See https://github.com/GregorySchwartz/integreat for the latest version.
inteGREAT is an algorithm for integrating multiple data sources. inteGREAT
can be easily extended to differential integration by including samples from
multiple phenotypes, such as disease and control.
stack install integreatintegreat --dataInput in.csv --alignmentMethod CosineSimilarity --edgeMethod SpearmanCorrelationintegrate --dataInput in.csv --premade --alignmentMethod CosineSimilarityintegreat, Gregory W. Schwartz. Integrate data from multiple sources to find
consistent (or inconsistent) entities.
Usage: integreat [--dataInput STRING] [--vertexInput STRING] [--entityDiff TEXT]
[--alignmentMethod STRING] [--edgeMethod STRING]
[--walkerRestart DOUBLE] [--steps INT] [--premade] [--test]
[--entityFilter INT] [--entityFilterStdDev DOUBLE]
[--permutations INT]
Available options:
-h,--help Show this help text
--dataInput STRING ([STDIN] | FILE) The input file containing the data
intensities. Follows the format:
dataLevel,dataReplicate,vertex,intensity. dataLevel
is the level (the base level for the experiment, like
"proteomic_cancer" or "RNA_cancer" for instance,
requires at least two levels), dataReplicate is the
replicate in that experiment that the entity is from
(the name of that data set with the replicate name,
like "RNA_cancer_1"), and vertex is the name of the
entity (must match those in the vertex-input), and
the intensity is the value of this entity in this
data set.
--vertexInput STRING ([Nothing] | FILE) The input file containing
similarities between entities. Follows the format:
vertexLevel1,vertexLevel2,
vertex1,vertex2,similarity. vertexLevel1 is the level
(the base title for the experiment, "data set") that
vertex1 is from, vertexLevel2 is the level that
vertex2 is from, and the similarity is a number
representing the similarity between those two
entities. If not specified, then the same entity
(determined by vertex in data-input) will have a
similarity of 1, different entities will have a
similarity of 0.
--entityDiff TEXT ([Nothing] | STRING) When comparing entities that are
the same, ignore the text after this separator. Used
for comparing phosphorylated positions with another
level. For example, if we have a strings ARG29 and
ARG29_7 that we want to compare, we want to say that
their value is the highest in correlation, so this
string would be "_"
--alignmentMethod STRING ([CosineSimilarity] | RandomWalker | RandomWalkerSim)
The method to get integrated vertex similarity
between levels. CosineSimilarity uses the cosine
similarity of each vertex in each network compared to
the other vertices in other networks. RandomWalker
uses a random walker with restart based network
algnment algorithm in order to get similarity.
RandomWalkerSim uses a random walker with restart and
actually simulates the walker to get a stochastic
result.
--edgeMethod STRING ([SpearmanCorrelation] | PearsonCorrelation ) The
method to use for the edges between entities in the
coexpression matrix.
--walkerRestart DOUBLE ([0.25] | PROBABILITY) For the random walker
algorithm, the probability of making a jump to a
random vertex. Recommended to be the ratio of the
total number of vertices in the top 99% smallest
subnetworks to the total number of nodes in the
reduced product graph (Jeong, 2015).
--steps INT ([100] | STEPS) For the random walker algorithm, the
number of steps to take before stopping.
--premade ([False] | BOOL) Whether the input data (dataInput)
is a pre-made network of the format "[(["VERTEX"],
[("SOURCE", "DESTINATION", WEIGHT)])]", where VERTEX,
SOURCE, and DESTINATION are of type INT starting at
0, in order, and WEIGHT is a DOUBLE representing the
weight of the edge between SOURCE and DESTINATION.
--test ([False] | BOOL) Whether the input data from premade
is from a test run. If supplied, the output is
changed to an accuracy measure. In this case, we get
the total rank below the number of permuted vertices
divided by the theoretical maximum (so if there were
five changed vertices out off 10 and two were rank 8
and 10 while the others were in the top five, we
would have (1 - ((3 + 5) / (10 + 9 + 8 + 7 + 6))) as
the accuracy.
--entityFilter INT ([Nothing] | INT) The minimum number of samples an
entity must appear in, otherwise the entity is
removed from the analysis.
--entityFilterStdDev DOUBLE
([Nothing] | DOUBLE) Remove entities that have less
than this value for their standard deviation among
all samples.
--permutations INT ([1000] | INT) The number of permutations for cosine
similarity permutation test or bootstrap. Right now
just does bootstrap and only shows the first
comparison if there are multiple comparisons.