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pair programming #67

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pair programming #67

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pair programming
cansavvy Nov 20, 2024
3c5f7a4
Merge branch 'main' into cansavvy/pair-on-gi-scores
cansavvy Dec 4, 2024
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78 changes: 43 additions & 35 deletions inst/rmd/scratch_gimap_GI_review.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -15,11 +15,12 @@ devtools::load_all()
## Get the gimap GI results

```{r}
gimap_dataset <- get_example_data("gimap") %>%
gimap_filter() %>%
gimap_annotate(cell_line = "HELA") %>%
gimap_normalize(
timepoints = "day") %>%
gimap_dataset <- get_example_data("gimap") %>%
gimap_filter() %>%
gimap_annotate(cell_line = "HELA") %>%
gimap_normalize(
timepoints = "day"
) %>%
calc_crispr() %>%
calc_gi()
```
Expand Down Expand Up @@ -61,7 +62,6 @@ Columns in the GI Mapping results not in the gimap results seem to include model
### Target overlap

```{r}
length(unique(gimap_dataset$normalized_log_fc$pgRNA_target))
length(unique(old_gi_results$pgRNA_target))
Expand Down Expand Up @@ -90,45 +90,53 @@ While the replicate values themselves can't be completely synced like we've done

```{r}
head(old_gi_results)
```

```{r}
joindf <- dplyr::full_join(old_gi_results, gimap_dataset$gi_scores,
by = c("pgRNA_target" = "pgRNA_target_double", "rep"),
suffix = c("_old", "_new"))
by = c("pgRNA_target" = "pgRNA_target_double", "rep"),
suffix = c("_old", "_new")
)
```

## Split out the comparisons we want to make

Have to drop NA's because the gimap results include Day05_RepA_early datapoints while the GI Mapping doesn't. Also have to drop NAs because there are GI Mapping targets that aren't represented in the gimap targets?
Have to drop NA's because the gimap results include `Day05_RepA_early` datapoints while the GI Mapping doesn't. Also have to drop NAs because there are GI Mapping targets that aren't represented in the gimap targets?

```{r}
joined_df <- rbind(
#join the gene_ctrl
full_join(
old_gi_results %>% filter(target_type == "gene_ctrl") %>% select(c("pgRNA_target", "paralog_pair", "mean_GI_score", "rep")),
gimap_dataset$gi_scores %>% separate(pgRNA_target_double, c("gene1", "gene2"), sep="_", remove = FALSE) %>% mutate(ctrl = "ctrl") %>% unite("pgRNA_target_summary", c("gene1", "ctrl"), sep="_") %>% select(c("pgRNA_target_summary", "pgRNA_target_double", "single_target_gi_score_1", "rep")),
by = c("pgRNA_target" = "pgRNA_target_summary", "paralog_pair"="pgRNA_target_double", "rep"),
suffix = c("_old", "_new")
) %>% mutate(target_type = "gene_ctrl") %>% distinct() %>% select(c("pgRNA_target", "mean_GI_score", "rep", "single_target_gi_score_1", "target_type")) %>% `colnames<-`(c("pgRNA_target", "GI_Mapping_GI_score", "rep", "gimap_GI_score", "target_type")) %>% drop_na(),
# join the ctrl_gene
full_join(
old_gi_results %>% filter(target_type == "ctrl_gene") %>% select(c("pgRNA_target", "paralog_pair", "mean_GI_score", "rep")),
gimap_dataset$gi_scores %>% separate(pgRNA_target_double, c("gene1", "gene2"), sep="_", remove=FALSE) %>% mutate(ctrl = "ctrl") %>% unite("pgRNA_target_summary", c("ctrl", "gene2"), sep="_") %>% select(c("pgRNA_target_summary", "pgRNA_target_double", "single_target_gi_score_2", "rep")),
by = c("pgRNA_target" = "pgRNA_target_summary", "rep"),
suffix = c("_old", "_new")
) %>% mutate(target_type = "ctrl_gene") %>% distinct() %>% select(c("pgRNA_target", "mean_GI_score", "rep", "single_target_gi_score_2", "target_type")) %>% `colnames<-`(c("pgRNA_target", "GI_Mapping_GI_score", "rep", "gimap_GI_score", "target_type")) %>% drop_na(),
#join the gene_gene
full_join(
old_gi_results %>% filter(target_type == "gene_gene") %>% select(c("pgRNA_target", "mean_GI_score", "rep")),
gimap_dataset$gi_scores %>% select(c("pgRNA_target_double", "double_target_gi_score", "rep")),
by = c("pgRNA_target" = "pgRNA_target_double", "rep"),
suffix = c("_old", "_new")
) %>% mutate(target_type = "gene_gene") %>% `colnames<-`(c("pgRNA_target", "GI_Mapping_GI_score", "rep", "gimap_GI_score", "target_type")) %>% drop_na()
)
old_gi_results_wide <- old_gi_results %>%
dplyr::select(paralog_pair, target_type, mean_GI_score, rep) %>%
tidyr::pivot_wider(names_from = target_type,
values_from = mean_GI_score)
# Reshape the data so we can plot
joined_df <- gimap_dataset$gi_scores %>%
dplyr::full_join(old_gi_results_wide,
by = c("pgRNA_target_double"= "paralog_pair",
"rep" = "rep")) %>%
dplyr::filter(!grepl("Day05", rep))
```

```{r}
joined_df %>% ggplot(aes(x=gimap_GI_score, y=GI_Mapping_GI_score, color=target_type)) + geom_point() + facet_wrap(rep~target_type) + theme(legend.position = "none")
```
joined_df %>%
ggplot(aes(x = single_target_gi_score_1, y = gene_ctrl)) +
geom_point() +
facet_wrap(~rep) +
theme(legend.position = "none")
```

```{r}
joined_df %>%
ggplot(aes(x = single_target_gi_score_2, y = ctrl_gene)) +
geom_point() +
facet_wrap(~rep) +
theme(legend.position = "none")
```

```{r}
joined_df %>%
ggplot(aes(x = double_target_gi_score, y = gene_gene)) +
geom_point() +
facet_wrap(~rep) +
theme(legend.position = "none")
```
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