too-many-cells 0.1.12.0 → 0.1.12.1

raw patch · 2 files changed

+3/−1 lines, 2 files

Files

• app/Main.hs +2/−0
• too-many-cells.cabal +1/−1

@@ -135,6 +135,7 @@                   , featureColumn :: Maybe Int <?> "([1] | COLUMN) The column (1-indexed) in the features.tsv.gz file to use for feature names. If using matrix market format, cellranger stores multiple columns in the features file, usually the first column for the Ensembl identifier and the second column for the gene symbol. If the Ensembl identifier is not quickly accessible, use --feature-column 2 for the second column, which is usually more ubiquitous. Useful for overlaying gene expression so you can say --draw-leaf \"DrawItem (DrawContinuous \\\"CD4\\\")\") instead of --draw-leaf \"DrawItem (DrawContinuous \\\"ENSG00000010610\\\")\"). Does not affect CSV format (the column names will be the feature names)."                   , normalization :: Maybe String <?> "([TfIdfNorm] | UQNorm | MedNorm | TotalMedNorm | BothNorm | NoneNorm) Type of normalization before clustering. TfIdfNorm normalizes based on the prevalence of each feature. UQNorm normalizes each observation by the upper quartile non-zero counts of that observation. MedNorm normalizes each observation by the median non-zero counts of that observation. TotalMedNorm normalized first each observation by total count then by median of non-zero counts across features. BothNorm uses both normalizations (first TotalMedNorm for all analysis then additionally TfIdfNorm during clustering). NoneNorm does not normalize. Default is TfIdfNorm for clustering and NoneNorm for differential (which instead uses the recommended edgeR single cell preprocessing including normalization and filtering, any normalization provided here will result in edgeR preprocessing on top). Cannot use TfIdfNorm for any other process as NoneNorm will become the default."                   , pca :: Maybe Int <?> "([Nothing] | INT) Not recommended, as it makes cosine similarity less meaningful (therefore less accurate -- instead, consider making your own similarity matrix and using cluster-tree, our sister algorithm, to cluster the matrix and plot with birch-beer). The number of dimensions to keep for PCA dimensionality reduction before clustering. Default is no PCA at all in order to keep all information. Should use with --shift-positive to ensure no negative values."+                  , shiftPositive :: Bool <?> "Shift features to positive values. Positive values are shifted to allow modularity to work correctly."                   , noFilter :: Bool <?> "Whether to bypass filtering genes and cells by low counts."                   , filterThresholds :: Maybe String <?> "([(250, 1)] | (DOUBLE, DOUBLE)) The minimum filter thresholds for (MINCELL, MINFEATURE) when filtering cells and features by low read counts. See also --no-filter."                   , prior :: Maybe String <?> "([Nothing] | STRING) The input folder containing the output from a previous run. If specified, skips clustering by using the previous clustering files."}@@ -144,6 +145,7 @@                    , featureColumn :: Maybe Int <?> "([1] | COLUMN) The column (1-indexed) in the features.tsv.gz file to use for feature names. If using matrix market format, cellranger stores multiple columns in the features file, usually the first column for the Ensembl identifier and the second column for the gene symbol. If the Ensembl identifier is not quickly accessible, use --feature-column 2 for the second column, which is usually more ubiquitous. Useful for overlaying gene expression so you can say --draw-leaf \"DrawItem (DrawContinuous \\\"CD4\\\")\") instead of --draw-leaf \"DrawItem (DrawContinuous \\\"ENSG00000010610\\\")\"). Does not affect CSV format (the column names will be the feature names)."                    , pca :: Maybe Int <?> "([Nothing] | INT) Not recommended, as it makes cosine similarity less meaningful (therefore less accurate -- instead, consider making your own similarity matrix and using cluster-tree, our sister algorithm, to cluster the matrix and plot with birch-beer). The number of dimensions to keep for PCA dimensionality reduction before clustering. Default is no PCA at all in order to keep all information. Should use with --shift-positive to ensure no negative values."                    , noFilter :: Bool <?> "Whether to bypass filtering genes and cells by low counts."+                   , shiftPositive :: Bool <?> "Shift features to positive values. Positive values are shifted to allow modularity to work correctly."                    , filterThresholds :: Maybe String <?> "([(250, 1)] | (DOUBLE, DOUBLE)) The minimum filter thresholds for (MINCELL, MINFEATURE) when filtering cells and features by low read counts. See also --no-filter."                    , delimiter :: Maybe Char <?> "([,] | CHAR) The delimiter for the csv file if using a normal csv rather than cellranger output and for --labels-file."                    , normalization :: Maybe String <?> "([TfIdfNorm] | UQNorm | MedNorm | TotalMedNorm | BothNorm | NoneNorm) Type of normalization before clustering. TfIdfNorm normalizes based on the prevalence of each feature. UQNorm normalizes each observation by the upper quartile non-zero counts of that observation. MedNorm normalizes each observation by the median non-zero counts of that observation. TotalMedNorm normalized first each observation by total count then by median of non-zero counts across features. BothNorm uses both normalizations (first TotalMedNorm for all analysis then additionally TfIdfNorm during clustering). NoneNorm does not normalize. Default is TfIdfNorm for clustering and NoneNorm for differential (which instead uses the recommended edgeR single cell preprocessing including normalization and filtering, any normalization provided here will result in edgeR preprocessing on top). Cannot use TfIdfNorm for any other process as NoneNorm will become the default."

@@ -1,6 +1,6 @@ cabal-version: >=1.10 name: too-many-cells-version: 0.1.12.0+version: 0.1.12.1 license: GPL-3 license-file: LICENSE copyright: 2019 Gregory W. Schwartz