_4_topics.rds")
topic_func <- readRDS("music_output/topic_func_4_topics.rds")
pdf("music_output/music_merged_4_topics_GO_annotations.pdf",
    width = 14, height = 12)
ggplot(topic_func$topic_annotation_result) +
  geom_point(aes(x = Cluster, y = Description,
                 size = Count, color = -log10(qvalue))) +
  scale_color_gradientn(colors = c("blue", "red")) +
  theme_bw() +
  theme(axis.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1))
dev.off()

Perturbation effect prioritizing

# calculate topic distribution for each cell.
distri_diff <- Diff_topic_distri(optimalModel,
                                 topic_model_list$perturb_information,
                                 plot = T,
                                 plot_path = "music_output/distribution_of_topic_4_topics.pdf")
saveRDS(distri_diff, "music_output/distri_diff_4_topics.rds")

t_D_diff_matrix <- dcast(distri_diff %>% dplyr::select(knockout, variable, t_D_diff),
                         knockout ~ variable)
rownames(t_D_diff_matrix) <- t_D_diff_matrix$knockout
t_D_diff_matrix$knockout <- NULL
pdf("music_output/music_merged_4_topics_TPD_heatmap.pdf", width = 12, height = 8)
Heatmap(t_D_diff_matrix,
        name = "Topic probability difference (vs ctrl)",
        cluster_rows = T, cluster_columns = T,
        column_names_rot = 45,
        heatmap_legend_param = list(title_gp = gpar(fontsize = 12, fontface = "bold")))
dev.off()

The overall perturbation effect ranking list.

distri_diff <- readRDS(file.path(res_dir, "music_output/distri_diff_4_topics.rds"))

rank_overall_result <- Rank_overall(distri_diff)
print(rank_overall_result)
# saveRDS(rank_overall_result, "music_output/rank_overall_4_topics_result.rds")
   perturbation ranking     Score off_target
1         SETD5       1 32.193659       none
2          CHD8       2 29.778960       none
3         ASH1L       3 27.549804       none
4        ARID1B       4 26.055173       none
5          PTEN       5 21.015782       none
6          RELN       6 19.725323       none
7          POGZ       7 19.274733       none
8         MECP2       8 16.098968       none
9          CHD2       9 15.626195       none
10        MYT1L      10 13.654875       none
11       DYRK1A      11 11.714480       none
12        HDAC5      12  9.397238       none
13         ADNP      13  8.510481       none

Topic-specific ranking list.

rank_topic_specific_result <- Rank_specific(distri_diff)
print(rank_topic_specific_result)
# saveRDS(rank_topic_specific_result, "music_output/rank_topic_specific_4_topics_result.rds")
    topic perturbation ranking
1  Topic1       ARID1B       1
2  Topic1         POGZ       2
3  Topic1         CHD8       3
4  Topic1        MECP2       4
5  Topic1         RELN       5
6  Topic1        SETD5       6
7  Topic1        MYT1L       7
8  Topic1         CHD2       8
9  Topic1        ASH1L       9
10 Topic2        MECP2       1
11 Topic2        MYT1L       2
12 Topic2        HDAC5       3
13 Topic2         RELN       4
14 Topic2        ASH1L       5
15 Topic2         CHD8       6
16 Topic2         PTEN       7
17 Topic2         POGZ       8
18 Topic3        ASH1L       1
19 Topic3         CHD2       2
20 Topic3         PTEN       3
21 Topic3       DYRK1A       4
22 Topic3         ADNP       5
23 Topic3        HDAC5       6
24 Topic3         POGZ       7
25 Topic3        SETD5       8
26 Topic3        MECP2       9
27 Topic3        MYT1L      10
28 Topic4        SETD5       1
29 Topic4         CHD8       2
30 Topic4       ARID1B       3
31 Topic4         RELN       4
32 Topic4         ADNP       5
33 Topic4        MYT1L       6
34 Topic4         POGZ       7
35 Topic4         PTEN       8
36 Topic4       DYRK1A       9

Perturbation correlation.

perturb_cor <- Correlation_perturbation(distri_diff,
                                        cutoff = 0.5, gene = "all", plot = T,
                                        plot_path = file.path(res_dir, "music_output/correlation_network_4_topics.pdf"))

