Load sources

• Do not forget to use correct name for views…

source("init.R")
source("functions_map.R")

Read the metaPR2 data

df <- rio::import(df_he492_file_name, guess_max = 100000)
samples_with_taxa <- rio::import(samples_with_taxa_file_name)
samples_without_taxa <- rio::import(samples_without_taxa_file_name)
samples <- bind_rows(samples_with_taxa, samples_without_taxa)

Cultures

cultures <- rio::import(cultures_file_name)


  # filter(!is.na(asv_code)) %>%  
  # group_by(asv_code, sequence_matching) %>% 
  # # Count the number of cells corresponding to same asv and same species
  # summarize(n_cells = n()) %>% 
  # # Keep for each asv the species occuring most often (if ties, take line randomly)  
  # dplyr::slice_max(n_cells, n=1, with_ties=FALSE) %>% 
  # ungroup()



# fasta_cells <- inner_join(cells, fasta, by = c("asv_code" = "seq_name"))

df_map <- inner_join(cultures, df, by = c("asv_code"="asv_code"))

# Group by single cell adn get info about the first asv

df_map <- df_map %>% 
  mutate(label = str_c(species_Luka,he492_asv_matching,culture_matching, sep = " - ")) %>%  
  group_by(file_code, label) %>% 
  summarise(n_reads_label = sum(n_reads))  %>% 
  ungroup()

df_map <- inner_join(samples_with_taxa, df_map) %>% 
  mutate(n_reads_label_pct =  n_reads_label/n_reads_taxa*100) %>% 
  filter(!is.na(latitude))

samples <- samples %>% 
  filter(!is.na(latitude))


# Plot the maps -----------------------------------------------------------



map_metapr2 (df_map, samples)

species, ASV, culture:  Arcocellulus_cornucervis - ASV_24 - HE492-43 

species, ASV, culture:  Attheya_longicornis - ASV_570 - HE492-59 

species, ASV, culture:  Chaetoceros_contortus - ASV_1089 - HE492-06 

species, ASV, culture:  Chaetoceros_convolutus - ASV_109 - HE492-64 

species, ASV, culture:  Chaetoceros_debilis - ASV_162 - HE492-19 

species, ASV, culture:  Chaetoceros_diadema - ASV_786 - HE492-12 

species, ASV, culture:  Chaetoceros_neogracilis - ASV_121 - HE492-73 

species, ASV, culture:  Chaetoceros_sp. - ASV_1173 - HE492-36 

species, ASV, culture:  Chaetoceros_sp. - ASV_1516 - HE492-03 

species, ASV, culture:  Corethron_sp. - ASV_282 - HE492-01 

species, ASV, culture:  Cylindrotheca_closterium - ASV_766 - HE492-63 

species, ASV, culture:  Cymatosiraceae - ASV_46 - HE492-46 

species, ASV, culture:  Fragilariopsis sp. - ASV_186 - HE492-50 

species, ASV, culture:  Pseudo-nitzschia_granii - ASV_54 - HE492-40 

species, ASV, culture:  Pseudo-nitzschia_turgidula - ASV_179 - HE492-47 

species, ASV, culture:  Shionodiscus_bioculatus - ASV_110 - HE492-10 

species, ASV, culture:  Skeletonema_marinoi - ASV_40 - HE492-39 

species, ASV, culture:  Thalassiosira sp. - ASV_117 - HE492-45 

species, ASV, culture:  Thalassiosira_gravida - ASV_243 - HE492-07 

species, ASV, culture:  Thalassiosira_oceanica - ASV_187 - HE492-54 

Metabarcodes HE792

metab <- rio::import(metabarcodes_file_name)


  # filter(!is.na(asv_code)) %>%  
  # group_by(asv_code, sequence_matching) %>% 
  # # Count the number of cells corresponding to same asv and same species
  # summarize(n_cells = n()) %>% 
  # # Keep for each asv the species occuring most often (if ties, take line randomly)  
  # dplyr::slice_max(n_cells, n=1, with_ties=FALSE) %>% 
  # ungroup()



# fasta_cells <- inner_join(cells, fasta, by = c("asv_code" = "seq_name"))

df_map <- inner_join(metab, df, by = c("asv_code"="asv_code"))

# Group by single cell adn get info about the first asv

df_map <- df_map %>% 
  mutate(label = str_c(species_Luka,he492_asv_matching, sep = " - ")) %>%  
  group_by(file_code, label) %>% 
  summarise(n_reads_label = sum(n_reads))  %>% 
  ungroup()

df_map <- inner_join(samples_with_taxa, df_map) %>% 
  mutate(n_reads_label_pct =  n_reads_label/n_reads_taxa*100) %>% 
  filter(!is.na(latitude))

samples <- samples %>% 
  filter(!is.na(latitude))


# Plot the maps -----------------------------------------------------------



map_metapr2 (df_map, samples)

species, ASV, culture:  Actinocyclus_sp. - ASV_123 

species, ASV, culture:  Chaetoceros borealis - ASV_270 

species, ASV, culture:  Chaetoceros sp. - ASV_296 

species, ASV, culture:  Chaetoceros_brevis - ASV_245 

species, ASV, culture:  Chaetoceros_convolutus - ASV_160 

species, ASV, culture:  Chaetoceros_sp. - ASV_227 

species, ASV, culture:  Chaetoceros_wighamii - ASV_168 

species, ASV, culture:  Eucampia_groenlandica - ASV_49 

species, ASV, culture:  Fragilariopsis_cylindrus - ASV_361 

species, ASV, culture:  Fragilariopsis_sp. - ASV_127 

species, ASV, culture:  Leptocylindrus_minimus - ASV_159 

species, ASV, culture:  Leptocylindrus_minimus - ASV_28 

species, ASV, culture:  Leptocylindrus_minimus - ASV_395 

species, ASV, culture:  Leptocylindrus_minimus - ASV_45 

species, ASV, culture:  Leptocylindrus_minimus - ASV_8 

species, ASV, culture:  Leptocylindrus_sp. - ASV_183 

species, ASV, culture:  Proboscia_alata - ASV_365 

species, ASV, culture:  Proboscia_sp. - ASV_362 

species, ASV, culture:  Raphid-pennate_X_sp. - ASV_202 

species, ASV, culture:  Rhizosolenia sp. - ASV_260 

species, ASV, culture:  Skeletonema_sp. - ASV_58 

species, ASV, culture:  Skeletonema_sp. - ASV_99 

species, ASV, culture:  Thalassiosira_concaviuscula - ASV_79 

species, ASV, culture:  Thalassiosira_hispida - ASV_70 

species, ASV, culture:  Thalassiosira_sp. - ASV_266