minor bugfixes

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: IgGeneUsage
 Type: Package
 Title: Differential gene usage in immune repertoires
-Version: 1.17.14
+Version: 1.17.15
 Authors@R: 
     person(given = "Simo",  
            family = "Kitanovski",
@@ -33,8 +33,7 @@ Suggests:
     ggplot2,
     ggforce,
     gridExtra,
-    ggrepel,
-    MASS
+    ggrepel
 LinkingTo: 
     BH (>= 1.66.0),
     Rcpp (>= 0.12.0),

inst/stan/dgu.stan

@@ -33,16 +33,16 @@ data {
   int<lower=0> N_gene;                     // gene
   int<lower=0> N_individual;               // number of individuals
   int<lower=0> N_condition;                // number of conditions
-  array[N_individual] int N;                   // number of tries (repertoire size)
-  array[N_gene, N_individual] int Y;           // number of heads for each coin
-  array[N_individual] int condition_id;    // id of conditions
-  array[N_individual] int individual_id;       // id of replicate
+  array [N_individual] int N;                   // number of tries (repertoire size)
+  array [N_gene, N_individual] int Y;           // number of heads for each coin
+  array [N_individual] int condition_id;    // id of conditions
+  array [N_individual] int individual_id;       // id of replicate
 }
 
 transformed data {
   // convert int N -> real N fo convenient division
   // in generated quantities block
-  real Nr [N_individual];
+  array [N_individual] real Nr;
   Nr = N;
 }
 
@@ -55,14 +55,14 @@ parameters {
   vector <lower=0> [N_condition] sigma_condition;
   vector <lower=0> [N_condition] sigma_individual;
 
-  array[N_individual] vector [N_gene] z_beta_individual;
-  array[N_condition] vector [N_gene] z_beta_condition;
+  array [N_individual] vector [N_gene] z_beta_individual;
+  array [N_condition] vector [N_gene] z_beta_condition;
 }
 
 transformed parameters {
-  array[N_individual] vector <lower=0, upper=1> [N_gene] theta;
-  array[N_individual] vector [N_gene] beta_individual;
-  array[N_condition] vector [N_gene] beta_condition;
+  array [N_individual] vector <lower=0, upper=1> [N_gene] theta;
+  array [N_individual] vector [N_gene] beta_individual;
+  array [N_condition] vector [N_gene] beta_condition;
 
   for(i in 1:N_condition) {
     beta_condition[i] = 0 + sigma_condition[i] * z_beta_condition[i];
@@ -98,16 +98,16 @@ model {
 
 generated quantities {
   // PPC: count usage (repertoire-level)
-  int Yhat_rep [N_gene, N_individual];
+  array [N_gene, N_individual] int Yhat_rep;
 
   // PPC: proportion usage (repertoire-level)
-  real Yhat_rep_prop [N_gene, N_individual];
+  array [N_gene, N_individual] real Yhat_rep_prop;
 
   // PPC: proportion usage at a gene level in condition
-  vector [N_gene] Yhat_condition_prop [N_condition];
+  array [N_condition] vector [N_gene] Yhat_condition_prop;
 
   // LOG-LIK
-  vector [N_gene] log_lik [N_individual];
+  array [N_individual] vector [N_gene] log_lik;
 
   // DGU matrix
   matrix [N_gene, N_condition*(N_condition-1)/2] dgu;

inst/stan/dgu_rep.stan

@@ -32,16 +32,16 @@ data {
   int<lower=0> N_gene;                     // gene
   int<lower=0> N_individual;               // number of individuals
   int<lower=0> N_condition;                // number of conditions
-  array[N_sample] int N;                   // number of tries (repertoire size)
-  array[N_gene, N_sample] int Y;           // number of heads for each coin
-  array[N_individual] int condition_id;    // id of conditions
-  array[N_sample] int individual_id;       // id of replicate
+  array [N_sample] int N;                   // number of tries (repertoire size)
+  array [N_gene, N_sample] int Y;           // number of heads for each coin
+  array [N_individual] int condition_id;    // id of conditions
+  array [N_sample] int individual_id;       // id of replicate
 }
 
 transformed data {
   // convert int N -> real N fo convenient division
   // in generated quantities block
-  real Nr [N_sample];
+  array [N_sample] real Nr;
   Nr = N;
 }
 
@@ -55,17 +55,17 @@ parameters {
   vector <lower=0> [N_condition] sigma_individual;
   real <lower=0> sigma_replicate;
 