perturb_cor
# saveRDS(perturb_cor, "music_output/perturb_cor_4_topics.rds")
    Perturbation_1 Perturbation_2  Correlation
2           ARID1B           ADNP -0.447523272
3            ASH1L           ADNP  0.808812895
16           ASH1L         ARID1B  0.111932621
30            CHD2          ASH1L  0.993900166
4             CHD2           ADNP  0.838181223
17            CHD2         ARID1B  0.094168491
18            CHD8         ARID1B  0.978666101
5             CHD8           ADNP -0.500026993
31            CHD8          ASH1L  0.092206774
44            CHD8           CHD2  0.052593036
6           DYRK1A           ADNP  0.955029925
45          DYRK1A           CHD2  0.919477277
32          DYRK1A          ASH1L  0.919100625
58          DYRK1A           CHD8 -0.297668475
19          DYRK1A         ARID1B -0.288603303
72           HDAC5         DYRK1A  0.932784067
33           HDAC5          ASH1L  0.874456238
46           HDAC5           CHD2  0.835012221
7            HDAC5           ADNP  0.797584426
20           HDAC5         ARID1B -0.221043654
59           HDAC5           CHD8 -0.157648224
86           MECP2          HDAC5  0.811847524
73           MECP2         DYRK1A  0.672188849
21           MECP2         ARID1B -0.631761939
8            MECP2           ADNP  0.586633586
60           MECP2           CHD8 -0.507132674
34           MECP2          ASH1L  0.433309624
47           MECP2           CHD2  0.378577277
61           MYT1L           CHD8  0.840005209
22           MYT1L         ARID1B  0.799747668
35           MYT1L          ASH1L  0.592301247
48           MYT1L           CHD2  0.540801963
87           MYT1L          HDAC5  0.398597255
74           MYT1L         DYRK1A  0.255949785
100          MYT1L          MECP2 -0.059212860
9            MYT1L           ADNP  0.005279451
101           POGZ          MECP2  0.958316633
23            POGZ         ARID1B -0.805004765
88            POGZ          HDAC5  0.736344855
62            POGZ           CHD8 -0.722861540
75            POGZ         DYRK1A  0.688067491
10            POGZ           ADNP  0.687278591
36            POGZ          ASH1L  0.380222120
49            POGZ           CHD2  0.353597376
114           POGZ          MYT1L -0.288746616
76            PTEN         DYRK1A -0.985212923
50            PTEN           CHD2 -0.962750106
11            PTEN           ADNP -0.951787011
37            PTEN          ASH1L -0.950209735
89            PTEN          HDAC5 -0.877983243
128           PTEN           POGZ -0.561378750
102           PTEN          MECP2 -0.535492807
115           PTEN          MYT1L -0.311745226
63            PTEN           CHD8  0.216529114
24            PTEN         ARID1B  0.178082584
116           RELN          MYT1L  0.974354811
64            RELN           CHD8  0.938901611
25            RELN         ARID1B  0.912206685
129           RELN           POGZ -0.491290755
38            RELN          ASH1L  0.422418671
51            RELN           CHD2  0.377326661
103           RELN          MECP2 -0.264536001
12            RELN           ADNP -0.187659173
90            RELN          HDAC5  0.182050610
142           RELN           PTEN -0.121093154
77            RELN         DYRK1A  0.047179655
65           SETD5           CHD8  0.935879993
26           SETD5         ARID1B  0.893863822
130          SETD5           POGZ -0.793996102
13           SETD5           ADNP -0.772282356
156          SETD5           RELN  0.767194513
117          SETD5          MYT1L  0.619428909
78           SETD5         DYRK1A -0.600332391
104          SETD5          MECP2 -0.590159858
143          SETD5           PTEN  0.543236545
91           SETD5          HDAC5 -0.425593665
52           SETD5           CHD2 -0.300856393
39           SETD5          ASH1L -0.257298553

Adaptation to the code to generate calibrated empirical TPD scores

summary_df <- Empirical_topic_prob_diff(optimalModel,
                                        topic_model_list$perturb_information)
saveRDS(summary_df, "music_output/music_merged_4_topics_ttest_summary.rds")

summary_df <- readRDS("music_output/music_merged_4_topics_ttest_summary.rds")

log10_pval_mat <- dcast(summary_df %>% dplyr::select(knockout, topic, polar_log10_pval),
                        knockout ~ topic)
rownames(log10_pval_mat) <- log10_pval_mat$knockout
log10_pval_mat$knockout <- NULL
pdf("music_output/music_merged_4_topics_empirical_tstats_heatmap.pdf",
    width = 12, height = 8)
ht <- Heatmap(log10_pval_mat,
              name = "Polarized empirical t-test -log10(p-value)\n(KO vs ctrl cell topic probs)",
              col = circlize::colorRamp2(breaks = c(-4, 0, 4), colors = c("blue", "grey90", "red")),
              cluster_rows = T, cluster_columns = T,
              column_names_rot = 45,
              heatmap_legend_param = list(title_gp = gpar(fontsize = 12,
                                                          fontface = "bold")))
draw(ht)
dev.off()

