added antigen sequences, gene name, catalog ids + updated docs

DOCS.md

@@ -31,6 +31,9 @@ The unique identifiers for each row in this dataset is `["candidate_library_id",
 The unique identifiers for each row in this dataset is `["candidate_library_id", "candidate_id", "antigen_pdb_id"]`. So, the number of rows are equal to the number of candidate protein + antigen combinations in each distinct library. The columns specific to the binding dataset are:
 
 - `antigen_pdb_id`: The PDB ID of the antigen protein used in the binding assay.
+- `antigen_gene_name`: The gene name associated with the PDB ID of the antigen.
+- `sino_catalog_id`: The ID of the antigen ordered from Sino Biological.
+- `antigen_sequence`: The expressed sequence of the antigen used in the assay.
 - `targeting`: Boolean value that specifies whether the antigen used in the binding assay matches the target for the candidate protein.
 - `binding_score_50nM`: The mean binding score from the assay where the candidate library was tested with 50 nM of the antigen protein.
 - `binding_score_500nM`: The mean binding score from the assay where the candidate library was tested with 500 nM of the antigen protein.
@@ -41,6 +44,9 @@ The unique identifiers for each row in this dataset is `["candidate_library_id",
 
 The unique identifiers for each row in this dataset is `["candidate_library_id", "candidate_id"]`. So, the number of rows are equal to the number of candidate proteins in each distinct pool. This columns specific to the specificity dataset are:
 
+- `target_gene_name`: The gene name associated with the PDB ID of the target antigen.
+- `sino_catalog_id`: The ID of the target antigen ordered from Sino Biological.
+- `target_sequence`: The expressed sequence of the target antigen used in the assay.
 - `log_specificity_score_50nM`: The mean specificity score from the assay where the candidate library was tested with 50 nM of the antigen protein.
 - `log_specificity_score_500nM`: The mean specificity score from the assay where the candidate library was tested with 500 nM of the antigen protein.
 - `log_specificity_score_50nM_std`: The standard error of the mean 50 nM specificity score estimate calculated from each of the replicates measured.
@@ -50,9 +56,13 @@ The unique identifiers for each row in this dataset is `["candidate_library_id",
 
 We include datasets that contain all of the replicates that were used to generate the binding and specificity mean scores/standard errors. As a result, the standard error columns are removed, and, for simplicity, we provide a row for each dose (column called `antigen_concentration_nM`). To further improve our data provenance, we include unique molecular identifier (UMI) counts for the expression and binding samples, along with sample ids for the expression and binding samples. The added columns are called:
 
-- `expression_sample_id`: The sample id of the expression sample.
-- `binding_sample_id`: The sample id of the binding sample.
+- `expression_sample_id`: The sample id of the expression sample. Different ID's that map to the same `candidate_library_id` are repeated measurements of the same expressed pool (which is the candidate_library_id). This means any variation observed here is likely measurement variation and not due to biological variation.
+- `binding_sample_id`: The sample id of the binding sample. Different ID's of this that map to the same `candidate_library_id` are separate biological replicates of the same pool as they represent distinct binding attempts. This means that any variation observed along this axis is due to a combination of measurement and biological variation.
 - `expression_umi_count`: The number of unique molecular identifiers (UMIs) observed for the binder in the expression sample.
 - `binding_umi_count`: The number of unique molecular identifiers (UMIs) observed for the binder in the binding sample.
 
-These columns are included in the binding replicates dataset, while the other two (expression-replicates and specificity-replicates) do not, for now. They can be made available upon request!
+### Notes
+
+When estimating our binding and specificity scores, we use a weak Bayesian prior and the UMI counts update the posterior. This means that zero counts are not truly zero, just that the probability of seeing that candidate is likely lower than one over the number of observations. 
+
+These extra columns are included in the binding replicates dataset, while the other two (expression-replicates and specificity-replicates) do not, for now. They can be made available upon request!

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