fix html links; note required cols; keep 3 more cols in custom datasets

README.md

@@ -18,7 +18,10 @@ Upon starting AIRRscape, the main panel shows basic instructions and options of
 To view custom datasets (.tab or .tsv files in AIRR-C format), first use the _Import Data_ tab to upload datasets, convert+combine them, and then download the processed files. Next, upload these files and select "Custom datasets - IgH" or "Custom datasets - IgH combined" from the dropdown menu.
 
 ### _Import Data_ tab
-To import datasets, first upload each dataset separately (maximum 6). As long as they follow AIRR-C standards (.tab or .tsv), they will be automatically converted for viewing in AIRRscape. Metadata is not required; simply enter the name of each dataset, which will be used as labels in each faceted dataset. Then, click the combine button to begin processing the datasets. Note the processing occurs in the background, so next just click the two download buttons separately to get the processed files - depending on dataset size, it might take 10-20 seconds before the download begins. Upload these two files in the main _AIRRscape_ tab for viewing.
+To import datasets, first upload each dataset separately (maximum 6). As long as they follow AIRR-C standards (.tab or .tsv) and include some key columns, they will be automatically converted for viewing in AIRRscape. Metadata is not required; simply enter the name of each dataset, which will be used as labels in each faceted dataset. Then, click the combine button to begin processing the datasets. Note the processing occurs in the background, so next just click the two download buttons separately to get the processed files - depending on dataset size, it might take 10-20 seconds before the download begins. Upload these two files in the main _AIRRscape_ tab for viewing.
+
+Required dataset columns:
+ _v_call, j_call, v_identity, junction_aa_
 
 ### _AIRRscape_ tab
 The heatmaps split antibody repertoires into bins based on their germline V-gene family + J-gene assignments (x-axis) and their CDR3 lengths (y-axis). Select the fill color for the bins using the second dropdown menu in the sidebar: color by 1) average somatic hypermutation (SHM), 2) maximum SHM, or 3) percent of total (i.e. what percent of antibodies in that panel are found in that bin).

shinyapp/app.R

@@ -85,7 +85,7 @@ AIRRscapeprocess <- function(x, filter_columns = TRUE, filter_to_HC = TRUE, renu
     mutate(across(shm_max, round, 2)) %>% 
     mutate(across(shm_mean, round, 2))
   ## this will filter the dataset if filter_columns option is set to true - note the any_of which allows columns to be missing
-  vars2 <- c("sequence_id", "binding", "neutralization", "cregion", "cdr3_aa_imgt","vgene", "vgf_jgene", "vgf","jgene", "cdr3length_imgt", "shm", "shm_max", "shm_mean", "ncount", "reads_per_clone")
+  vars2 <- c("sequence_id", "binding", "neutralization", "cregion", "cdr3_aa_imgt","vgene", "vgf_jgene", "vgf","jgene", "cdr3length_imgt", "consensus_count", "duplicate_count", "clone_id", "shm", "shm_max", "shm_mean", "ncount", "reads_per_clone")
   if (filter_columns) {
     x <- x %>% select(any_of(vars2))
   }
@@ -230,8 +230,10 @@ ui <- fluidPage(
       p("- In the topology drop-down menu, the first 2 options will construct a NJ or parsimony topology from the selected set of antibodies in the table. The final 4 options will find the nearest sequences (up to 500) of a single selected antibody, with four possible distance thresholds. Use the height & width sliders to change the window size of the topology."),
       p("- Finally make sure to check that all antibodies in the table have the same CDR3 length or the topology calculation will fail."),
       h4("GitHub repo & citation:"),
-      p("To run AIRRscape locally and for more detailed usage instructions, see the ", a("README", href="https://github.com/czbiohub/AIRRscape", target="_blank"), " on GitHub."), #, target="_blank"
-      p("The AIRRscape ", a("publication", href="https://doi.org/10.1371/journal.pcbi.1010052", target="_blank"), " is available at PLOS Computational Biology."), # , target="_blank"
+      tags$div("To run AIRRscape locally and for more detailed usage instructions, see the ", tags$a(href="https://github.com/czbiohub/AIRRscape", "README", target="_blank"), " on GitHub."),
+      # p("To run AIRRscape locally and for more detailed usage instructions, see the ", a("README", href="https://github.com/czbiohub/AIRRscape", target="_blank"), " on GitHub."), #, target="_blank"
+      # p("The AIRRscape ", a("publication", href="https://doi.org/10.1371/journal.pcbi.1010052", target="_blank"), " is available at PLOS Computational Biology."), # , target="_blank"
+      tags$div("The AIRRscape ", tags$a(href="https://doi.org/10.1371/journal.pcbi.1010052", "publication", target="_blank"), " is available at PLOS Computational Biology."),
       h6("Questions? Please email: eric.waltari at czbiohub.org"),
       width = 4
     ),