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      stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] igraph_1.2.11          reshape2_1.4.4         gplots_3.1.1          
 [4] entropy_1.3.1          dplyr_1.0.8            ggplot2_3.3.5         
 [7] ComplexHeatmap_2.6.2   MUSIC_1.0              SAVER_1.1.2           
[10] clusterProfiler_3.18.1 hash_2.2.6.2           topicmodels_0.2-12    
[13] Biostrings_2.58.0      XVector_0.30.0         IRanges_2.24.1        
[16] S4Vectors_0.28.1       BiocGenerics_0.36.1    SeuratObject_4.0.4    
[19] Seurat_4.1.0           data.table_1.14.2      workflowr_1.7.0       

loaded via a namespace (and not attached):
  [1] utf8_1.2.2            R.utils_2.11.0        reticulate_1.24      
  [4] tidyselect_1.1.2      RSQLite_2.2.11        AnnotationDbi_1.52.0 
  [7] htmlwidgets_1.5.4     BiocParallel_1.24.1   Rtsne_0.15           
 [10] scatterpie_0.1.7      munsell_0.5.0         codetools_0.2-18     
 [13] ica_1.0-2             future_1.24.0         miniUI_0.1.1.1       
 [16] withr_2.5.0           spatstat.random_2.1-0 colorspace_2.0-3     
 [19] GOSemSim_2.16.1       Biobase_2.50.0        NLP_0.2-1            
 [22] knitr_1.38            rstudioapi_0.13       ROCR_1.0-11          
 [25] tensor_1.5            DOSE_3.16.0           listenv_0.8.0        
 [28] git2r_0.30.1          slam_0.1-50           polyclip_1.10-0      
 [31] bit64_4.0.5           farver_2.1.0          rprojroot_2.0.2      
 [34] downloader_0.4        parallelly_1.31.0     vctrs_0.4.1          
 [37] generics_0.1.2        xfun_0.30             R6_2.5.1             
 [40] clue_0.3-60           graphlayouts_0.8.0    bitops_1.0-7         
 [43] spatstat.utils_2.3-0  cachem_1.0.6          fgsea_1.21.0         
 [46] assertthat_0.2.1      promises_1.2.0.1      scales_1.2.0         
 [49] ggraph_2.0.5          enrichplot_1.10.2     gtable_0.3.0         
 [52] Cairo_1.5-15          globals_0.14.0        processx_3.5.3       
 [55] goftest_1.2-3         tidygraph_1.2.0       rlang_1.0.2          
 [58] GlobalOptions_0.1.2   splines_4.0.4         lazyeval_0.2.2       
 [61] spatstat.geom_2.3-2   BiocManager_1.30.16   yaml_2.3.5           
 [64] abind_1.4-5           httpuv_1.6.5          qvalue_2.22.0        
 [67] tools_4.0.4           ellipsis_0.3.2        spatstat.core_2.4-0  
 [70] jquerylib_0.1.4       RColorBrewer_1.1-3    ggridges_0.5.3       
 [73] Rcpp_1.0.9            plyr_1.8.6            zlibbioc_1.36.0      
 [76] purrr_0.3.4           ps_1.6.0              rpart_4.1-15         
 [79] deldir_1.0-6          GetoptLong_1.0.5      pbapply_1.5-0        
 [82] viridis_0.6.2         cowplot_1.1.1         zoo_1.8-9            
 [85] ggrepel_0.9.1         cluster_2.1.2         fs_1.5.2             
 [88] magrittr_2.0.3        scattermore_0.7       DO.db_2.9            
 [91] circlize_0.4.14       lmtest_0.9-40         RANN_2.6.1           
 [94] whisker_0.4           fitdistrplus_1.1-8    matrixStats_0.61.0   
 [97] patchwork_1.1.1       mime_0.12             evaluate_0.15        
[100] xtable_1.8-4          shape_1.4.6           gridExtra_2.3        
[103] compiler_4.0.4        tibble_3.1.6          KernSmooth_2.23-20   
[106] crayon_1.5.1          shadowtext_0.1.1      R.oo_1.24.0          
[109] htmltools_0.5.2       ggfun_0.0.5           mgcv_1.8-39          
[112] later_1.3.0           tidyr_1.2.0           DBI_1.1.2            
[115] tweenr_1.0.2          MASS_7.3-56           Matrix_1.4-1         
[118] cli_3.2.0             R.methodsS3_1.8.1     pkgconfig_2.0.3      
[121] getPass_0.2-2         rvcheck_0.2.1         plotly_4.10.0        
[124] spatstat.sparse_2.1-0 foreach_1.5.2         xml2_1.3.3           
[127] bslib_0.3.1           yulab.utils_0.0.4     stringr_1.4.0        
[130] callr_3.7.0           digest_0.6.29         sctransform_0.3.3    
[133] RcppAnnoy_0.0.19      spatstat.data_2.1-2   tm_0.7-8             
[136] rmarkdown_2.13        leiden_0.3.9          fastmatch_1.1-3      
[139] uwot_0.1.11           gtools_3.9.2          shiny_1.7.1          
[142] modeltools_0.2-23     rjson_0.2.21          lifecycle_1.0.1      
[145] nlme_3.1-155          jsonlite_1.8.0        viridisLite_0.4.0    
[148] fansi_1.0.3           pillar_1.7.0          lattice_0.20-45      
[151] fastmap_1.1.0         httr_1.4.2            survival_3.3-1       
[154] GO.db_3.12.1          glue_1.6.2            iterators_1.0.14     
[157] png_0.1-7             bit_4.0.4             ggforce_0.3.3        
[160] stringi_1.7.6         sass_0.4.1            blob_1.2.3           
[163] caTools_1.18.2        memoise_2.0.1         irlba_2.3.5          
[166] future.apply_1.8.1