-  array[N_sample] vector [N_gene] z_beta_sample;
-  array[N_individual] vector [N_gene] z_beta_individual;
-  array[N_condition] vector [N_gene] z_beta_condition;
+  array [N_sample] vector [N_gene] z_beta_sample;
+  array [N_individual] vector [N_gene] z_beta_individual;
+  array [N_condition] vector [N_gene] z_beta_condition;
 }
 
 
 transformed parameters {
-  array[N_sample] vector <lower=0, upper=1> [N_gene] theta;
-  array[N_sample] vector [N_gene] beta_sample;
-  array[N_condition] vector [N_gene] beta_condition;
-  array[N_individual] vector [N_gene] beta_individual;
+  array [N_sample] vector <lower=0, upper=1> [N_gene] theta;
+  array [N_sample] vector [N_gene] beta_sample;
+  array [N_condition] vector [N_gene] beta_condition;
+  array [N_individual] vector [N_gene] beta_individual;
 
   for(i in 1:N_condition) {
     beta_condition[i] = 0 + sigma_condition[i] * z_beta_condition[i];
@@ -109,16 +109,16 @@ model {
 
 generated quantities {
   // PPC: count usage (repertoire-level)
-  int Yhat_rep [N_gene, N_sample];
+  array [N_gene, N_sample] int Yhat_rep;
 
   // PPC: proportion usage (repertoire-level)
-  real Yhat_rep_prop [N_gene, N_sample];
+  array [N_gene, N_sample] real Yhat_rep_prop;
 
   // PPC: proportion usage at a gene level in condition
-  vector [N_gene] Yhat_condition_prop [N_condition];
+  array [N_condition] vector [N_gene] Yhat_condition_prop;
 
   // LOG-LIK
-  vector [N_gene] log_lik [N_sample];
+  array [N_sample] vector [N_gene] log_lik;
 
   // DGU matrix
   matrix [N_gene, N_condition*(N_condition-1)/2] dgu;

inst/stan/gu.stan

@@ -33,31 +33,31 @@ data {
   int<lower=0> N_gene;                     // gene
   int<lower=0> N_individual;               // number of individuals
   int<lower=0> N_condition;                // number of conditions
-  array[N_individual] int N;               // number of tries (repertoire size)
-  array[N_gene, N_individual] int Y;       // number of heads for each coin
-  array[N_individual] int condition_id;    // id of conditions
-  array[N_individual] int individual_id;   // id of replicate
+  array [N_individual] int N;               // number of tries (repertoire size)
+  array [N_gene, N_individual] int Y;       // number of heads for each coin
+  array [N_individual] int condition_id;    // id of conditions
+  array [N_individual] int individual_id;   // id of replicate
 }
 
 transformed data {
   // convert int N -> real N fo convenient division
   // in generated quantities block
-  real Nr [N_individual];
+  array [N_individual] real Nr;
   Nr = N;
 }
 
 parameters {
   real <lower=0> phi;
   real <lower=0, upper=1> kappa;
   real <lower=0> sigma_individual;
-  array[N_individual] vector [N_gene] z_beta_individual;
+  array [N_individual] vector [N_gene] z_beta_individual;
   vector [N_gene] beta_condition;
 }
 
 transformed parameters {
-  array[N_individual] vector <lower=0, upper=1> [N_gene] theta;
-  array[N_individual] vector [N_gene] beta_sample;
-  array[N_individual] vector [N_gene] beta_individual;
+  array [N_individual] vector <lower=0, upper=1> [N_gene] theta;
+  array [N_individual] vector [N_gene] beta_sample;
+  array [N_individual] vector [N_gene] beta_individual;
 
   for(i in 1:N_individual) {
     beta_individual[i] = beta_condition + sigma_individual * z_beta_individual[i];
@@ -85,16 +85,16 @@ model {
 
 generated quantities {
   // PPC: count usage (repertoire-level)
-  int Yhat_rep [N_gene, N_individual];
+  array [N_gene, N_individual] int Yhat_rep;
 
   // PPC: proportion usage (repertoire-level)
-  real Yhat_rep_prop [N_gene, N_individual];
+  array [N_gene, N_individual] real Yhat_rep_prop;
 
   // PPC: proportion usage at a gene level in condition
   vector [N_gene] Yhat_condition_prop;
 
   // LOG-LIK
-  vector [N_gene] log_lik [N_individual];
+  array [N_individual] vector [N_gene] log_lik;
 
   //TODO: speedup, run in C++ not big factor on performance
   for(j in 1:N_gene) {

inst/stan/gu_rep.stan

@@ -32,16 +32,16 @@ data {
   int<lower=0> N_gene;                     // gene
   int<lower=0> N_individual;               // number of individuals
   int<lower=0> N_condition;                // number of conditions
-  array[N_sample] int N;                   // number of tries (repertoire size)
-  array[N_gene, N_sample] int Y;           // number of heads for each coin
-  array[N_individual] int condition_id;    // id of conditions
-  array[N_sample] int individual_id;       // id of replicate
+  array [N_sample] int N;                   // number of tries (repertoire size)
+  array [N_gene, N_sample] int Y;           // number of heads for each coin
+  array [N_individual] int condition_id;    // id of conditions
+  array [N_sample] int individual_id;       // id of replicate
 }
 
 transformed data {
   // convert int N -> real N fo convenient division
   // in generated quantities block
-  real Nr [N_sample];
+  array [N_sample] real Nr;
   Nr = N;
 }
 
@@ -52,17 +52,17 @@ parameters {
   real <lower=0> sigma_individual;
   real <lower=0> sigma_replicate;
 
-  array[N_sample] vector [N_gene] z_beta_sample;
-  array[N_individual] vector [N_gene] z_beta_individual;
+  array [N_sample] vector [N_gene] z_beta_sample;
+  array [N_individual] vector [N_gene] z_beta_individual;
 
   vector [N_gene] beta_condition;
 }
 
 
 transformed parameters {
-  array[N_sample] vector <lower=0, upper=1> [N_gene] theta;
-  array[N_sample] vector [N_gene] beta_sample;
-  array[N_individual] vector [N_gene] beta_individual;
+  array [N_sample] vector <lower=0, upper=1> [N_gene] theta;
+  array [N_sample] vector [N_gene] beta_sample;
+  array [N_individual] vector [N_gene] beta_individual;
 
   for(i in 1:N_individual) {
     beta_individual[i] = beta_condition + sigma_individual * z_beta_individual[i];
@@ -98,16 +98,16 @@ model {
 
 generated quantities {
   // PPC: count usage (repertoire-level)
-  int Yhat_rep [N_gene, N_sample];
+  array [N_gene, N_sample] int Yhat_rep;
 
   // PPC: proportion usage (repertoire-level)
-  real Yhat_rep_prop [N_gene, N_sample];
+  array [N_gene, N_sample] real Yhat_rep_prop;
 
   // PPC: proportion usage at a gene level in condition
   vector [N_gene] Yhat_condition_prop;
 
   // LOG-LIK
-  vector [N_gene] log_lik [N_sample];
+  array [N_sample] vector [N_gene] log_lik;
 
   //TODO: speedup, run in C++ not big factor on performance
   for(j in 1:N_gene) {

man/DGU.Rd

@@ -22,15 +22,15 @@ DGU(ud,
 }
 
 \arguments{
-\item{ud}{Data.frame with 4 columns: 
+\item{ud}{Data.frame with 4 or 5 columns: 
 \itemize{
 \item 'individual_id' = character, name of the donor (e.g. Pt1)
 \item 'condition' = character, name of biological conditions (e.g. tumor)
 \item 'gene_name' = character, Ig gene name (e.g. IGHV1-69)
 \item 'gene_usage_count' = number, frequency (=usage) of rearrangements from 
        individual_id x condition x gene_name
-\item [optional] 'repertoire' = character or number, if more than one 
-      repertoire (biological replicates) is available per individual 
+\item [optional] 'replicate' = character or number. Replicate id, if more than 
+      one repertoire (biological replicates) is available per individual 
 }
 ud can also be be a SummarizedExperiment object. See examplary data 
 'data(Ig_SE)' for more information.

man/LOO.Rd

@@ -50,9 +50,8 @@ LOO(ud,
 \item 'gene_name' = character, Ig gene name (e.g. IGHV1-69)
 \item 'gene_usage_count' = number, frequency (=usage) of rearrangements from 
        individual_id x condition x gene_name
-
-\item [optional] 'repertoire' = character or number, if more than one 
-      repertoire (biological replicates) is available per individual 
+\item [optional] 'replicate' = character or number. Replicate id, if more than 
+      one repertoire (biological replicates) is available per individual 
 }
 ud can also be be a SummarizedExperiment object. See dataset
 'data(Ig_SE)' for more information.